Benign false-data injection as a moving-target defense to secure mobile wireless communications

Ghourab, Esraa M.; Azab, Mohamed

doi:10.1016/j.adhoc.2019.102064

“…Note that the intercept probability is calculated according to (17) and (18). Since SR 3 has the lowest intercept probability, it can easily conclude that SR 3 has the highest reputation according to (20), as shown in Fig. 5 for the two interception scenarios.…”

Section: B Intercept Probabilities and Srs Reputationsmentioning

confidence: 91%

Reputation-Aware Relay Selection with Opportunistic Spectrum Access: A Blockchain Approach

Ghourab¹,

Bariah²,

Muhaidat³

et al. 2022

Preprint

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<p>The highly dynamic nature of cognitive radio (CR) systems and their stringent latency requirements pose a major challenge in the realization of efficient intelligent transport systems (ITS). In this paper, we investigate relay selection and opportunistic spectrum access in conjunction with blockchain technology in a secure manner. Specifically, we propose a cross-layer method for secure relay selection, where secondary relays (SRs) are granted access to available spectrum bands based on the balance of their respective virtual wallets. These virtual wallets, which are built based on the SRs' secrecy capacity and their behavior in the network, are the predominant factors that allow SRs to participate in an auction model. To quantify the trustworthiness of the SRs, we formulate a mathematical framework to evaluate the trust value of each SR, which is then leveraged for rewarding or penalizing the SR. Furthermore, we develop an offline blockchain framework to store the information of participating relays and make it available for future operations, in order to detect reputable and non-reputable relays in the presence of multiple eavesdroppers. The stored reputations of the participating relays are used to develop a self-learning algorithm to exclude the non-reputable relays from the selection group. Finally, we present thorough numerical results to demonstrate the superiority of the proposed system in terms of security, credibility, and integrity.</p>

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“…On the other hand, a spatiotemporal diversity-based mechanism has been evaluated to confuse eavesdroppers by sending fake data over relays/vehicles traveling on a multi-lane road through dynamic paths. Moreover, in [20], the authors presented a novel MTD framework to improve the channel secrecy capacity in a two-phase decode-and-forward (DF) network. The approach MTD proposed in [20] enables multi-dimensional spatiotemporal diversification of user traffic in a cooperative wireless network, preventing attackers from tracing the signal transmission pattern.…”

Section: A Related Workmentioning

confidence: 99%

Reputation-Aware Relay Selection with Opportunistic Spectrum Access: A Blockchain Approach

Ghourab¹,

Bariah²,

Muhaidat³

et al. 2022

Preprint

View full text Add to dashboard Cite

<p>The highly dynamic nature of cognitive radio (CR) systems and their stringent latency requirements pose a major challenge in the realization of efficient intelligent transport systems (ITS). In this paper, we investigate relay selection and opportunistic spectrum access in conjunction with blockchain technology in a secure manner. Specifically, we propose a cross-layer method for secure relay selection, where secondary relays (SRs) are granted access to available spectrum bands based on the balance of their respective virtual wallets. These virtual wallets, which are built based on the SRs' secrecy capacity and their behavior in the network, are the predominant factors that allow SRs to participate in an auction model. To quantify the trustworthiness of the SRs, we formulate a mathematical framework to evaluate the trust value of each SR, which is then leveraged for rewarding or penalizing the SR. Furthermore, we develop an offline blockchain framework to store the information of participating relays and make it available for future operations, in order to detect reputable and non-reputable relays in the presence of multiple eavesdroppers. The stored reputations of the participating relays are used to develop a self-learning algorithm to exclude the non-reputable relays from the selection group. Finally, we present thorough numerical results to demonstrate the superiority of the proposed system in terms of security, credibility, and integrity.</p>

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“…The honeypot system maintains a sufficient number of hardware and software resources to execute the attack monitoring activities. Also, the system contains the huge active and passive attacker data sets 1–4 …”

Section: Introductionmentioning

confidence: 99%

“…Also, the system contains the huge active and passive attacker data sets. [1][2][3][4] Particularly, honeypot creates a duplicate and isolated network to validate the external traffics. It helps improve the security of main networks.…”

Section: Introductionmentioning

confidence: 99%

Deep reinforcement learning for building honeypots against runtime DoS attack

Veluchamy¹,

Kathavarayan²

2021

Int J of Intelligent Sys

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Honeypot is a network environment used to protect the legitimate network resources from attacks. Honeypot creates an environment that impresses attackers to inject their activities to steal resources. This is a way to detect the attacks by doing attack detection procedures. In this work, Denial of Service (DoS) attacks are effectively detected by proposed honeypot system. Machine Learning (ML) and Deep Learning (DL) methods evolve in many areas to build intelligent decision making systems. This work uses DL approaches and secures event validation procedures for finding predicting DoS attacks. The proposed system called Deep Adaptive Reinforcement Learning for Honeypots (DARLH) is implemented to monitor internal and external DoS attacks. In the honeypot environment, the proposed DARLH system implements DARL based IDS (Intrusion Detection System) agents and Deep Recurrent Neural Network (DRNN) based IDS agents for monitoring multiple runtime DoS attacks. These techniques support for dynamic IDS against DoS attack. In addition, the DARLH creates protected poison distribution and server side supervision system for keeping the monitoring events legitimate.This work is implemented and performance is evaluated. The results are compared with existing 2 systems like GNBH, BCH and RNSG. In this comparison, the proposed system provides 5% to 10% better results than other systems.

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Benign false-data injection as a moving-target defense to secure mobile wireless communications

Cited by 14 publications

References 13 publications

Reputation-Aware Relay Selection with Opportunistic Spectrum Access: A Blockchain Approach

Reputation-Aware Relay Selection with Opportunistic Spectrum Access: A Blockchain Approach

Reputation-Aware Relay Selection with Opportunistic Spectrum Access: A Blockchain Approach

Deep reinforcement learning for building honeypots against runtime DoS attack

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