#  Strategic Information Attacks on Incentive-Compatible Navigational
Recommendations in Intelligent Transportation Systems

Ya-Ting Yang, Haozhe Lei, and Quanyan Zhu The Authors are with the Department
of Electrical and Computer Engineering, New York University, Brooklyn, NY,
11201, USA; E-mail: {yy4348, hl4155<EMAIL_ADDRESS>YY and HL have
contributed equally. Correspondence should be addressed to YY.

###### Abstract

Intelligent transportation systems (ITS) have gained significant attention
from various communities, driven by rapid advancements in informational
technology. Within the realm of ITS, navigational recommendation systems (RS)
play a pivotal role, as users often face diverse path (route) options in such
complex urban environments. However, RS is not immune to vulnerabilities,
especially when confronted with potential information-based attacks. This
study aims to explore the impacts of these cyber threats on RS, explicitly
focusing on local targeted information attacks in which the attacker favors
certain groups or businesses. We study human behaviors and propose the
coordinated incentive-compatible RS that guides users toward a mixed Nash
equilibrium, under which each user has no incentive to deviate from the
recommendation. Then, we delve into the vulnerabilities within the
recommendation process, focusing on scenarios involving misinformed demands.
In such cases, the attacker can fabricate fake users to mislead the RS’s
recommendations. Using the Stackelberg game approach, the analytical results
and the numerical case study reveal that RS is susceptible to informational
attacks. This study highlights the need to consider informational attacks for
a more resilient and effective navigational recommendation.

###### Index Terms:

Intelligent transportation systems, path recommendation, information attack,
Stackelberg game.

##  I Introduction

Harnessing the vast information available from modern wireless communication
and Internet of Things (IoT) advancements [1, 2], coupled with the progress
made in data science and artificial intelligence [3, 4], intelligent
transportation systems (ITS) have gained substantial attention for their
ability to effectively tackle traffic congestion and elevate driver
experiences. Within the realm of ITS, path recommendation systems (RS) such as
Google Maps and Apple Maps play a vital role in complex urban environments
with diverse route choices [5] for users, including drivers and pedestrians.
Based on the given information, the RS offers routes to simplify users’
decision-making processes by presenting good paths, aiming to reduce travel
duration and alleviate congestion [6]. However, unlike routing, user
compliance with recommendations is not guaranteed, emphasizing the need for
incentive-compatible recommendations [7].

In addition, the path recommendation process is prone to various
vulnerabilities [8] that attackers can leverage to promote particular groups
or businesses in a local targeted sense or potentially exacerbating congestion
levels on a broader network-wide scale. Within this context, information-based
attacks emerge as a critical concern, as they empower malicious entities to
spread misinformation and manipulate drivers to achieve their objectives [9].
For example, within the Waze platform, police officers can intentionally
provide no congestion information on certain roads to catch speeding drivers
[10], while residents may fabricate congestion reports to divert traffic away
from their residential areas [11]. Moreover, [12] illustrates how Sybil-based
attacks can effectively manipulate the crowdedness at the points of interest,
traffic congestion levels, and public transportation occupancy within Google
Maps.

In this work, we discuss the potential vulnerabilities of the coordinated
incentive-compatible navigational RS. Our initial step involves formulating a
comprehensive RS problem. Beyond the traditional RS approaches that typically
optimize individual user costs or the overall social profile [13, 14], our
approach takes into account the impact of human behaviors. Our primary goal is
to ensure that users do not have incentives to deviate from the
recommendation, while considering the non-user drivers. From the established
framework, we observe an equivalence between the incentive-compatible RS and
the Wardrop equilibrium [15], a connection that facilitates the risk analysis
and the design of computational algorithms.

To assess the risk of cyber attacks on RS, particularly in the context of
local information attacks, we employ Stackelberg game frameworks. The attacker
is the leader who conveys misinformation, while the RS, as the follower,
responds to the provided information. Although attacks may differ in their
methods, their impacts can be classified into two major categories -
misinformed demands and misinformed traffic conditions such as travel time
costs. This study focuses on the former, in which the attacker can fabricate
Sybil-based users with fake origin-destination (OD) pairs to make the RS
recommend the true users towards a specific road that favors certain groups or
businesses. That is, by carefully designing the misinformed demands, such as
how many fake users with which OD pairs, the attacker can make the RS
redistribute the true users on other alternative paths towards the target
road.

This research demonstrates that the RS considering human behaviors and
recommending users toward a mixed Nash equilibrium is susceptible to
informational attacks. Our contributions can be summarized as follows.

  * •

We formulate a game-theoretic problem aimed at designing an incentive-
compatible navigational RS that considers the behaviors of non-users within
the traffic network and guides users toward an optimal traffic equilibrium
where no users have incentives to deviate from the provided recommendations.

  * •

We identify vulnerabilities of the RS, with a focus on the misinformed demands
in the local targeted attack. To quantify the risk of these vulnerabilities,
we employ a Stackelberg game approach. Both analytical results and numerical
case studies show that users are highly vulnerable to attacks targeting roads
by fabricating fake demands with OD pairs on alternative paths.

  * •

We discover a “Resilience Paradox” where the local targeted attack by
misinformed demands or traffic conditions can benefit the overall traffic
outcome regarding total travel time costs in some situations.

##  II Related Works

Research efforts, e.g., [5, 16], on navigational RSs share the same goal of
elevating user experience and mitigating the congestion level. One aspect
typically focuses on optimizing recommendations for independent routing, which
tends to overlook other users and may lead to the flash crowd effect [17].
Alternatively, the RS may prioritize system efficiency at the expense of some
users’ utilities, or may consider a user-oriented equilibrium routing [18, 7]
that reduces system costs to a certain level compared with independent
routing. Our study aims to go one step further. We consider human factors such
as user’s compliance with recommendations and the behaviors of those non-user
drivers, then propose a more comprehensive RS that guides users towards a
mixed Nash equilibrium.

Regarding malicious entities or potential attackers [8] in the realm of ITS
[19], most studies typically have focused on attacks that aim to increase the
overall congestion level of the traffic network [20]. We aim to scrutinize the
particular vulnerabilities inherent to navigational RSs, which are susceptible
to a wide range of potential attacks, and focus on the local targeted attacks
that has few systematic studies. Specifically, this work expands the scope of
recent studies [12] and identifies a broadened class of attacks, including
attackers who may seek to mislead users onto specific roads. We delve into
Sybil-based attack methods, where misinformation regarding non-existent
demands comes from fake users.

##  III System Model and Preliminary

The feedback structure of the coordinated incentive-compatible navigational RS
is illustrated in Fig. 1. When seeking path recommendations, users begin by
providing their OD pairs to the RS. The RS collects data from various sources,
including traffic sensors, cloud databases, and user reports. Then, the RS
processes this information to generate path recommendations for the users.

###  III-A Navigational Recommendation Systems

Motivated by the congestion game [7], the ingredients for a RS consist of the
following. The RS is the app that recommends paths to its “users”
$u\in\mathcal{U}=\\{1,\cdots,m\\}$. Each user $u$ has an origin $O_{u}$ and
destination $D_{u}$ pair (OD pair), denoting the OD pair of user $u$ as
$\theta_{u}$ for later use. Each user $u$ then has a set of feasible paths
$\mathcal{S}_{u}=\\{1,\cdots,k_{u}\\}$ from origin $O_{u}$ and destination
$D_{u}$. Such a set can be identified by shortest path algorithms [21]. The
urban transportation network can be represented as a graph
$\mathcal{G}=(\mathcal{V},\mathcal{E})$, where $\mathcal{V}$ denotes the set
of intersections; $\mathcal{E}$ represents roads between intersections.
Passing through an edge $e\in\mathcal{E}$ induces a cost
$c_{e}:\mathbb{R}_{\geq 0}\mapsto\mathbb{R}_{+}$ related to the expected flow
$f_{e}\in\mathbb{R}_{\geq 0}$ on that road $e$. One possible choice of the
cost function $c_{e}(\cdot)$ can be the travel time cost
$c_{e}(f_{e})=t_{e}\left(1+\eta\left(\frac{f_{e}}{k_{e}}\right)^{\zeta}\right)$
that given by the standard Bureau of Public Roads (BPR) function, where
$t_{e}\in\mathbb{R}_{+}$ is the free-flow travel time on edge $e$,
$k_{e}\in\mathbb{R}_{+}$ is the capacity of edge $e$, and
$\eta,\zeta\in\mathbb{R}_{\geq 0}$ are some parameters.

Figure 1: The process for path recommendations. The users report their OD
pairs to the RS. The RS gathers data (from sensors, cloud database, other
users’ report, etc.) and then provide path recommendations to the users;
(1)-(5) indicate possible vulnerabilities of the RS.

