=Paper=
{{Paper
|id=Vol-2590/paper28
|storemode=property
|title=Empirical Study on Trust, Reputation, and Game Theory Approach to Secure Communication in a Group of Unmanned Vehicles
|pdfUrl=https://ceur-ws.org/Vol-2590/paper28.pdf
|volume=Vol-2590
|authors=Egor Marinenkov,Sergey Chuprov,Ilya Viksnin,Iuliia Kim
|dblpUrl=https://dblp.org/rec/conf/micsecs/MarinenkovCVK19
}}
==Empirical Study on Trust, Reputation, and Game Theory Approach to Secure Communication in a Group of Unmanned Vehicles==
https://ceur-ws.org/Vol-2590/paper28.pdf
Empirical Study on Trust, Reputation, and
Game Theory Approach to Secure
Communication in a Group of Unmanned
Vehicles
Egor Marinenkov[0000−0001−9895−239X] , Sergey Chuprov[0000−0001−7081−8797] ,
Ilya Viksnin[0000−0002−3071−6937] , and Iuliia Kim[0000−0002−6951−1875]
ITMO University, Saint-Petersburg, Russia
egormarinenkov@gmail.com, chuprov@itmo.ru, wixnin@mail.ru,
yulia1344@gmail.com
Abstract. The paper presents an approach based on a combination
of Reputation, Trust, and Game Theory to ensure secure data transfer
in distributed networks of unmanned autonomous vehicles. Trust and
Reputation-based approaches have gained popularity in computer net-
works to improve their security. The existence of “soft” attacks, when
the initiator of the attack is a legitimate agent, and traditional means of
protecting information are powerless, it is possible to use methods based
on trust. Using Game Theory approaches in cybersecurity allows opti-
mizing intrusion detection and network security systems. In this paper,
we reviewed the foundations of Trust and Reputation-based models, and
Game Theory approaches in computer systems and attempts of its imple-
mentations in distributed network security and communication protocols.
We described the operating conditions of a group of AVs, elaborated an
apparatus for calculating the indicators of Trust and Reputation, and
presented an approach based on a combination of Game Theory with
Trust and Reputation. To validate the effectiveness of the proposed ap-
proach a custom software simulator was developed. Experiments with a
group of AVs driving through the intersection was conducted. The re-
sults showed that a combination of Trust, Reputation and Game Theory
allows more effective detection of bogus data, transmitted by the agents
in a system.
Keywords: Trust · Reputation · Game Theory · Multi-agent System
1 Introduction
With the development of global technological progress, robotic systems are be-
ginning to be applied in various areas of human life. Auto manufacturers are
actively researching the development and integration of AVs on the roads of our
Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0).
2 E. Marinenkov et al.
cities. Small unmanned aerial vehicles (drones) have become widespread in ev-
eryday life and can be used for a variety of purposes: from searching for missing
people to farming. One of the main advantages of AVs is the ability to perform
complex, dangerous and monotonous for humans’ organism tasks. To perform
some tasks, it is more promising to use groups of fully autonomous robots ca-
pable of functioning without a human-operator and more efficiently performing
tasks distributed in the area.
Each robotic device can be represented as a combination of physical and
information components, called the cyber-physical system [8]. To study groups
of autonomous robotic devices, a multi-agent approach has gained popularity
[1]. In this approach, all robots are represented by a set of agents capable of
interacting with each other and performing common, group tasks. For optimal
planning of group actions, they can use the data obtained with the sensors from
the environment and transmit them to each other via a wireless communication
channel. However, as with any network, the transmitted data between elements
may be subject to security threats.
2 Literature Review
2.1 Trust and Reputation
In some social networks, online stores and e-commerce applications, user rep-
utation rating systems have gained popularity. The presence of a reputation
indicator implies the existence of certain generally accepted norms and rules of
behavior on a resource. Violation of such rules and norms by the user will lead
to a decrease in the indicator of his reputation, as well as to a decreasing trust
to him from other users. For example, if one of the sellers of the online store
selling a product that has characteristics different from the declared ones, or
the delivery time did not correspond to the expected one, it is less likely that
buyers will want to buy goods from him if there are other sellers who are more
trustworthy.
