A Game Theory Based Collaborative Security Detection Method for Internet of Things Systems

Wu, Hao; Wang, Wei

doi:10.1109/tifs.2018.2790382

Cited by 77 publications

(21 citation statements)

References 45 publications

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“…Miranda et al [2] propose a security framework for the detection and prevention of intrusions in software-defined networks. Hao et al [3] propose a collaborative game theory-based security detection framework for IoT to model the confrontation between attacker and defender to identify security events. Moreover, collaborative information sharing is achieved by consensus protocol.…”

Section: Motivationmentioning

confidence: 99%

A Secure Integrated Framework for Fog-Assisted Internet-of-Things Systems

Junejo

Komninos

McCann

2021

IEEE Internet Things J.

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Fog-Assisted Internet of Things (Fog-IoT) systems are deployed in remote and unprotected environments, making them vulnerable to security, privacy, and trust challenges. Existing studies propose security schemes and trust models for these systems. However, mitigation of insider attacks, namely blackhole, sinkhole, sybil, collusion, self-promotion, and privilege escalation, has always been a challenge and mostly carried out by the legitimate nodes. Compared to other studies, this paper proposes a framework featuring attribute-based access control and trust-based behavioural monitoring to address the challenges mentioned above. The proposed framework consists of two components, the security component (SC) and the trust management component (TMC). SC ensures data confidentiality, integrity, authentication, and authorization. TMC evaluates Fog-IoT entities' performance using a trust model based on a set of QoS and network communication features. Subsequently, trust is embedded as an attribute within SC's access control policies, ensuring that only trusted entities are granted access to fog resources. Several attacking scenarios, namely DoS, DDoS, probing, and data theft are designed to elaborate on how the change in trust triggers the change in access rights and, therefore, validates the proposed integrated framework's design principles. The framework is evaluated on a Raspberry Pi 3 Model B to benchmark its performance in terms of time and memory complexity. Our results show that both SC and TMC are lightweight and suitable for resource-constrained devices.

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Section: Motivationmentioning

confidence: 99%

A Secure Integrated Framework for Fog-Assisted Internet-of-Things Systems

Junejo

Komninos

McCann

2021

IEEE Internet Things J.

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“…It is estimated that approximately 20 billion devices will be connected to the IoT in 5G networks. Due to the openness of the transmission channel, the wireless network is more vulnerable to large-scale malicious attacks compared with the traditional wired network [9]. Traditional methods for securing wireless networks are often based on bit-level security protocols.…”

Section: To Wired Communication the Open Communication Environment Mmentioning

confidence: 99%

Specific Emitter Identification Techniques for the Internet of Things

Sa¹,

Lang

Wang³

et al. 2020

IEEE Access

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Specific Emitter Identification (SEI) detects the individual emitter according its varied signal characteristics. The method operates in the physical layer of the internet and can effectively improve the security of the Internet of Things (IoT). Generally, SEI identifies the uniqueness of the transmitting platform by using the unintentional modulation information of the emitter such as radar, which has ''fingerprint'' characteristics. Existing SEI methods are based on hand-crafted features to distinguish different emitters. In this paper, traditional feature extraction methods are studied and a new recognition method is proposed. To determine the effectiveness of the method, the output signals of eight amplifiers are collected as the research object. The power spectrum characteristics and adjacent channel power ratio (ACPR) of the signal are then extracted and eight amplifiers are distinguished. Finally, the quadrature-phase signals are converted into pictures, and convolutional neural networks are used to automatically extract features for classification and recognition. The results show that the recognition rate of converting signals into pictures can reach 95%, when SNR is 20dB. INDEX TERMS Specific emitter identification, IoT, power amplifier.

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“…In [17], a passive blind video similar tamper detection algorithm based on multi-scale normalized mutual information is proposed, which realizes video frame replication, frame insertion and frame deletion tamper detection. Meanwhile, some researchers apply game theory to prevent information from being tampered or theft in [18]- [20]. In [21], the interaction between attacker and defender was model as a noncooperative security game, players adjust their strategy by analyzing and predicting the opponent's strategy.…”

Section: Introductionmentioning

confidence: 99%

Privacy Protection: An Anti-Fraud Scheme Based on Improved Bayesian Game Model in Multimedia Communication System

et al. 2020

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In the multimedia communication system, users are often exposed to fraud information from malicious applications (such as providing fake bids and false content), which can easily cause privacy leakage. And multimedia information pluralism make it more difficult to protect users' privacy. To solve these problems, this article proposed an anti-fraud scheme based on improved Bayesian game model. First, we designed a Bargain-bayesian game model for modeling the interaction between applications and regular users. We used the Two-round bargain method to establish payoff matrix for two players and adjust it through regular user's detection rate dynamically. Then we obtained the application's best bid in the first round of bargain by backward induction to prevent malicious applications from sending fake bids. Second, we customized a group of test users and developed another interaction model between applications and users (i.e., regular and test) as the Two-side Bayesian game model based on sliding adaptive logistic regression method, then we put three influencing factors into the payoff matrix: test user's ratio, malicious application's ratio, and regular user's ratio. Through Bayesian Nash Equilibria analysis, we obtained values of three influencing factors when malicious applications provide true content, and thus solved the problem of malicious applications providing false content. Finally, experiment results proved that the new scheme had effectively raised expected payoffs for both players and their transaction achievement rate, and lowered the probability of users being deceived by malicious applications, which successfully solved the issue of users' privacy disclosure.

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A Game Theory Based Collaborative Security Detection Method for Internet of Things Systems

Cited by 77 publications

References 45 publications

A Secure Integrated Framework for Fog-Assisted Internet-of-Things Systems

A Secure Integrated Framework for Fog-Assisted Internet-of-Things Systems

Specific Emitter Identification Techniques for the Internet of Things

Privacy Protection: An Anti-Fraud Scheme Based on Improved Bayesian Game Model in Multimedia Communication System

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