In summary, the structural objects that the RS considers is given by
$\mathscr{G}=\left\langle\
\mathcal{U},(\mathcal{S}_{u})_{u\in\mathcal{U}},\mathcal{G},(c_{e}(\cdot))_{e\in\mathcal{E}}\
\right\rangle$. In this study, we use a mixed strategy for the stochastic
choice behavior of the app users. Denote user $u$’s strategy as a probability
mass function over the feasible paths set
$\textbf{P}_{u}\in\Delta(\mathcal{S}_{u})$. Here,
$\textbf{P}_{u}=\\{p_{u,i}\\}_{i=1,\cdots,k_{u}}$, and
$\mathcal{P}_{u}:=\Delta(\mathcal{S}_{u})$ with
$\mathcal{P}:=\Pi_{u\in\mathcal{U}}\mathcal{P}_{u}$. That is,
$p_{u,i}\in[0,1]$ represents the probability that user $u$ places on the path
$s_{u,i}\in\mathcal{S}_{u}$ with the constraints

$\sum_{i=1}^{k_{u}}p_{u,i}=1,\ \forall u\in\mathcal{U}.$ (1)

Note that the probability $p_{u,i}$ can also be viewed as the expected volume
generated by user $u$ on path $s_{u,i}$, which leads to the expected flow on
edge $e$ as

$f_{e}=\sum_{u\in\mathcal{U}}\sum_{i=1}^{k_{u}}p_{u,i}\mathbf{1}_{\\{e\in
s_{u,i}\\}}.$ (2)

Therefore, a generalized travel cost
$C_{u,i}:\mathcal{P}\mapsto\mathbb{R}_{+}$ for user $u$ with each path
$s_{u,i}$ can be written as the one that sums over all edge costs of the path:

$C_{u,i}(\textbf{P})=\sum_{e\in s_{u,i}}c_{e}(f_{e}),$ (3)

for all app users’ path choice probabilities $\textbf{P}\in\mathcal{P}$, with
$\textbf{P}=\\{\textbf{P}_{u}\\}_{u\in\mathcal{U}}$.

In addition to those app users $\mathcal{U}$, the traffic condition, or the
expected flow on each road, is influenced by other “drivers” in the urban
transportation network. Hence, we denote the set of other drivers who dispense
with the RS as $\bar{\mathcal{U}}$. Similarly, each driver
$\bar{u}\in\bar{\mathcal{U}}$ has an origin $O_{\bar{u}}$ and destination
$D_{\bar{u}}$ pair that determines a set of feasible paths
$S_{\bar{u}}=\\{1,\cdots,k_{\bar{u}}\\}$. Without loss of generality, we
assume that those drivers’ stochastic choice behavior can be modeled by the
multinomial logit (MNL) model [22], which has been widely used in various
fields such as economics, transportation [23], marketing, and social sciences,
to analyze discrete choice among multiple alternatives. In the context of path
choices, the model supposes that individuals make choices based on the cost
they associate with each available option. Hence, based on the current
(initial) cost (travel time) $C_{\bar{u},i}^{o}\in\mathbb{R}_{+}$ on path
$s_{\bar{u},i}$, driver $\bar{u}$’s path choice preference without
recommendations is

$p_{\bar{u},i}^{o}=\frac{e^{V_{\bar{u},i}}}{\sum_{i=1}^{k_{\bar{u}}}e^{V_{\bar{u},i}}},$
(4)

Note that the term
$V_{\bar{u},i}=-\alpha_{\bar{u}}-\beta_{\bar{u}}C_{{\bar{u}},i}^{o}$, where
$\alpha_{\bar{u}}\in\mathbb{R}$ and $\beta_{\bar{u}}\in\mathbb{R}$ indicate
driver $\bar{u}$’s valuations. Then, we denote
$\textbf{P}^{o}=\\{p_{\bar{u},i}^{o}\\}_{\bar{u}\in\bar{\mathcal{U}},i=1,\cdots,k_{\bar{u}}}$
as all drivers’ path choice preferences.

Therefore, the recommendation
$\textbf{P}^{r}=\\{p_{u,i}^{r}\\}_{u\in\mathcal{U},i=1,\cdots,k_{u}}$ to all
the app users can be a feasible one if it satisfies the following constraints:

$\displaystyle\sum^{k_{u}}_{i=1}p^{r}_{u,i}\left(C_{u,i}(\textbf{P}^{r}_{u},\textbf{P}^{r}_{-u},\textbf{P}^{o})\right)-p^{d}_{u,i}\left(C_{u,i}(\textbf{P}^{d}_{u},\textbf{P}^{r}_{-u},\textbf{P}^{o})\right)$
$\displaystyle\qquad\leq 0,\forall u\in\mathcal{U},\forall\
\textbf{P}^{d}_{u}=\\{p^{d}_{u,i}\\}_{i\in\\{1,\cdots,k_{u}\\}}\in\mathcal{P}_{u},$
(5a) $\displaystyle\sum^{k_{u}}_{i=1}p^{r}_{u,i}=1,\forall u\in\mathcal{U},$
(5b) $\displaystyle p^{r}_{u,i}\geq 0,\forall u\in\mathcal{U},\forall
s_{u,i}\in\mathcal{S}_{u},$ (5c)

with

$f^{r}_{e}=\sum_{u\in\mathcal{U}}\sum_{i=1}^{k_{u}}p^{r}_{u,i}\mathbf{1}_{\\{e\in
s_{u,i}\\}},\forall e\in\mathcal{E},$ (6)

denotes the expected flow caused by the recommendations to users on each road,
and

$f^{o}_{e}=\sum_{\bar{u}\in\bar{\mathcal{U}}}\sum_{i=1}^{k_{\bar{u}}}p^{o}_{\bar{u},i}\mathbf{1}_{\\{e\in
s_{\bar{u},i}\\}},\forall e\in\mathcal{E},$ (7)

indicates the expected flow caused by other drivers on the road. The expected
flow caused by users and drivers result in the cost of each user $u$’s choice
$s_{u,i}$:

$C_{u,i}(\textbf{P}^{r})=\sum_{e\in s_{u,i}}c_{e}(f^{r}_{e}+f^{o}_{e}),\forall
u\in\mathcal{U},\forall s_{u,i}\in\mathcal{S}_{u},$ (8)

where the recommendation to user $u$ is
$\textbf{P}^{r}_{u}\in\mathcal{P}_{u}$, and the recommendations to other users
except $u$ is
$\textbf{P}^{r}_{-u}\in\Pi_{u^{\prime}\in\mathcal{U}\setminus\\{u\\}}\mathcal{P}_{u^{\prime}}$.
It is important to note that constraint (5a) coincides with the definition of
Nash equilibrium, where each user will have no incentive to unilaterally
deviate from the recommended mixed strategy. This RS takes into account the
preferences of the users as a group and creates coordinated incentive-
compatible recommendations, which differs from the recommendation of the
shortest path to all users. The constraints (5b) and (5c) ensure that
$\\{p_{u,i}^{r}\\}_{u\in\mathcal{U},i=1,\cdots,k_{u}}$ are valid mixed
strategies.

###  III-B Feasibility Analysis

To analyze the feasibility of problem (5), we can connect it with the Wardrop
equilibrium [15].

######  Definition 1 (Wardrop equilibrium).

A Wardrop equilibrium (WE) is a feasible path flow and road load pair
$(\boldsymbol{y},\boldsymbol{f})$ with vectors
$\boldsymbol{y}\in\mathbb{R}_{\geq
0}^{\Pi_{t\in\mathcal{T}}|\mathcal{S}_{t}|}$ and
$\boldsymbol{f}\in\mathbb{R}_{\geq 0}^{|\mathcal{E}|}$, where
$\boldsymbol{y}:=\\{y_{t,i}\\}_{t\in\mathcal{T},s_{t,i}\in\mathcal{S}_{t}}$
and $\boldsymbol{f}:=\\{f_{e}\\}_{e\in\mathcal{E}}$ for which the prevailing
cost of all used strategies is minimal, or, mathematically, for demand $t$ and
corresponding strategy set $\mathcal{S}_{t}$,

$\displaystyle\forall t\in\mathcal{T},\forall
s_{t,i},s_{t,j}\in\mathcal{S}_{t},\quad y_{t,i}>0$
$\displaystyle\Longrightarrow\sum_{e\in
s_{t,i}}c_{e}\left(f_{e}\right)\leq\sum_{e\in
s_{t,j}}c_{e}\left(f_{e}\right),$

where the path flow vector $\boldsymbol{y}$ and the road load vector
$\boldsymbol{f}$ need to satisfy the following constraints:

$\displaystyle d_{t}$
$\displaystyle=\sum_{s_{t,i}\in\mathcal{S}_{t}}y_{t,i}\text{ with }y_{t,i}\geq
0,\quad\forall t\in\mathcal{T},$ (9) $\displaystyle f_{e}$
$\displaystyle=\sum_{t\in\mathcal{T}}\sum_{s_{t,i}\in\mathcal{S}_{t}}y_{t,i}\mathbf{1}_{\\{e\in
s_{t,i}\\}},\quad\forall e\in\mathcal{E}.$

######  Proposition 1.

A Wardrop equilibrium flow-load pair $(\boldsymbol{y},\boldsymbol{f})$ in
Definition 1 that corresponds to the recommendation-load $(\textbf{P}^{r}$,
$(f^{r}_{e})_{e\in\mathcal{E}})$ is a feasible solution for the incentive-
compatible navigational RS described in (5)-(8).