Depending on the sources, interpretations of Trust and Reputation (T&R)
may vary slightly. The content of these concepts goes deep into antiquity, with
the advent of the first groups of people and the interaction between them, those
concepts also appeared that can now be described as T&R. The work [6] defines
trust as an open and positive relationship between people, containing confidence
in decency and goodwill. If we move away from the human relationship and
describe the trust between some agents in a computer system, in [10] trust
described as a subjective expectation of agent A of certain behavior from agent
B based on a history of interactions. It follows from the definition that trust
allows us to assume what kind of expected action or inaction might come from
the agent. From the same definition follows the subjectivity of trust in relation
to one or another object of relationships.
Reputation is defined as an opinion about the intentions and norms of a
particular agent, based on the history of his behavior and interactions with him
T&R and GT Approach to Secure Communication in a Group of UV 3
[10]. Quantification can be calculated based on the opinions or observations of
other group members. Unlike subjective trust (relying on one’s own experience
and other factors), reputation allows reflecting a public measure of the agent’s
reliability based on observations or assessments of group members.
To use the approach based on T&R in information systems, it is necessary
to formalize and take into account quantitative indicators of T&R and data
on observations and assessments. This can be especially relevant in decentral-
ized networks, where there is a lack of network infrastructure and the nodes
interact directly with each other. Such networks are called peer-to-peer (P2P)
networks [15]. P2P networks have gained widespread popularity with the advent
of the Internet of Things (IoT) concept [16] and vehicular (VANETs) and mo-
bile (MANETs) ad-hoc networks [17]. P2P allows to transfer and process large
amounts of information, at a cost lower than using a centralized infrastructure
network [3]. However, due to the decentralized structure, the presence of het-
erogeneous elements and specific features, such networks are subject to “soft”
attacks aimed at the contextual integrity of the transmitted data between nodes.
“Traditional” cybersecurity methods, such as authentication or cryptography,
are not effective against such attacks.
In the case of AVs, VANETs allow transmitting data from one vehicle to
another and to the transport infrastructure objects. Such data transfer can be
used by the Intelligent Transport System (ITS) to build optimal routes, gener-
ate informational and emergency messages warning of bad weather conditions,
construction and maintenance road works, and etc. Papers offering T&R-based
data security techniques may offer different approaches to calculating these met-
rics. For example, the authors of [11] suggest calculating the trust indicator in
the range from −1 to 1, when, as in [4], it is proposed to calculate the T&R
indicators in the range from 0 to 1. It is worth noting that in the present paper
we use the calculus described in [4] and complement it with an approach based
on Game Theory.
In [18] Starub et al. proposed a multi-level intrusion detection system (IDS)
to protect self-driving vehicles from malicious attacks, as well as false data.
The system is based on the method of determining the reputation of nodes.
The system contains shared knowledge generated by all communication partic-
ipants. The level of reputation depends on the history of the behavior of one
or another node. Despite the interesting system architecture proposed by the
authors, it is difficult to evaluate the effectiveness of the proposed solution. The
work lacks both calculus to calculate the reputation level, as well as validation of
the effectiveness of the solution and comparison with other existing T&R-based
approaches.
Kim and Viksnin in [7] proposed a method for calculating T&R, based on
the theory of loans to ensure the security of flying drones communication. The
idea of the method is that it would be unprofitable for the saboteur to perform
a destructive impact on the group. In case the agent transmits incorrect infor-
mation, its indebtedness increases. The results of the experiments showed that
the intruder transmitting incorrect data was blocked in 90.2% cases.
4 E. Marinenkov et al.
To verify the reliability of the data, two approaches are proposed in the
papers: objective and subjective. In the second case, the nodes rely on the opinion
of other nodes to form a trust indicator. In [13], the authors addressed the
data privacy problem when calculating the trust of the nodes and proposed a
framework that allows finding a balance between trust and privacy in the system.
