On-Off Attacks Mitigation against Trust Systems in Wireless Sensor Networks

Labraoui, Nabila; Gueroui, Mourad; Sekhri, Larbi

doi:10.1007/978-3-319-19578-0_33

Cited by 25 publications

(17 citation statements)

References 17 publications

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“…Labraoui et al proposed an On‐Off attack mitigation against trust systems (O 2 Trust) 18 . Unlike previous trust models, O 2 Trust used the punishment future as a punishment mechanism parameter, which helped to perceive malicious node that aimed to launch intelligent attacks against trust‐establishment and consequently On‐Off attack was mitigated effectively (Figure 1).…”

Section: Related Workmentioning

confidence: 99%

BTDS: Bayesian‐based trust decision scheme for intelligent connected vehicles in VANETs

Fang

Zhang

Liu

et al. 2020

Trans Emerging Tel Tech

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Intelligent connected vehicles (ICVs) are becoming the mainstream of the global automotive industry in the future, and its information security issues gradually emerge. The heterogeneity of Vehicle Ad hoc NETworks (VANETs) and the mobility of terminal nodes make the defense against the security attacks very complex, especially the internal attacks. In this article, toward defense against the internal attacks for ICVs in VANETs, we first propose a Gaussian-distribution-based and third-party-recommendation trust management model, which is called GTTMM, to solve the issue that the trust value increases too fast under continuous cooperation. And Bayesian-based trust decision scheme extending from GTTMM is named as BDTS, which incorporates the Bayesian Network, for defending against a variant of On-Off attack, called high trust value On-Off attack (HTV-On-Off attack). With the simulation study, it is demonstrated that, compared with reputation-based framework for high integrity sensor networks (RFSN), a traditional trust management model under the Beta distribution, GTTMM can better satisfy the principle of trust value, that is, "hard to get, easy to lose", while BDTS can defend against the HTV-On-Off attack effectively.

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Section: Related Workmentioning

confidence: 99%

BTDS: Bayesian‐based trust decision scheme for intelligent connected vehicles in VANETs

Fang

Zhang

Liu

et al. 2020

Trans Emerging Tel Tech

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“…Thus, the network should allow the detection of intrusions. It is therefore necessary to design robust and lightweight algorithms for data encryption, authentication mechanisms for privacy protection, and secure routing for data relays to protect the entire network against passive and active attacks, and denials of external service providers [55,56,57]. Adaptability.…”

Section: Overview Of Wsnsmentioning

confidence: 99%

Big Data Collection in Large-Scale Wireless Sensor Networks

Djedouboum

Ari

Guéroui

et al. 2018

Sensors

105

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Data collection is one of the main operations performed in Wireless Sensor Networks (WSNs). Even if several interesting approaches on data collection have been proposed during the last decade, it remains a research focus in full swing with a number of important challenges. Indeed, the continuous reduction in sensor size and cost, the variety of sensors available on the market, and the tremendous advances in wireless communication technology have potentially broadened the impact of WSNs. The range of application of WSNs now extends from health to the military field through home automation, environmental monitoring and tracking, as well as other areas of human activity. Moreover, the expansion of the Internet of Things (IoT) has resulted in an important amount of heterogeneous data that are produced at an exponential rate. Furthermore, these data are of interest to both industry and in research. This fact makes their collection and analysis imperative for many purposes. In view of the characteristics of these data, we believe that very large-scale and heterogeneous WSNs can be very useful for collecting and processing these Big Data. However, the scaling up of WSNs presents several challenges that are of interest in both network architecture to be proposed, and the design of data-routing protocols. This paper reviews the background and state of the art of Big Data collection in Large-Scale WSNs (LS-WSNs), compares and discusses on challenges of Big Data collection in LS-WSNs, and proposes possible directions for the future.

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“…In addition, the initial trust value was set to 0.99 in our experiment because we used very high trust thresholds such as 0.9, and thus if the initial trust value was as low as 0.5, then most nodes would be eliminated soon after the simulation starts. For this reason, high initial trust values have been used in experiments in many studies in the literature [35,36]. We explain each experiment in detail in Section 4.2.…”

Section: • Attack Modelsmentioning

confidence: 99%

An Enhanced Trust Mechanism with Consensus-Based False Information Filtering Algorithm against Bad-Mouthing Attacks and False-Praise Attacks in WSNs

Suh

Cho

2019

Electronics

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To defend against insider attacks in wireless sensor networks (WSNs), trust mechanisms (TMs) using the notion of trust in human society have been proposed and are still actively researched. In the WSN with a trust mechanism (TM), each sensor node evaluates the trustworthiness of its neighbor sensors based on their behaviors, for example packet forwarding, and collaborates only with trustworthy neighbors while removing untrustworthy neighbor from its neighbor list. The reputation system (RS) is an advanced type of trust mechanism that evaluates the trustworthiness of a node by additionally considering neighbor nodes’ observations or evaluations about it. However, intelligent inside attackers in WSNs can discover the security vulnerabilities of trust mechanisms by examining the operations of TM (or RS), because the software modules of the TM (or RS) are installed and operating in their local storage and memory, and thus, they can avoid detection by the trust mechanisms. Bad-mouthing attacks and false-praise attacks are well-known examples of such intelligent insider attacks. We observed that existing trust mechanisms do not have effective countermeasures to defend against such attacks. In this paper, we propose an enhanced trust mechanism with a consensus-based false information filtering algorithm (TM-CFIFA) that can effectively defend against bad-mouthing attacks and false-praise attacks. According to our experiment results, compared with an existing representative RS model, our TM-CFIFA shortened the detection time of a packet drop attacker, which is supported by a false-praise attacker by at least 83%, and also extended the lifetime of a victim sensor node that is under bad-mouthing attacks by at least 15.8%.

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On-Off Attacks Mitigation against Trust Systems in Wireless Sensor Networks

Cited by 25 publications

References 17 publications

BTDS: Bayesian‐based trust decision scheme for intelligent connected vehicles in VANETs

BTDS: Bayesian‐based trust decision scheme for intelligent connected vehicles in VANETs

Big Data Collection in Large-Scale Wireless Sensor Networks

An Enhanced Trust Mechanism with Consensus-Based False Information Filtering Algorithm against Bad-Mouthing Attacks and False-Praise Attacks in WSNs

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