###### Proof.

A correspondence can be observed between Definition 1 and the problem with the
RS in (5). That is, type $t$ and $\mathcal{S}_{t}$ in the definition
correspond to user $u$ and the set of feasible paths $\mathcal{S}_{u}$, the
road load vector $f_{e}$ in the definition is the same as the expected flow
$f_{e}$ on edge $e$, and the path flow $y_{t,i}$ in the definition corresponds
to the probability $p_{u,i}$ that user $u$ places on the path $s_{u,i}$.
Hence, letting $d_{t}=1$ in the definition will lead to
$\sum^{k_{u}}_{i=1}p^{r}_{u,i}=1$ that naturally satisfies constraint (5b).

As a result, the WE flow-load pair $(\boldsymbol{y},\boldsymbol{f})$ that
corresponds to the ($\textbf{P}^{r}$, $(f^{r}_{e})_{e\in\mathcal{E}}$) pair in
the problem can be viewed as a feasible solution for the RS. More
specifically, if ($\textbf{P}^{r}$, $(f^{r}_{e})_{e\in\mathcal{E}}$) is a WE,
then for each user $u$ in constraint (5a): Since only paths with minimum cost
are used, all the paths used by any given user have the same cost. That is,
for $p_{u,i}>0$, the cost
$C_{u,i}(\textbf{P}^{r}_{u},\textbf{P}^{r}_{-u},\textbf{P}^{o})$ should be the
same for $u$. The overall expected cost is independent of the probability
$p_{u,i}$ of $C_{u,i}(\textbf{P}^{r}_{u},\textbf{P}^{r}_{-u},\textbf{P}^{o})$.
Lastly, note that if ($\textbf{P}^{r}$, $(f^{r}_{e})_{e\in\mathcal{E}}$) is an
equilibrium, there is no incentive for a user to deviate to any other
$\textbf{P}^{d}_{u}\in\mathcal{P}_{u}$. ∎

Note that the connection can also be observed through variational inequalities
[24]. To begin with, denote the expected cost
$\sum^{k_{u}}_{i=1}p^{r}_{u,i}\left(C_{u,i}(\textbf{P}^{r}_{u},\textbf{P}^{r}_{-u},\textbf{P}^{o})\right)$
in problem (5) as $H_{u}(\textbf{P}^{r}_{u},\textbf{P}^{r}_{-u})$ for each
user $u$, and an equilibrium is the recommendation
$\textbf{P}^{r}=\\{p_{u,i}^{r}\\}_{u\in\mathcal{U},i=1,\cdots,k_{u}}$ that
satisfies $H_{u}(\textbf{P}^{r}_{u},\textbf{P}^{r}_{-u})\leq
H_{u}(\textbf{P}^{d}_{u},\textbf{P}^{r}_{-u}),\forall\
\textbf{P}^{d}_{u}\in\mathcal{P}_{u},\forall u\in\mathcal{U}$. Let
$\nabla_{u}H_{u}(\textbf{P}^{r})$ represent the gradient of
$H_{u}(\textbf{P}^{r}_{u},\textbf{P}^{r}_{-u})$ with respect to each element
of $\textbf{P}^{r}_{u}$ and $\frac{\partial H_{u}(\textbf{P}^{r})}{\partial
p^{r}_{u,i}}=C_{u,i}(\textbf{P}^{r})+\sum_{j\in\mathcal{S}_{u}}p^{r}_{u,j}\frac{\partial
C_{u,j}(\textbf{P}^{r})}{\partial p^{r}_{u,i}}$. Then, a solution of (5a)
satisfies

$\sum_{u\in\mathcal{U}}\langle\nabla_{u}H_{u}(\textbf{P}^{r}),\textbf{P}^{r}_{u}-\textbf{P}^{d}_{u}\rangle\leq
0,\forall\ \textbf{P}^{d}\in\mathcal{P}.$ (10)

Similarly, from the definition of WE, let each type $t$ be associated with one
corresponding user $u$. Then, we can get

$\sum_{u\in\mathcal{U}}\langle
C_{u}(\textbf{P}^{r}),\textbf{P}^{r}_{u}-\textbf{P}^{d}_{u}\rangle\leq
0,\forall\ \textbf{P}^{d}\in\mathcal{P},$ (11)

where
$C_{u}(\textbf{P}^{r})=\\{C_{u,i}(\textbf{P}^{r})\\}_{s_{u,i}\in\mathcal{S}_{u}}$.
Then, the equilibrium for the RS’s problem and the WE can be treated as
equivalent in the following.

######  Definition 2.

For the game $\Gamma(\mathscr{G},\Phi)$ with structural objects $\mathscr{G}$
in Section III-A and evaluation functions
$\Phi=\\{\Phi_{u}\\}_{u\in\mathcal{U}}$, where
$\Phi_{u}:\mathcal{P}\mapsto\mathbb{R}_{+}^{|\mathcal{S}_{u}|},\forall
u\in\mathcal{U}$, the Nash equilibria $NE(\Phi)$ is the set of
$\textbf{P}^{r}\in\mathcal{P}$ satisfying
$\sum_{u\in\mathcal{U}}\langle\Phi_{u}(\textbf{P}^{r}),\textbf{P}^{r}_{u}-\textbf{P}^{d}_{u}\rangle\leq
0,\forall\ \textbf{P}^{d}\in\mathcal{P}$.

The evaluation functions are $\\{\nabla_{u}H_{u}\\}_{u\in\mathcal{U}}$,
$\nabla_{u}H_{u}:\mathcal{P}\mapsto\mathbb{R}_{+}^{|\mathcal{S}_{u}|}$ for the
RS and $\\{C_{u}\\}_{u\in\mathcal{U}}$,
$C_{u}:\mathcal{P}\mapsto\mathbb{R}_{+}^{|\mathcal{S}_{u}|}$ in WE.

###  III-C Solving for the Equilibrium

Knowing that the feasible recommendation for problem (5) exists, we then
proceed to find the mixed strategy $\textbf{P}^{r}_{u}$ for each user $u$.
First, for user $1$’s constraint (5a) in the RS’s problem:

$\sum^{k_{1}}_{i=1}p^{r}_{1,i}\left(C_{1,i}(\textbf{P}^{r}_{1},\textbf{P}^{r}_{-1},\textbf{P}^{o})\right)-p^{d}_{1,i}\left(C_{1,i}(\textbf{P}^{d}_{1},\textbf{P}^{r}_{-1},\textbf{P}^{o})\right)\leq
0.$

Given other users’ strategy $\textbf{P}^{r}_{-1}$, user $1$’s best response
(or the best recommendation to user $1$) is the following.

$\displaystyle\operatorname*{arg\,min}_{p^{r}_{1,1},\cdots,p^{r}_{1,k_{1}}}$
$\displaystyle\sum^{k_{1}}_{i=1}p^{r}_{1,i}C_{1,i}(\textbf{P}^{r}_{1},\textbf{P}^{r}_{-1},\textbf{P}^{o}),$
(12) s.t. $\displaystyle\sum^{k_{1}}_{i=1}p^{r}_{1,i}=1,\ \ p^{r}_{1,i}\geq
0,\forall s_{1,i}\in\mathcal{S}_{1}.$

Here, the problem in (12) computes mixed strategies
$p^{r}_{1,i},\cdots,p^{r}_{1,k_{1}}$ that minimize user $1$’s expected cost.
Then, other users’ constraints (5a) in the RS’s problem follow a similar
procedure. Note that the additional expected flow resulting from other
drivers’ path choice preferences $\textbf{P}^{o}$ does not change with
$\textbf{P}^{r}$. More specifically,
$c_{e}(f_{e})=t_{e}(1+\eta(\frac{f_{e}}{k_{e}})^{\zeta})$ can be rewritten as
$c_{e}(f^{r}_{e},f^{o}_{e})=t_{e}(1+\eta(\frac{f^{r}_{e}+f^{o}_{e}}{k_{e}})^{\zeta})$,
where $f^{r}_{e}$ comes from users’ mixed strategies $\textbf{P}^{r}$, and
$f^{o}_{e}$ remains a constant comes from other drivers’ choice preferences
$\textbf{P}^{o}$.