Experiments conducted using the ONE network simulator showed that the use
of the proposed linkability protocol can increase the privacy of transmitted data
by using pseudonyms for nodes and offers more flexibility than the standard
secure broadcast authentication protocol used in the ONE simulator.
2.2 Game Theory
Game theory is a branch of mathematical economics that studies the resolution
of conflicts between players and the optimality of their strategies. It is widely
used in various fields of human activity, such as economics and management,
industry and agriculture, military and construction, trade and transport, com-
munications, etc [2].
One of the tasks of Game Theory implementation in the field of cybersecurity
is to optimize the actions of the security administrators in network systems. In
the context of Game Theory, this task can be formalized as follows: there are two
coalitions: defenders (administrators) and attackers; the goal of administrators
is to minimize damage to the system by optimally distributing tasks among
themselves, and the goal of the attackers is to exploit the system. Considering
the different behavior of attackers, it is possible to identify such strategies for
the behavior of administrators (both for a coalition and for each administrators),
in which, regardless of the attackers strategy, the damage to the system will be
minimal. One of the approach was described in the [5]. The authors found a
strategy in which Nash equilibrium is achieved, which guarantees an optimal
solution to the defending side, regardless of the attackers decisions. The authors
conducted a comparative analysis of approaches to ensuring a safety circuit based
on Game Theory and common sense decision algorithms. To verify the developed
model, real statistics were used from Hackmageddon, the Verizon 2013 Data
Breach Investigation report, and the Ponemon report of 2011.
In [14] Roy et al. provide an overview of the game-theoretic models appli-
cation for network security assurance. Authors review static games and divide
them into complete imperfect information and incomplete imperfect information
games. In the former type of game, the authors cite the example of an informa-
tion war and a quantitative risk assessment for effective investment decisions in
the field of cybersecurity. The latter gave examples of games in the framework
to counter DDoS and intrusions in ad-hoc networks. The authors also analyze
dynamic games and subdivide them into 4 types: complete perfect information,
complete imperfect information, incomplete perfect information and incomplete
imperfect information games. The first type of games is used for risk analysis
in computer networks, where, as a rule, there are only two participants: a net-
work administrator and an attacker. Implementation of Game Theory allows
T&R and GT Approach to Secure Communication in a Group of UV 5
to determine the optimal strategy for several iterations, which allows to opti-
mally distribute resources for long periods of time. For the second type, an IDS
and several scenarios, based on the completeness of knowledge about the system
by attackers were considered. This approach allows to determine the optimal
strategies for the players, which can subsequently be applied as a deciding rule
when implementing or modifying such a system. Third type described a game
in which network participants reduce the propagation speed of a worm-attack,
which allows to scan a system for important and valuable information. In the
fourth type, games like admin-attacker were also considered. The described pa-
per is interesting in the way that it considers Game Theory applications under
various conditions of density and correctness of the available data from players.
In [19] Game Theory is used for security assurance in electronic commerce
applications. The authors describe the security game model using the penalty
parameter, calculate replicator dynamics, and analyze the evolutionary stable
strategy of the game model. As a result, the authors conclude that reducing
the cost of investment leads to the stimulation of investment in cybersecurity.
With an increase in investment costs, the penalty parameter allows to save the
incentive for investments.
The described papers on T&R and Game Theory approaches show the expe-
diency of applying such approaches in the areas related to distributed networks
and automated systems. We have already published works on the results of ap-
plying T&R in the group of AVs, and we believe that refining this approach
using Game Theory fundamentals will help to achieve better results.
3 Problem Statement
There are various types of attacks on data transferred between agents in the
system. Attacks can be both passive when the attacker does not directly influ-
ence the system, or active when an unauthorized modification of information
assets, system properties, and its state occurs during the attack. As a means of
counteracting these malicious attacks, various defenses can be used. However,
there are some attacks when agents already authorized in the system, which
were initially considered legitimate, begin to transmit inaccurate data due to
the failure of the sensors or unauthorized interference with the hardware and
software components. Using these improper data to optimize group actions can
lead to a decrease in the efficiency of the system, and in the case of groups of
AVs, it can lead to a traffic accident. Such attacks on context data integrity are
called “soft” attacks in the literature [20].
