Game theoretic approach of eavesdropping attack in millimeter-wave-based WPANs with directional antennas

Kim, Meejoung

doi:10.1007/s11276-018-1713-4

Cited by 7 publications

(4 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The use of game theory together with advanced ML in the database is incomplete. A similar game-theoretic-based approach in the wireless personal network area proposed by Kim 135 to analyze and detect passive eavesdropping attack was studied. In particular, for game eavesdroppers and transmissions, this is an emerging research area.…”

Section: Other Machine Learning Methodsmentioning

confidence: 99%

Machine learning in cybersecurity: a comprehensive survey

Dasgupta

Akhtar

Sen

2020

Journal of Defense Modeling & Simulation

106

View full text Add to dashboard Cite

Today’s world is highly network interconnected owing to the pervasiveness of small personal devices (e.g., smartphones) as well as large computing devices or services (e.g., cloud computing or online banking), and thereby each passing minute millions of data bytes are being generated, processed, exchanged, shared, and utilized to yield outcomes in specific applications. Thus, securing the data, machines (devices), and user’s privacy in cyberspace has become an utmost concern for individuals, business organizations, and national governments. In recent years, machine learning (ML) has been widely employed in cybersecurity, for example, intrusion or malware detection and biometric-based user authentication. However, ML algorithms are vulnerable to attacks both in the training and testing phases, which usually leads to remarkable performance decreases and security breaches. Comparatively, limited studies have been conducted to understand the essence and degree of the vulnerabilities of ML techniques against security threats and their defensive mechanisms. It is imperative to systematize recent works related to cybersecurity using ML to seek the attention of researchers, scientists, and engineers. Therefore, in this paper, we provide a comprehensive survey of the works that have been carried out most recently (from 2013 to 2018) on ML in cybersecurity, describing the basics of cyber-attacks and corresponding defenses, the basics of the most commonly used ML algorithms, and proposed ML and data mining schemes for cybersecurity in terms of features, dimensionality reduction, and classification/detection techniques. In this context, this article also provides an overview of adversarial ML, including the security characteristics of deep learning methods. Finally, open issues and challenges in cybersecurity are highlighted and potential future research directions are discussed.

show abstract

Section: Other Machine Learning Methodsmentioning

confidence: 99%

Machine learning in cybersecurity: a comprehensive survey

Dasgupta

Akhtar

Sen

2020

Journal of Defense Modeling & Simulation

106

View full text Add to dashboard Cite

show abstract

“…Tere have been many studies using game theory to analyze the behaviors of hackers and defenders [24][25][26]. Researchers build diferent models for diferent application environments and use game theory for macro analysis [27][28][29][30]. Li et al propose a zero-sum complete information static game model under the network topology graph and proposes a sensitive cost parameter for attacking and defensing [31].…”

Section: Introductionmentioning

confidence: 99%

Mixed Strategy Analysis in Attack-Defense Game Model Based on 5G Heterogeneous Network of CPS Using ncPSO

Liu

Chai

Liu

et al. 2022

Security and Communication Networks

View full text Add to dashboard Cite

The development of the 5th Generation Mobile Communication Technology not only brings convenience to people but also brings many network security problems. Based on the static game theory of complete information, a game model of attack and defense with limited resources in heterogeneous networks of Cyber Physical Systems is established. This model analyzes the basic rules of the offensive and defensive strategies of both parties when the offensive and defensive resources are limited in the 5th Generation Mobile Communication Technology network environment. The model can also describe the interaction between attackers and defenders. A novel compact particle swarm optimization algorithm is proposed to solve the difficult problem of solving the Nash equilibrium of this game model. The simulation experiment proves that novel compact particle swarm optimization algorithm has good optimization ability and shows that the algorithm can effectively solve the Nash equilibrium of the model. The simulation experiment provides a strategic reference for the attack-defense game with limited resources.

show abstract

“…Moreover, most of them focus on the scenario with single eavesdropper, such as [12]- [16], [18], [19]. The resource allocation for improving the security of mmWave communications was investigated in two recent works [20] and [21]. However, [20] and [21] focus only on the scenario with a legitimate transmission link, and also ignored the beam training overheads and the inter-beam inference of mmWave communications, while we concentrate on the scenario with multiple legitimate transmission links and multiple eavesdropping attack links as well as considering the beam training overheads and the interbeam inference of mmWave communications, which is more practical and more intractable.…”

Section: Introductionmentioning

confidence: 99%

Resource Management for Maximizing the Secure Sum Rate in Dense Millimeter-Wave Networks

2020

View full text Add to dashboard Cite

Due to the densifying of users and the reflections caused by small-scale objects, secure communication is an important research problem in dense millimeter-wave (mmWave) networks. In this paper, we investigate the resource management problem of joint transmission reception point (TRP) selection, power control and beamwidth selection for maximizing the secure sum rate in the dense mmWave network, while considering beamforming training overheads, blockage effect in mmWave communications, and imperfect channel state information (CSI) from a legitimate user equipment (LUE) to the corresponding malicious user equipment (MUE). To handle the problem with low complexity, we first break it down into two subproblems (i.e., TRP selection and joint power control and beamwidth allocation), and propose a two-stage game based decentralized resource management approach to solve them iteratively, where in the first stage we propose a matching game based distributed TRP selection algorithm to solve the first subproblem in which novel utilities for both LUEs and TRPs are designed to handle the high directivity transmission problem, and in the second stage we propose a weekly acyclic game based two-dimensionalstrategy concurrent better response algorithm for the second subproblem to deal with the huge strategy space, which has been proved to converge to an invariant two-dimensional-strategy Nash equilibrium (NE). Moreover, to adapt to the three-dimensional optimization variables and the huge strategy space of the considered problem, we also propose a new three-dimensional-strategy iterative weekly acyclic game to solve it, where the three-dimensional optimization variables are optimized alternately during the decisionmaking process. Finally, extensive simulations are conducted to verify the effectiveness of the proposed schemes. INDEX TERMS Millimeter-wave (mmWave) communication, resource management, game theory, secure sum rate.

show abstract

Game theoretic approach of eavesdropping attack in millimeter-wave-based WPANs with directional antennas

Cited by 7 publications

References 35 publications

Machine learning in cybersecurity: a comprehensive survey

Machine learning in cybersecurity: a comprehensive survey

Mixed Strategy Analysis in Attack-Defense Game Model Based on 5G Heterogeneous Network of CPS Using ncPSO

Resource Management for Maximizing the Secure Sum Rate in Dense Millimeter-Wave Networks

Contact Info

Product

Resources

About