To this end, since (12) is a constrained optimization problem, we can use the
penalty methods (PM) or other techniques to solve the problem for each user,
and iteratively find the equilibrium solution. The evolution of the iterative
process adopts the best response dynamics
$\mathcal{D}^{\Phi}=\\{\mathcal{D}^{\Phi}_{u}\\}_{u\in\mathcal{U}}$, where at
iteration $n$ and $n+1$ for user $u$,

$\mathcal{D}^{\Phi}_{u}(\textbf{P}^{r,(n)})=BR_{u}(\textbf{P}^{r,(n)})-\textbf{P}^{r,(n)}_{u},$
(13)

with
$BR_{u}(\textbf{P}^{r,(n)})\\!=\\!\\{\textbf{P}^{r,(n+1)}_{u}\\!\in\\!\mathcal{P}_{u},\langle\textbf{P}^{r,(n+1)}_{u}-\textbf{P}^{d}_{u},\Phi(\textbf{P}^{r,(n)})\rangle$
$\leq 0,\forall\textbf{P}^{d}_{u}\in\mathcal{P}_{u}\\}$, and can be shown to
satisfy Nash stationarity.

######  Definition 3 (Nash Stationarity).

For the game $\Gamma(\mathscr{G},\Phi)$ with evaluation functions
$\Phi=\\{\Phi_{u}\\}_{u\in\mathcal{U}}$, where
$\Phi_{u}:\mathcal{P}\mapsto\mathbb{R}_{+}^{|\mathcal{S}_{u}|}$, the dynamics
$\mathcal{D}^{\Phi}=\\{\mathcal{D}^{\Phi}_{u}\\}_{u\in\mathcal{U}}$ with
$\mathcal{D}^{\Phi}_{u}:\mathcal{P}\mapsto[-1,1]^{|\mathcal{S}_{u}|}$ satisfy
Nash stationarity if $\mathcal{D}^{\Phi}(\textbf{P}^{r})=0$ if and only if
$\textbf{P}^{r}\in\mathcal{P}$ is an equilibrium $NE(\Phi)$.

######  Proposition 2.

The RS’s iterative process that adopts the best response dynamics
$\mathcal{D}^{\Phi}=\\{\mathcal{D}^{\Phi}_{u}\\}_{u\in\mathcal{U}}$ in (13)
satisfies Nash stationarity as in Definition 3.

###### Proof.

By Definitions 2 and 3, since $\mathcal{D}^{\Phi}(\textbf{P}^{r})=0$ in (13)
if and only if $\textbf{P}^{r}_{u}\in BR_{u}(\textbf{P}^{r}),\forall\
u\in\mathcal{U}$, $\textbf{P}^{r}\in NE(\Phi)$. ∎

The procedure for solving the RS’s problem is described in Algorithm 1, and
the numerical results are shown and discussed in Section VI.

Algorithm 1 Recommendation System

1:Input
$\left\langle\mathcal{U},(\mathcal{S}_{u})_{u\in\mathcal{U}},\mathcal{\bar{U}},(\mathcal{S}_{\bar{u}})_{\bar{u}\in\mathcal{\bar{U}}},\mathcal{G},(c_{e}(\cdot))_{e\in\mathcal{E}}\right\rangle$,

2:Initialize recommendation $\textbf{P}^{r}$,

3:Obtain $\textbf{P}^{o}$ for other drivers $\mathcal{\bar{U}}$,

4:while $n<\text{MaxIter}$ do

5: choose $u=n\mod|\mathcal{U}|$,

6: solve
$\operatorname*{arg\,min}_{\textbf{P}^{r}_{u}}\sum^{n_{u}}_{i=1}p^{r}_{u,i}C_{u,i}(\textbf{P}^{r}_{u},\textbf{P}^{r,(n)}_{-u},\textbf{P}^{o})$,

7: update
$\textbf{P}^{r,(n+1)}=\textbf{P}^{r,(n+1)}_{u}\cup\textbf{P}^{r,(n)}_{-u}$

8:Return optimal $\textbf{P}^{r}$

##  IV Attack Models

###  IV-A Attack Methods and Consequences

As the RS offers recommendations based on users’ OD pairs, transportation
network, and traffic conditions, it becomes susceptible to a range of
vulnerabilities, as depicted in red in Fig. 1. Those potential attacks can be
but not limited to the following: 1) Sensor Manipulation Attacks: These
involve tampering with traffic sensors to manipulate the collected data,
consequently impacting the cost used by the RS; 2) Communication Attacks
(e.g., Man-in-the-Middle): These types hijack and alter the path
recommendations for users; 3) Misinformation Attacks on Drivers: These attacks
induce changes in drivers’ choice preferences through misinformation (e.g.,
fabricated accident reports or anonymous biased reviews), leading to shifts in
expected flow and cost as evaluated by the RS; 4) Misinformation Attacks on
Demand (e.g., Sybil-based attack): Attackers can fabricate non-existent users
with fictitious demands; 5) Feedback Availability: Attackers could exploit
delays in information structure or initiate denial-of-service attacks on the
sensor data.

Although these attacks may differ in their methods, their consequences can be
classified into two major categories: misinformed costs and misinformed
demands. In this study, we focus on the misinformed demands. The attacker is
capable of creating Sybil users with certain origins and destinations that
change the demand of the corresponding OD pairs, which will influence the
resulting recommendation, that is, the feasible ($\textbf{P}^{r}$,
$(f^{r}_{e})_{e\in\mathcal{E}}$) pair in the RS’s problem will be different.

###  IV-B Attacker’s Objectives

Imagine an attacker driven by self-interest, in conflict with the overall
social welfare goal of reducing congestion. This scenario can be studied at
both local targeted and network-wide levels: the former pertains to specific
groups or locations, while the latter considers the system-wide impact.

Targeted Attacks: The attacker seeks to bias the system by suggesting paths
that favor particular groups (e.g., higher-paying users) or businesses (e.g.,
those paying the attacker to ensure users see particular ads or pass by their
shops). This manipulation promotes or harms specific interests.

Network-Wide Attacks: The attacker aims to disrupt the system by increasing
delays or congestion indices across the network, consequently raising the
overall traffic time cost. These actions could harm the system’s reputation,
leading to user dissatisfaction or a loss of trust in the RS.

In our ensuing discussion, we will delve into the local perspective, as
existing literature such as [20] has already focused on the network-wide
attack.

###  IV-C Local Attack through Misinformed Demands

For the sake of simplicity, we suppose that the RS selects the WE flow-load
pair as a practical recommendation, with the effects introduced by other non-
user drivers already encompassed within the cost function for the ensuing
discussion. Then, the RS’s problem can be reformulated as the following using
the notations from Definition 1.

####  IV-C1 Attacking the WE-based RS

Let a set $\mathcal{T}\subset\mathcal{V}\times\mathcal{V}$ represents the
origin-destination (OD) pairs. For each OD pair $t\in\mathcal{T}$, a flow of
demand from user $d_{t}=\sum_{u\in\mathcal{U}}\mathbf{1}_{\\{\theta_{u}=t\\}}$
must be routed from the corresponding origin to its destination. Then, the
feasible path set for each $t$ is $\mathcal{S}_{t}=\\{1,\cdots,k_{t}\\}$. We
denote $F(\boldsymbol{d})$ as the set of flow-load pair
$(\boldsymbol{y},\boldsymbol{f})$ that satisfies both constraints in (9) with
the demand vector $\boldsymbol{d}:=\left(d_{t}\right)_{t\in\mathcal{T}}$,
where the path flow vector
$\boldsymbol{y}:=\left(y_{t,s}\right)_{t\in\mathcal{T},s\in\mathcal{S}_{t}}$
and the edge load vector
$\boldsymbol{f}:=\left(f_{e}\right)_{e\in\mathcal{E}}$. Then, according to
Beckmann [25], Wardrop equilibrium can be computed as the solution to the
following optimization problem,

$\displaystyle\min_{\boldsymbol{y},\boldsymbol{f}}\ $
$\displaystyle\sum_{e\in\mathcal{E}}\int_{0}^{f_{e}}c_{e}(z)dz$ (14a) s.t.
$\displaystyle(\boldsymbol{y},\boldsymbol{f})\in F(\boldsymbol{d}).$ (14b)

We represent the optimization problem as $W(\boldsymbol{d})$, and the
corresponding WE solution pair as
$(\widehat{\boldsymbol{y}},\widehat{\boldsymbol{f}})$.