To counter “soft” attacks on multi-agent systems, a T&R approach that has
gained popularity in the field of e-commerce can be used [12, 9]. In this paper, we
address the problem of contextual data integrity in the group of mobile robots
and propose a model, based on T&R indicators using elements of the Game
Theory. The next section describes our approach in more detail.
6 E. Marinenkov et al.
4 Our Approach, Based on Trust, Reputation and Game
Theory
First of all, it is necessary to describe the limitations, assumptions, and oper-
ating conditions of the system to which the described calculus can be applied
to calculate the T&R indicators of the agents. The group of AVs can be de-
scribed as a set E = {e1 , e2 , . . . , en } of agents. The process of system functioning
can be described as a discrete process, and accordingly consists of many states
T = {t0 , . . . , tend }, where t0 is the initial moment, and tend is the endpoint in
time. At each moment of time, the agents transmit to each other data on their
location and state of the surroundings. Agents obtain these data using on-board
sensors. These data are necessary for further planning and optimization of group
actions, the description of which is beyond the scope of this paper. As an as-
sumption, we consider the transfer of data between agents in ideal conditions,
that is, without loss and interference.
The transmitted data can be either correct or bogus. In the first case, the
data reflects the actual (real) location and environment characteristics of the
agent ei at the time of transmission tj . In the second case, the data is incorrect
and does not reflect the real characteristics of the agent ei at the time of the data
transfer tj . The data may be bogus due to breakdown, failure of the sensors or
malicious interference with the software and hardware components of the agent
ei .
To identify agents that transmit bogus data, we propose the following pro-
cedure based on T&R assessment. Each of the group agents has an indicator of
T&R. The assessment is based on the transmitted data verification at each time
moment t by other agents. To describe our approach, we need to introduce three
indicators: Truth, Trust, and Reputation.
4.1 Truth, Trust, and Reputation
T ruth - an indicator that displays a subjective correctness assessment of the
transferred data by other agents. Correctness is determined using the sensors of
agents and can be described as (1).
T rutht = ftrt (data), (1)
where T rutht is the evaluation of data at the time t, data is the data to be
evaluated, ftrt is the evaluation function of T ruth at the time t.
Reputation (R) is an indicator based on a retrospective of the T ruth assess-
ment of each agent. It can be described as (2).
Rt = frt (T rutht ) = frt (ftrt (data)), (2)
where Rt is the reputation value at the time t, frt is the evaluation function of
R at the time t.
T&R and GT Approach to Secure Communication in a Group of UV 7
T rust is an indicator characterizing a subjective assessment of agent behavior
by other agents. It is calculated based on a combination of T ruth and R and
can be represented as (3).
T rustt = ftrustt (Rt−1 , T rutht ) = ftrustt (frt−1 (ftrt−1 (data)), ftrt (data)), (3)
where T rustt is the indicator of T rust at the time t, ftrustt is the function of
evaluating T rust at the time t.
As a limitation, each of the above indicators is in the range of [0, 1]. However,
in the process of functioning of the system, situations may arise when none of
the agents has the opportunity to assess the correctness of the data transmitted
to them. For example, such a situation may arise in the t0 time moment (initial-
ization of the system), when agents are distributed over the area and they do
not have a retrospective assessment, or when a new agent joins the group. As an
assessment mechanism in such situations, we propose using the approach based
on the Game Theory described below.
4.2 Game Theory
In the context of Game Theory, a game will be understood as a confrontation
between two agents: G - a trusted agent that receives data, and U - an agent that
transfers data. Players have two strategies. For agent G: strategy 1 - trust to the
agent U , and strategy 2 - do not trust to the agent U . For agent U : strategy 1 -
transmit correct data, and strategy 2 - transmit bogus data. In order to consider
the game in normal form and express it through the payment matrix, payments
when the players win/lose can be designed.
