Game theoretic attack analysis in online social network (OSN) services

White, Jonathan; Park, Joon Soo; Kamhoua, Charles A.; Kwiat, Kevin

doi:10.1145/2492517.2500257

Cited by 17 publications

(9 citation statements)

References 15 publications

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“…White et al (2013) developed a model of an online social network and simulated an attack towards this model. A game theoretic approach is used for the attack to analyze the actions an attacker and how much damage the attacker may cause to the system.…”

Section: Methods For Increasing User Awareness Of Privacymentioning

confidence: 99%

A Survey of Social Network Forensics

Karabiyik¹,

Canbaz²,

Aksoy³

et al. 2016

JDFSL

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Social networks in any form, specifically online social networks (OSNs), are becoming a part of our everyday life in this new millennium especially with the advanced and simple communication technologies through easily accessible devices such as smartphones and tablets. The data generated through the use of these technologies need to be analyzed for forensic purposes when criminal and terrorist activities are involved. In order to deal with the forensic implications of social networks, current research on both digital forensics and social networks need to be incorporated and understood. This will help digital forensics investigators to predict, detect and even prevent any criminal activities in different forms. It will also help researchers to develop new models/techniques in the future. This paper provides literature review of the social network forensics methods, models, and techniques in order to provide an overview to the researchers for their future works as well as the law enforcement investigators for their investigations when crimes are committed in the cyber space. It also provides awareness and defense methods for OSN users in order to protect them against to social attacks.

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Section: Methods For Increasing User Awareness Of Privacymentioning

confidence: 99%

A Survey of Social Network Forensics

Karabiyik¹,

Canbaz²,

Aksoy³

et al. 2016

JDFSL

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“…Deng et al [25] proposed a theoretical dynamic model in social networks based on reciprocal altruism by utilizing the N-player prisoner's dilemma (NPD). There are very many game-theoretical studies on social networks and social media (e.g., [26][27][28]), and some of these studies insisted that online social networks have common features with public goods [29,30]. Therefore, some studies attempt to model SNSs in the framework of the evolutionary game like the public goods game [3,9,31,32].…”

Section: Related Workmentioning

confidence: 99%

Modeling and analyzing users’ behavioral strategies with co-evolutionary process

2021

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Social networking services (SNSs) are constantly used by a large number of people with various motivations and intentions depending on their social relationships and purposes, and thus, resulting in diverse strategies of posting/consuming content on SNSs. Therefore, it is important to understand the differences of the individual strategies depending on their network locations and surroundings. For this purpose, by using a game-theoretical model of users called agents and proposing a co-evolutionary algorithm called multiple-world genetic algorithm to evolve diverse strategy for each user, we investigated the differences in individual strategies and compared the results in artificial networks and those of the Facebook ego network. From our experiments, we found that agents did not select the free rider strategy, which means that just reading the articles and comments posted by other users, in the Facebook network, although this strategy is usually cost-effective and usually appeared in the artificial networks. We also found that the agents who mainly comment on posted articles/comments and rarely post their own articles appear in the Facebook network but do not appear in the connecting nearest-neighbor networks, although we think that this kind of user actually exists in real-world SNSs. Our experimental simulation also revealed that the number of friends was a crucial factor to identify users’ strategies on SNSs through the analysis of the impact of the differences in the reward for a comment on various ego networks.

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“…A number of previous works apply traditional game theoretic approach for cyber security problems. In [26] and [27], the authors apply a Markov decision process (MDP) to secure information sharing in online social networks. In addition, [28] and [29] apply a Markov game framework for optimal data management in online social network.…”

Section: Game Theory For Cyber Securitymentioning

confidence: 99%

Game Theory with Learning for Cyber Security Monitoring

Chung

Kamhoua

Kwiat

et al. 2016

2016 IEEE 17th International Symposium on High Assurance Systems Engineering (HASE)

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Carefully crafted computer worms such as Stuxnet and recent data breaches on retail organizations (e.g., Target, Home Depot) are very sophisticated security attacks on critical cyber infrastructures. Such attacks are referred to as advanced persistent threat (APT), and are on constant rise with severe implications. In all these attacks, the presence of an attacker itself is difficult to detect as they log-in as legitimate users. Hence, these attacks comprising multiple actions are challenging to differentiate from benign and therefore common detection techniques have to deal with high false positive rates. While machine learning and game theoretic models have been applied for intrusion detection, machine learning techniques lack the ability to model the rationality of the players, while the game theoretic approaches rely on the strict assumption of full rationality and complete information. This thesis discusses an approach that proposes Q-Learning to model the decision process of a security administrator which addresses the joint limitations of using game theory and machine learning techniques for this problem. This work compares variations of Q-Learning with a traditional stochastic game model by performing a simulation under different pair of profiles for attackers and defenders using parameters derived from real incident data of a large computer organization.Analysis on the strengths and weaknesses of the algorithms, and how the parameters in the algorithms affect the performance are studied. Simulation results show that Naive Q-Learning, despite the restricted information on the opponent, better reduces the impact of an attacker compared to Minmax Q-Learning against all attackers, or Stochastic Games players against less rational opponents.ii To my parents, for their love and support.iii ACKNOWLEDGMENTS

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Game theoretic attack analysis in online social network (OSN) services

Cited by 17 publications

References 15 publications

A Survey of Social Network Forensics

A Survey of Social Network Forensics

Modeling and analyzing users’ behavioral strategies with co-evolutionary process

Game Theory with Learning for Cyber Security Monitoring

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