######  Remark 1.

When delivering recommendations to user $u$ with the OD pair
$\theta_{u}=t\in\mathcal{T}$, the RS suggests a mixed strategy $p_{u,i}$ over
the feasible $s_{u,i}\in\mathcal{S}_{u}=\mathcal{S}_{t}$, each
$p_{u,i}=y_{t,s_{u,i}}/d_{t}$.

Subsequently, consider the situation where a Sybil-based attacker generates
non-existent demands $\boldsymbol{d}^{a}\in\mathbb{Z}_{\geq
0}^{|\mathcal{T}|}$ using Sybil (fake) users. Then, the RS will need to
consider an aggregated demand of
$\boldsymbol{d}^{\prime}=\boldsymbol{d}+\boldsymbol{d}^{a}$. Note that for
each OD pair $t$, the demand $d_{t}^{\prime}$ under attack consists of
$d_{t}+d_{t}^{a}$. Without loss of generality, we can assume that a proportion
of $\frac{d_{t}}{d_{t}+d_{t}^{a}}$ of the WE expected path flow
$\widehat{y}_{t,s}^{\prime}$ with respect to $W(\boldsymbol{d}^{\prime})$ is
caused by true users. Hence, denote
$\widehat{y}^{u}_{t,s}=\frac{d_{t}}{d_{t}+d_{t}^{a}}\widehat{y}_{t,s}^{\prime}$.
In a local targeted attack, the attacker aims to generate a certain level of
expected flow caused by true app users on the target edge $e^{\prime}$. That
is, the attacker aims to make
$\sum_{t\in\mathscr{T}}\sum_{s\in\mathscr{S}_{t}}\widehat{y}^{u}_{t,s}\mathbf{1}_{\\{e^{\prime}\in
s\\}}$ achieve a desired level $\gamma\in\mathbb{R}_{\geq 0}$.

$\displaystyle\min_{\boldsymbol{d}^{a}}\ $
$\displaystyle\sum_{t\in\mathcal{T}}d_{t}^{a}$ (15a) s.t.
$\displaystyle\sum_{t\in\mathcal{T}}\sum_{s\in\mathcal{S}_{t}}\widehat{y}^{u}_{t,s}\mathbf{1}_{\\{e^{\prime}\in
s\\}}\geq\gamma,$ (15b)
$\displaystyle(\widehat{\boldsymbol{y}},\widehat{\boldsymbol{f}})\in\operatorname*{arg\,min}W(\boldsymbol{d}+\boldsymbol{d}^{a}),$
(15c) $\displaystyle d_{t}^{a}\geq 0,\forall t\in\mathcal{T}.$ (15d)

The attacker’s problem can then be computed by Algorithm 2.

Algorithm 2 Misinformed Demands Attack

1:Input $\left\langle\
\mathcal{U},(\mathcal{S}_{u})_{u\in\mathcal{U}},\mathcal{G},(c_{e}(\cdot))_{e\in\mathcal{E}}\
\right\rangle$,

2:Initialize fabricated demand $\boldsymbol{d^{a}}$,

3:Obtain true demand $\boldsymbol{d}$ from
$(\mathcal{S}_{u})_{u\in\mathcal{U}}\mbox{ and }\mathcal{G}$,

4:while desired result from $\boldsymbol{d^{a}}$ is not met do

5: while $(\boldsymbol{y},\boldsymbol{f})$ does not reach WE do

6: gradient descent on (14) using PM,

7: Obtain WE $(\widehat{\boldsymbol{y}},\widehat{\boldsymbol{f}})$,

8: gradient descent on (15) using PM,

9:Return optimal $\boldsymbol{d^{a}}^{*}$

Lastly, we need to notice that the level $\gamma$ in problem (15) can not be
arbitrarily large, which leads us to the following.

######  Remark 2.

The edge load $f_{e}$ is bounded by the total demand of the true user
$d_{T}=\sum_{t\in\mathcal{T}}d_{t}$. Hence, the attacker’s desired level
$\gamma$ is also upper-bounded by $d_{T}=\sum_{t\in\mathcal{T}}d_{t}$.

(a) Recommendations without attack.

(b) Recommendations under uniform attack.

(c) Recommendations under optimal attack.

Figure 2: The experiment results. Without attack: OD pairs 1-5 and 3-5 are
recommended path 3 and path 1 with a higher probability, respectively. Under
uniform attack: OD pairs 1-5 is still recommended path 3 with a higher
probability, while both choices for OD pair 3-5 are recommended with a
probability close to 0.5. Under optimal attack: OD pairs 1-5 and 3-5 are
recommended path 2, containing the targeted road with a higher probability.

##  V Characterization for the Misinformed
Demands Attack

###  V-A Optimality Conditions for WE-based Recommendation

Under the assumption that the cost functions $c_{e}(f_{e})$ are continuous and
non-decreasing in $f_{e}$, a pair $(\boldsymbol{y},\boldsymbol{f})\in
F(\boldsymbol{d})$ is a minimizer of $W(\boldsymbol{d})$ if and only if it
satisfies the following KKT conditions.

$\displaystyle c_{e}(f_{e})-\lambda_{e}=0,\ $ $\displaystyle\forall
e\in\mathcal{E},$ (16a)
$\displaystyle-\nu_{t}+\sum_{e\in\mathcal{E}}\lambda_{e}\mathbf{1}_{\\{e\in
s\\}}-\mu_{t,s}=0,\ $ $\displaystyle\forall s\in\mathcal{S}_{t},\forall
t\in\mathcal{T},$ (16b) $\displaystyle\mu_{t,s}y_{t,s}=0,\ $
$\displaystyle\forall s\in\mathcal{S}_{t},\forall t\in\mathcal{T},$ (16c)

with Lagrangian multipliers $\nu_{t}\in\mathbb{R}_{+},\forall
t\in\mathcal{T}$, $\lambda_{e}\in\mathbb{R},\forall e\in\mathcal{E}$, and
$\mu_{t,s}\geq 0,\forall s\in\mathcal{S}_{t},\forall t\in\mathcal{T}$. Then, a
pair $(\boldsymbol{y},\boldsymbol{f})$ satisfying the constraints with
multipliers
$-\boldsymbol{\nu}=-(\nu_{t})_{t\in\mathcal{T}},\boldsymbol{\lambda}=(\lambda_{e})_{e\in\mathcal{E}},\boldsymbol{\mu}=(\mu_{t,s})_{s\in\mathcal{S}_{t},t\in\mathcal{T}}$
also satisfies

$\nu_{t}=\sum_{e\in s}c_{e}(f_{e})-\mu_{t,s}\begin{cases}=\sum_{e\in
s}c_{e}(f_{e}),\ y_{t,s}>0,\\\ \leq\sum_{e\in s}c_{e}(f_{e}),\
y_{t,s}=0,\end{cases}$

which coincides with the definition of WE.

###  V-B Impact of Demand Changes on WE

Then, we aim to examine how the WE pair
$(\widehat{\boldsymbol{y}},\widehat{\boldsymbol{f}})$ can be influenced by
changes in the demand $\boldsymbol{d}$ according to [26].

######  Proposition 3.

Let the pair $(\boldsymbol{y},\boldsymbol{f})$ with the corresponding
multipliers $\boldsymbol{\nu}$ and $\boldsymbol{\mu}$ described in (16) be a
WE for demand $\boldsymbol{d}$ and
$(\boldsymbol{y}^{\prime},\boldsymbol{f}^{\prime})$ with corresponding
multipliers $\boldsymbol{\nu}^{\prime}$ and $\boldsymbol{\mu}^{\prime}$ be a
WE for demand $\boldsymbol{d}^{\prime}$. Then,
$(\boldsymbol{\nu}^{\prime}-\boldsymbol{\nu})^{T}(\boldsymbol{d}^{\prime}-\boldsymbol{d})\geq\boldsymbol{\mu}^{\prime
T}\boldsymbol{y}+\boldsymbol{\mu}^{T}\boldsymbol{y}^{\prime}\geq 0$.

The result of the above proposition can also be written as

$\left[\sum_{e\in
s}c_{e}(f_{e}^{\prime})-c_{e}(f_{e})\right](d_{t}^{\prime}-d_{t})\geq 0,$

for $s\in\mathcal{S}_{t}$ with $y_{t,s}^{\prime},y_{t,s}>0$. The Proposition 3
states that if one demand $d_{t}$ is increased by Sybil users, with other
demands remaining the same, then the equilibrium cost $\nu_{t}$ calculated by
the RS for the user $u$ with OD pair $\theta_{u}=t$ is also increased.