Since agent G cannot confirm or refute the data at the time of its receipt, it is
necessary to consider the risk of losing reliable data. For this, it is necessary to in-
troduce the concept of the value of data. Let datai ∈ DAT A be the kind of infor-
mation existing inside the system. Then ∃ v(datai ) : v(datai ) 6= v(dataj ), i 6= j
is the maximum value of the data i. We consider the fact, that the value of data
decreases with time, and it is necessary to introduce the concept of the value of
data at time t. Let ∃ tf : 0 < tf ≤ t be the time point for receiving data, then
∃ v(datai , tf , t) : v(datai , tf , t) ≤ v(datai ) is the value of the data i at the time
t. It can be calculated by the equation (4).
v(datai , tf , t) = v(datai ) × kdatai (tf , t), (4)
where kdatai (tf , t) is the function of relevance of the data i at time t.
We consider k(tf , t) 6= 0 as long as the agent cannot refute the data, therefore,
we chose an exponential function of the form ax , presented in (5) for calculating
the actuality of the data.
kdatai (tf , t) = (adatai )t−tf , (5)
where adatai ∈ (0; 1). Therefore, kdatai (tf , t) ∈ (0; 1].
8 E. Marinenkov et al.
Based on the foregoing, agent’s payoff function G can be described by the
equation (6).
v(datai ) x = 1, y = 1
0 x = 1, y = 2
fG (x, y) = , (6)
v(datai , tf , t) x = 2, y = 1
v(datai , tf , t) x = 2, y = 2
where x, y is the number of agents’ G and U strategies.
For the agent U , the biggest gain will be the the value T ruth(datai ) = 1 of
the agent G, in the case when the agent U lied, and minimal - when the agent
G has trust to U , and U provided him with correct data. To denote the wins of
the agent U , we introduce the payoff function, presented in (7).
−1 x = 1, y = 1
1 x = 1, y = 2
fU (x, y) = , (7)
0 x = 2, y = 1
0 x = 2, y = 2
where x, y is the number of agents’ G and U strategies. In the Table 1 the
payment matrix of the game is represented.
Table 1. Game payoff matrix.
Player U
Strategy 1 Strategy 2
Strategy 1 v(datai ); −1 0; 1
Player G
Strategy 2 v(datai , tf , t); 0 v(datai , tf , t); 0
We assume that the agent U is rational in its actions with respect to G. Let
us consider the resulting winnings. It can be seen from the Table 1 that the game
has Nash equilibrium, namely, the outcome when agent U chooses strategy 2, and
agent G chooses strategy 2. In this case, the equilibrium exists because agents
cannot increase their winnings when the opponent does not change strategies.
Consequently, the agent U , if he is an intruder, will prefer to lie, since his
winnings will fluctuate from 0 to 1, but not from -1 to 0. In turn, the agent G
needs to understand that he will not receive the maximum win, no matter what
strategy U agent chooses, the winnings will be stable. Thus, the authors formed
the third condition for the formation of T ruth indicator - in the case when it is
not possible for the agent to verify the information personally or by asking other
trusted agents, the agent must not be trusted and T ruth(datai ) = 0.
T&R and GT Approach to Secure Communication in a Group of UV 9
1 2 3 4 5 6 7 8 9 10
11 12 13 14 15 16 17 18 19 20
21 22 23 24 25 26 27 28 29 30
31 32 33 34 35 36 37 38 39 40
41 42 43 44 45 46 47 48 49 50
51 52 53 54 55 56 57 58 59 60
61 62 63 64 65 66 67 68 69 70
71 72 73 74 75 76 77 78 79 80
81 82 83 84 85 86 87 88 89 90
91 92 93 94 95 96 97 98 99 100
Fig. 1. Model of intersection and schematic representation of the vehicles’ driving
direction.
5 Software Simulation
To validate the effectiveness of our Game Theory approach implementation in
a group of AVs, custom software simulator was developed. In the simulator,
the movement of vehicles through an intersection was imitated. The intersection
scheme is represented in Fig. 1. It has the following properties:
– software testing ground is divided into equal sectors, and each sector has its
unique number;
– software testing ground size: 10 × 10 sectors;
– software testing ground has 4 roads: two vertical (oncoming and passing)
and two horizontal (oncoming and passing).