######  Proposition 4.

For $W(\boldsymbol{d})$ with demand $\boldsymbol{d}$, let $\boldsymbol{f}$ be
a WE corresponds to cost $c_{e}(f_{e})$ and $\boldsymbol{f}^{\prime}$ be a WE
corresponds to cost $c_{e}^{\prime}(f_{e})$, then
$\left[c_{e}^{\prime}(f_{e})-c_{e}(f_{e})\right](f_{e}^{\prime}-f_{e})\leq 0$
and
$\left[c_{e}^{\prime}(f_{e}^{\prime})-c_{e}(f_{e}^{\prime})\right](f_{e}^{\prime}-f_{e})\leq
0$.

Then, we show that an increasing cost on an edge $e\in\mathcal{E}$ will cause
the equilibrium load on that edge $f_{e}$ to decrease. The reduced load can be
interpreted as a redistribution to alternative feasible paths. Hence, the
attacker can fulfill the goal if the load is redistributed to the desired road
$e^{\prime}$.

##  VI Numerical Experiments

We use the traffic network described in Fig. 3 as a case study of the RS and
the misinformed demand attack. Both RS and the attacker’s problem can be
addressed using gradient descent with PM. In this case, we simply consider
users with two OD pairs: one (blue) aims to go from node $1$ to $5$, and the
other (green) wants to go from node $3$ to $5$. The set of feasible paths for
OD 1-5 is $\mathcal{S}_{1}=\\{1,2,3\\}$ with choice $1$ being path 1-3-4-5,
choice $2$ being path 1-3-5, and choice $3$ being path 1-2-5. The set of
feasible paths for OD 3-5 is $\mathcal{S}_{1}=\\{1,2\\}$ with choice $1$ being
path 3-4-5, and choice $2$ being path 3-5. The number displayed on each edge
represents the free-flow time cost $t_{e}$.

###  VI-A Recommendation System

For simplicity, the cost function $c_{e}(\cdot)$ is selected as the travel
time cost $c_{e}(f_{e})=t_{e}(1+0.4\left(\frac{f_{e}}{k_{e}}\right)^{2})$,
where $t_{e}$ is the free-flow travel time on edge $e$ indicated red in Fig.
3, and the capacity $k_{e},\forall e\in\mathcal{E}$ is chosen as $10$. In the
case of independent routing, users with OD pair 1-5 will either choose choice
1 or 3, and users with OD pair 3-5 will choose choice 1. In this case, the
worst case (all users decide on choice 1) total travel time cost will be $236$
seconds ($10.4$ sec for path 3-4-5 and $13.2$ sec for path 1-3-4-5). However,
if users follow the recommendation in Fig. 2(a), the total travel time cost
will be $126$ seconds with both choices for OD pair 3-5 being $5.3$ sec and
both choice 1 and 3 for OD pair 1-5 being $7.3$ sec. As a result, the proposed
RS can help achieve a better outcome (lower total cost) while not sacrificing
users’ performance (in terms of expected travel time cost).

Figure 3: The setting where two OD pairs (blue and green) are presented with
multiple viable paths. The attacker’s goal is to manipulate the
recommendations to the users, coaxing them to go through paths containing the
orange edge. That is, the attacker desires choice 2 of both OD pairs. The
free-flow road costs are indicated in red on the edges.

###  VI-B Under Attacks

We consider the case where the attacker can fabricate user demands for a set
$\mathcal{K}\subset\mathcal{V}\times\mathcal{V}$ of distinct OD pairs to
mislead the RS who selects WE as a feasible recommendation. We have $10$
authentic users aiming to go from node $1$ to $5$ (10 demands for OD pair
1-5), and $10$ users want to go from node $3$ to $5$ (10 demands for OD pair
3-5), the attacker can identify the desired fake demand levels by solving the
problem in section IV-C. As shown in Fig. 2(c), we can observe that both OD
pairs 1-5 and 3-5 exhibit a higher probability of passing the target edge
$(3,5)$. The expected flow caused by authentic users illustrated in Fig. 4
also shows that $f^{r}_{(3,5)}$ meets the desired level $\gamma\geq 10$ by
generating a total of $30$ non-existent demands on the traffic network.

For benchmarking purposes, we select two relatively straightforward attacker
profiles: uniform and random. These attackers are restricted to allocating the
same amount of demands as the optimal attacker, totaling $30$ demands. The
uniform attacker evenly distributes the total demand across all OD pairs,
while the random attacker distributes the demand randomly among the OD pairs.
It should be noted that, in the case of the random attacker, we are interested
in presenting its average performance of $200$ experiments.

Figure 4: The expected edge flow caused by authentic users with and without
attack. In this case, the targeted edge is $(3,5)$ and the desired flow level
is $\gamma=10$.

As shown in Fig. 4, we compare the expected flow load on each edge generated
by authentic users. In this comparison, all attacker profiles contribute to an
increase in the expected flow on our targeted edge $(3,5)$. However, it is
essential to note that the optimal attacker yields the most significant
increase in flow on this edge and is the only one that reaches the desired
level $\gamma=10$.

###  VI-C From Braess’ Paradox to Resilience Paradox

To this end, a natural question is: Can the local targeted attack lead to a
better overall outcome (total costs for drivers and users) in some situations?
We can start with a carefully crafted example leveraging the classical Braess’
network [27].

####  VI-C1 Misinformed Demands

Within the transportation network shown in Fig. 5, there are $30$ users aiming
to go from node A to node B, and the $\epsilon$ is small enough so that the
cost on C-D is close to $0$ even though all $30$ users are passing through.
Before the attack (illustrated in Fig. 5(a)), the RS will recommend a mixed
strategy $(1/3,1/3,1/3)$ on path A-C-B, A-C-D-B, and A-D-B, respectively. The
overall costs on these three paths are all $4$, which leads to a total travel
time cost of $120$ for users. Suppose the attacker wants more “users” to pass
D-B by fabricating a large demand on C-D to make C-D seem congested to the RS,
as in Fig. 5(b). The RS will recommend a strategy $(0.5,0,0.5)$ on paths
A-C-B, A-C-D-B, and A-D-B, respectively. The overall costs on A-C-B and A-D-B
are both $3.5$, which leads to the total travel time cost for users becoming
$105$. The cost under attack is better than the performance without attack.

(a) Without attack

(b) Under attack

Figure 5: A carefully crafted example for the discussion on the “Resilience
Paradox”.

####  VI-C2 Misinformed Costs

Consider an alternative attack scenario where the attacker can introduce
misinformed traffic conditions by manipulating the cost functions associated
with edges, rather than fabricating non-existent demands. For illustration
purposes, using the same example as depicted in Fig. 5, where there are $30$
users aiming to travel from A to B, the recommendation without attack is
$(1/3,1/3,1/3)$, resulting in a total cost of $120$. In the case of a
misinformed cost attack, the attacker can mislead the RS into a similar
recommendation, specifically $(0.5,0,0.5)$, along with a reduced total cost of
$105$, as discussed in Section VI-C1, by misinforming the coefficient
$\epsilon$ associated with the cost of C-D to a sufficiently high value.

####  VI-C3 Misinformation in Changing Other Drivers’ Behavior

Suppose that there are $20$ drivers and $10$ users aim to go from node A to
node B, and the transportation network is shown in Fig. 5. Before the attack,
the path A-C-D-B seems better for other less strategic drivers. Considering
other drivers’ behavior, the RS will recommend a mixed strategy $(0.5,0,0.5)$
on paths A-C-B, A-C-D-B, and A-D-B, respectively. The overall costs on these
three paths are $4.5,5$, and $4.5$, which leads to the total travel time cost
for drivers and users becoming $145$. If the attacker’s objective is to divert
more “users” to route D-B by simulating a car accident on road C-D to misguide
drivers, it results in drivers’ behavioral response being $(0.5,0,0.5)$. In
this case, the RS will recommend a mixed strategy $(0,1,0)$ on paths A-C-B,
A-C-D-B, and A-D-B, respectively. The overall costs on these three paths are
all $4$, resulting in an overall travel time cost of 120 for both drivers and
users. This outcome represents an improvement over the performance in the
absence of the attack. We call this phenomenon “Resilience Paradox”, where
attacks help improve the performance of the recommendations, and strengthen
the resilience of the network.

We summarize the scenarios in the carefully constructed example involving a
total demand of $30$ drivers and users discussed above as Table I. The
relation of total travel time costs between these cases not only illustrates
the “Resilience Paradox” but also emphasizes the significance of the RS. The
latter is evident that a lower total cost is attained when the demand
composition shifts from $20$ drivers and $10$ users to $30$ users in scenarios
without attacks.

Drivers | Users | Attack | Travel Time Costs
---|---|---|---
20 | 10 | No | 145
20 | 10 | Yes | 120
0 | 30 | No | 120
0 | 30 | Yes | 105
TABLE I: Travel Time Costs in Different Scenarios

##  VII Conclusions

This paper aims to explore potential informational attacks on navigational
RSs. We first propose an RS that considers human factors such as users’
compliance and other non-user drivers’ behaviors when providing
recommendations. Then, we identify various avenues attackers can exploit to
benefit certain groups or elevate traffic congestion levels and focus on the
misinformed demand attack in a local targeted sense. The attacker’s problem is
analyzed based on the Stackelberg game framework rooted in Wardrop equilibrium
with additional non-existent demands.

Our study shows that users are vulnerable to attacks targeting specific roads
by creating non-existent demands with OD pairs associated with alternative
path options. Through the exploration, we highlight the need to consider
informational attacks for a more resilient and effective navigational
recommendation in the future. In addition, one of the other possible future
directions can be investigating different scenarios, such as the impact of
misinformed traffic conditions (costs) attacks on the RS. In contrast to the
network-wide attacks that worsen traffic congestion, we also illustrate the
“Resilience Paradox”, in which the locally targeted attack by misinformed
demands or misinformed traffic conditions can benefit the overall traffic
outcome in terms of total travel time costs. This also points out that the
locally targeted attack is a potential aspect worth further investigation.

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# Open Terminology Management and Sharing Toolkit for Federation of
Terminology Databases

###### Abstract

Consolidated access to current and reliable terms from different subject
fields and languages is necessary for content creators and translators.
Terminology is also needed in AI applications such as machine translation,
speech recognition, information extraction, and other natural language
processing tools. In this work, we facilitate standards-based sharing and
management of terminology resources by providing an open terminology
management solution – the EuroTermBank Toolkit. It allows organisations to
manage and search their terms, create term collections, and share them within
and outside the organisation by participating in the network of federated
databases. The data curated in the federated databases are automatically
shared with EuroTermBank, the largest multilingual terminology resource in
Europe, allowing translators and language service providers as well as
researchers and students to access terminology resources in their most current
version.

Keywords: terminology, terminology management, termbank, terminology sharing,
terminology database

languageresourceLanguage Resources

Open Terminology Management and Sharing Toolkit for Federation of Terminology
Databases

Andis Lagzdiņš†, Uldis Siliņš†, Mārcis Pinnis†‡, Toms Bergmanis†‡,
---
Artūrs Vasiļevskis† and Andrejs Vasiļjevs†‡
†Tilde / Vienības gatve 75A, Riga, Latvia
‡Faculty of Computing, University of Latvia / Raiņa bulv. 19, Riga, Latvia
<EMAIL_ADDRESS>

Abstract content

## 1\. Introduction

Language evolves: new words are coined, existing words change their meaning,
and some even become unused. New concepts and terms that denote them are
created every day, but many older concepts and their denotations rapidly
become obsolete. Consequently, terminological data become obsolete over time
if not regularly updated. Individual term collections are usually maintained
by the respective institution, such as an industrial company, an academic
centre, or a public administration. Still, many institutions lack a proper
terminology management system and struggle to maintain their terms current.
This has practical and financial consequences as consolidated access to
current and reliable terms from different sources is necessary not only for
content creators and translators but also for artificial intelligence (AI)
applications.

Terminology management is even more challenging for termbanks that provide
access to term collections aggregated from different institutions. Although
terminology work benefits from a rigorous standardisation process and
essential standards developed by ISO TC37 [Vasiljevs and Borzovs, 2006],
insufficient supporting tools and infrastructure as well as different
terminology management practices (including what data in what format is being
stored) that are in place across Europe are factors that hinder terminology
data sharing in a timely fashion [Gornostay, 2010].

Figure 1: Federated nodes linked to the EuroTermBank Federated Network. Figure
2: A conceptual depiction of EuroTermBank Federated Network.

In this paper, we describe a solution to these challenges that was created for
the largest aggregation of European terminology resources, namely
EuroTermBank111https://www.eurotermbank.com/ [Vasiljevs et al., 2008] and
institutions participating in the EuroTermBank Federated Network. We present
the EuroTermBank Toolkit (ETBT), an open terminology management toolkit for
the EuroTermBank Federated Network that allows organisations to manage their
term collections and share them within and outside the organisation.
The motivation of this work was to support other initiatives of natural
language processing (NLP) like automated text and speech translation with
reliable terminology. In the following subsections, we briefly describe the
application of terminology in these fields, then provide a short overview of
EuroTermBank and the federated approach to terminology consolidation. Then, we
continue with a description of the EuroTermBank Toolkit, its functionality and
architecture, and the current state of the EuroTermBank Federated Network by
providing statistics of terminology resources available within the network and
institutions hosting federated nodes.

### 1.1. Applications of Terminology in Natural Language Processing

While terminology in NLP is sometimes considered in a monolingual setting,
most of its applications are related to multilingualism and translation.
Terminology data has been proven to boost the quality of machine translation
in the past [Pinnis, 2015] and has been helpful in the work of professional
translators via computer-aided translation software [Arcan et al., 2014, Arcan
et al., 2017, Verplaetse and Lambrechts, 2019]. As of relatively recently,
there has been a plethora of research on terminology integration in modern
machine translation systems based on artificial neural networks [de Gspert et
al., 2018, Dinu et al., 2019, Jon et al., 2021, Bergmanis and Pinnis, 2021b,
Exel et al., 2020, Exel et al., 2020, Wang et al., 2022]. The sheer volume of
research on terminology integration in modern machine translation systems
indicates a great interest from the industry of language service providers.
Similar trends can be observed with the development of machine translation of
speech [Cross Vila et al., 2018, Di Gangi et al., 2019, Vydana et al., 2021]
and video subtitles [Matusov et al., 2019, Siekmeier et al., 2021, Schioppa et
al., 2021], for which there is also a growing need for correct translation of
terminology [Gaido et al., 2021].
Currently, however, the use of terminology in machine translation is not
hindered by the lack of technology but rather by the lack of high-quality
terminology data. Unlike statistical machine translation systems, which were
robust to noise that is present in training data, the modern generation
machine translation systems are susceptible to poor quality training data
[Belinkov and Bisk, 2018, Khayrallah and Koehn, 2018]. The same also applies
to the quality of terminology data, which is often created by humans for
humans only. Data created for human consumption is often unsuitable for
machines as it contains irregularities that render it machine-unreadable
[Bergmanis and Pinnis, 2021a]—findings which yet again emphasise the
importance of standardised practices for the curation of machine-readable
terminology data.

### 1.2. Overview of EuroTermBank

The objective of the work on EuroTermBank is to contribute to the advancement
of the terminology infrastructure in all member countries of the European
Union (EU) [Henriksen et al., 2006]. The difference between EuroTermBank and
other European terminology databases, such as the Interactive Terminology for
Europe222https://iate.europa.eu/333IATE was originally named as the Inter-
Agency Terminology Exchange. (IATE) [Johnson and Macphail, 2000], is in their
primary objectives. Although widely used by translators across Europe, the
primary goal of the IATE database, for example, is to serve agencies and
institutions of the EU by creating a centralised terminology platform for
their translation needs. Thus, while IATE consolidates term collections of EU
institutions, EuroTermBank is a collection of term collections of EU and many
national and other institutions. As a result, the terminological data
assembled in EuroTermBank is not created and managed by a single community but
rather in a distributed fashion, often even by geographically focused working
groups. The main stakeholders in maintaining the content of EuroTermBank are
public institutions dealing with national or international terminology work.
Examples are the State Language Centre of Latvia and the Institute of the
Estonian Language, which coordinate terminology work in Latvia and Estonia.
Other examples include the Institute of the Lithuanian Language, the
University of Copenhagen, the Culture Information Systems Centre of Latvia,
the Árni Magnússon Institute for Icelandic Studies, the Jožef Stefan
Institute, the International Network for Terminology – TermNet, the Swedish
Institute of Standards and the Institute for Language and Folklore.

These institutions continuously maintain their terminology resources, meaning
that the terminology may be added, altered, and even discarded from the local
term collections at any moment, and thus the terminology may change
constantly. This poses a challenge for EuroTermBank that aggregates the
terminology resources of these institutions. If terminology keeps changing,
there needs to be an automated process that ensures the currentness of
terminological data in the global terminological databases.

Figure 3: Term search view of ETBT.

### 1.3. Federated Approach in Terminology Consolidation

The necessity to move away from a single, isolated data bank towards a multi-
bank environment was suggested by ?), who proposed simultaneously accessing
several data banks that are all integrated into an overall working structure
that includes not only the databases but also other computerised tools and
resources. The notion of the collection of cooperating database systems that
are autonomous and possibly heterogeneous has been proposed before [Sheth and
Larson, 1990]. However, it is ?) who foresees the federation of term banks as
a new concept in linking portals and data repositories that will go far beyond
the establishment of pointers or links towards the level of exchangeability
and semantic interoperability of data and data structures.