The experiment was conducted with a group consisted of 3 vehicles (agents):
one of them was a saboteur and provided incorrect data to the other agents. The
communication in the group was organized in the following way:
– depending on a situation, the saboteur can either transmit correct of bogus
data;
– legitimate agents also can provide others with bogus data in case of technical
problems; the probability of technical fail occurrence was set as 0.1;
– the initial value of agent reputation (R) was equal to 0.5;
– the agent that had the R value equal or less than 0.25 was considered as
a saboteur and blocked from the group communication; such threshold was
set because of the short communication period in the intersection;
– the information transmitted by one vehicle could be assessed only if this
vehicle was visible to one of the trusted agents; the vehicle can detect others
if they are located in one of the 8 adjacent sectors around it.
10 E. Marinenkov et al.
For the experiment, we decided to use a group of three AVs. Since one AV can
detect objects in 8 sectors around itself, in order to ensure the correct operation
of the T&R-based method, three members of the group are enough on this
software testing ground. An increase in the number of group participants at the
intersection will lead to the possibility of using agent’s own observations, rather
than relying on the proposed method.
Experiment setup:
– the experiment had 1000 independent tests;
– during each test, the movement of the vehicle group through the intersection
was analyzed.
Results assessment:
– to assess the obtained results, 4 parameters were calculated:
• True Positive (TP) - case when the AV was a saboteur and it was clas-
sified as a saboteur;
• False Positive (FP) - case when the AV was not a saboteur but was
classified as a saboteur;
• True Negative (TN) - case when the AV was a legitimate agent and was
classified as a correct agent;
• False Negative (FN) - case when the AV was a saboteur and was classified
as a legitimate agent.
– based on these values, a classification parameter Accuracy was calculated,
as in (8).
TP + TN
Accuracy = (8)
TP + FP + TN + FN
The comparative analysis between 2 models was performed:
– model with standard T&R-based approach;
– model with T&R and integrated Game Theory approach.
The average results after 1000 independent tests are represented in Fig. 5.
Our T&R and Game Theory approach contributed to the increase of saboteur
detection accuracy: it is possible to note that the Accuracy parameter raised by
0.1. Moreover, implementation of Game Theory provided a significant increase
in TP agent classification cases and helped to reduce FN rate values. However,
the rise of FP errors and decrease of TN rate was noted. The reason of these
deviations is that the Game Theory approach does not give an opportunity to
set up a median T ruth level not only to saboteurs but also to legitimate agents.
6 Acknowledgments
This article was prepared with the financial support of the Ministry of Science
and Higher Education of the Russian Federation under the agreement No. 075-
15-2019-1707 from 22.11.2019 (identifier RFMEFI60519X0189, internal number
05.605.21.0189).
T&R and GT Approach to Secure Communication in a Group of UV 11
2 1.94 without Game Theory
with Game Theory
1.5
Rates values
1.27
1 0.89
0.8
0.73 0.71 0.72
0.5
0.2
0.11
0.06
0
TP FP TN FN Accuracy
Fig. 2. Average True Positive, False Positive, True Negative, False Negative, and cal-
culated Accuracy values on 1000 testing runs and its comparison with and without
using Game Theory approach.
7 Conclusion
In this paper, we proposed an approach based on a combination of Trust, Rep-
utation, and Game Theory fundamentals to secure communication in a group
of unmanned vehicles. In addition to traditional attacks on data in distributed
networks, there are also “soft” attacks when legitimate agents broadcast bogus
data, which may be due to technical problems or unauthorized malicious access
to the agent’s hardware and software. The paper describes the characteristics
and assumptions of a group of unmanned vehicles in which the implementation
of the presented model is possible. To validate the effectiveness of the presented
approach in a group of unmanned autonomous vehicles, we developed custom
software simulator. Simulator allows to imitate traversal of the intersection by a
group of AVs and communication between them. Despite the fact that the classi-
fication accuracy of saboteurs, transmitted bogus data, increased by 0.1, using of
the T&R in a combination with Game Theory approach allowed to significantly
increase the True Positive and reduce False Negative values.
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