Figure 4: Term discussion view of ETBT.

A federated approach to consolidate distributed terminology resources was
foreseen from the very beginning of the development of EuroTermBank [Vasiljevs
and Rirdance, 2007]. The first implementation used distributed search queries
over interlinked external termbases and aggregated returned results in a
consolidated search results view. This implementation was eventually phased
out due to serious practical drawbacks. External bases provided their results
in proprietary formats that tended to change over time. Consolidation of
different results into a unified structure for representation was complicated
because of data format incompatibilities. There were significant delays in
providing consolidated output to users due to frequent performance issues in
some of the interlinked termbases.

For this reason, the federated approach presented in this paper consists of a
homogeneous network of participating institutions that use unified data
exchange mechanisms based on the latest versions of the TBX standard.
Networked institutions either adapt the API of their existing databases to
comply with the requirements of EuroTermBank Federated Network or migrate to
the open EuroTermBank toolkit. Shareable data is dynamically synchronised with
the central EuroTermBank database, where it is consolidated with resources
coming from all participating institutions.

### 1.4. Aims of EuroTermBank Toolkit

The ETBT aims to guarantee the currentness of the terminological data
available at EuroTermBank by synchronising it with EuroTermBank federated
nodes of organisations and institutions throughout Europe. The ETBT also aims
to facilitate the streamlining and standardisation of the terminology curation
and sharing practices throughout Europe, thus lowering the cost and effort
required to share terminological data for both the data owners and data users.
Last but not least, the open nature of the terminology management toolkit
intends to eliminate the need for non-standard processes in terminological
data sharing.

Figure 5: Term entry edit view of the ETBT. The example shows the English
language side of a multilingual term.

## 2\. Concept of EuroTermBank Toolkit

The EuroTermBank Federated Network consists of independent Federated Nodes of
national, regional, or even organisational scope. These nodes are comprised of
institutions that independently identify and coin terminology and administer
it to share the resulting data with the pan-European terminology
repository—EuroTermBank. EuroTermBank aggregates and publishes the terminology
data to make it accessible for stakeholders in Europe and beyond. Figure 2
gives a conceptual view of the EuroTermBank Federated Network.

The ETBT plays a vital role in terminology data sharing both locally and
globally because most terminology work is carried out predominantly in a local
setting. The ETBT facilitates standardisation and streamlining of terminology
curation by offering readily available tools and infrastructure. For example,
the ETBT is based on common standards in terminology management and sharing,
such as ISO 12620 on data categories [ISO, 2019a], ISO 26162 on terminology
databases [ISO, 2019b], and the TermBase eXchange (TBX) 2 standard [ISO,
2008]. The application of standards-based tools reduces the cost and effort of
terminology curation and guarantees that the resulting terminology collections
are mutually compatible, thus ensuring ease of sharing. Compatibility with the
same shared standards as assured by the ETBT also enables conformity with a
machine-readable data structure—an often overlooked quality for terminology,
which nevertheless is paramount for terminology integration in machine
translation [Bergmanis and Pinnis, 2021a].

Likewise, the EuroTermBank Toolkit ensures the currentness of the
terminological data available at EuroTermBank by synchronising it with
EuroTermBank federated nodes of organisations and institutions throughout
Europe.

Figure 6: The architecture of the ETBT within the framework of the
EuroTermBank Federated Network.

## 3\. Functionality

#### Search

Figure 3 demonstrates the term search view of the ETBT. Terms can be searched
for in the entire local database or a specific term collection or set of
collections. Likewise, collections and search results can be filtered by
domain and language.

#### Terminology management

Terminology data can be added in two principal ways: 1) by creating a new term
entry (as well as editing an existing one) and 2) by importing an existing
collection. Terminological information for new term entries is added by
following the TBX 2 format. Besides basic data categories, such as subject
field, term equivalents in different languages, definitions, and examples of
how the term is used in context, information about the term’s morphological
properties (e.g., grammatical part of speech, number, and gender) (Figure 5
b), various administrative information and usage metadata (e.g., register,
type, currentness) (Figure 5 c), media – images and videos (Figure 5 d) – and
other categories can be added to provide extensive information about the term.
Likewise, the same information can be added for the corresponding terms in
other languages, thus making the terminology collection multilingual.

Unless approved, the term is saved as a draft (Figure 5 a), in which case it
is visible only to the members of the current group and is not published. The
import functionality supports CSV, TBX, and Excel file formats allowing to
reuse already pre-existing terminology data.

#### Terminology sharing

Term collections can be shared within a user group by adding new
collaborators, or they can be exported to CSV, TBX and Excel file formats. If
a term collection is made public, it is made accessible to the members of the
general public through EuroTermBank.

#### Collaboration

Users can share the term candidates with collaborators, participate in
discussions about the concepts and term candidates (see Figure 4), and approve
term candidates and new entries.

## 4\. Architecture

The ETBT is designed using the microservices architecture where each service
can be deployed as a container using, e.g., the
Kubernetes444https://kubernetes.io container orchestration system. The
architecture of the ETBT within the framework of the EuroTermBank Federated
Network is depicted in Figure 6. The ETBT consists of six components:

  * •

A frontend application, which provides a graphical user interface for end-
users and is developed as a single page application using
Angular555https://angular.io.

  * •

A headless (i.e., without a graphical user interface, but with an application
programming interface (API)) content management system (CMS) that stores
static content for the frontend application. For the CMS, we use the
Strapi666https://strapi.io headless CMS.

  * •

A user service, which handles user management, authentication and
authorisation. For the user service, we use the
Keycloak777https://www.keycloak.org identity and access management solution.

  * •

A discussion service, which provides functionality for terminologists to
discuss individual term entries and to enable involvement in terminology work.
The discussion service is built as an ASP.NET
Core888https://docs.microsoft.com/en-us/aspnet/core/?view=aspnetcore-6.0 web
service with an underlying MySQL999https://www.mysql.com database.

  * •

A log service that allows to store and visualise log data. The log service
utilises the Elastic Search101010https://www.elastic.co engine for data
storage and retrieval and Kibana111111https://www.elastic.co/kibana for
visualisation of data that is stored by Elastic Search.

  * •

A term service that provides all functionality necessary for terminology
management (i.e., creation, editing, import, export, etc.), retrieval, and
sharing. The term service is built as an ASP.NET Core web application with
Elastic Search and an underlying MySQL database.

All terminology specified as public and thus sharable is automatically
synchronised with EuroTermBank. The synchronisation is performed by each
federated node individually. Federated nodes push changes in public term
collections to EuroTermBank’s Central synchronisation API. All terminological
data exchange is performed using the TBX 2 data format.

## 5\. Current State of the EuroTermBank Federated Network

EuroTermBank is currently the largest centralised online terminology bank in
Europe, providing access to more than 14.5 million terms from 463 collections.
The EuroTermBank Federated network consortium currently consists of eight
members – four of which use a customised EuroTermBank Toolkit solution, while
the other four have established a synchronisation proxy with the EuroTermBank
database exchanging information with the network. These eight members
represent a total of eight countries (Austria, Denmark, Estonia, Iceland,
Latvia, Lithuania, Slovenia, and Sweden), which comprise academia and industry
leaders in terminology and language technologies. The network’s future goals
are to have at least one network member in each member state of the European
Union.

## 6\. Conclusion

We presented the EuroTermBank Toolkit, an open terminology management toolkit
for the EuroTermBank Federated Network. The toolkit addresses the problem of
outdated terminology data in shared terminology repositories by providing a
standards-based infrastructure for terminology management and sharing for
organisations across Europe and beyond. The ETBT facilitates standardisation
and streamlining of terminology curation by offering readily available tools
and infrastructure for collaboration and data sharing. The common approach
enabled by ETBT provides an easy to implement solution for any institution
needing a standards-based tool for terminology management and data sharing. It
also enables management of machine-readable data for machine translation
systems and other NLP tools and facilitates data synchronisation with
EuroTermBank – the largest multilingual terminology resource in Europe. The
instructions for the deployment of the ETBT are publicly available at:
https://github.com/Eurotermbank/Federated-Network-Toolkit-deployment.

## Acknowledgements

The research leading to these results has received funding from the research
project ”Competence Centre of Information and Communication Technologies” of
EU Structural funds, contract No. 1.2.1.1/18/A/003 signed between IT
Competence Centre and Central Finance and Contracting Agency, Research No.
2.9. “Automated multilingual subtitling”.

This work was partly done within the scope of eTranslation TermBank Project
(Action: 2019-EU-IA-0049) which is co-financed by the European Union’s
Connecting Europe Facility.

## 7\. Bibliographical References

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