Detecting Bad-Mouthing Attacks on Reputation Systems Using Self-Organizing Maps

Banković, Zorana; Vallejo, Juan Carlos; Fraga, David; Moya, José Manuel Huidobro

doi:10.1007/978-3-642-21323-6_2

Cited by 22 publications

(12 citation statements)

References 4 publications

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“…These fabricated fake recommendations aim to ruin the reputation of the target entity. As a result, the system isolates the victim entity or decreases its opportunity of being chosen as a service-provider (Banković, Vallejo, Fraga, & Moya, 2011). The malicious entities remain to provide appropriate recommendations for the other entities.…”

Section: Biased Recommendationsmentioning

confidence: 99%

Trust and reputation for Internet of Things: Fundamentals, taxonomy, and open research challenges

Ahmed

Hamid

Gani

et al. 2019

Journal of Network and Computer Applications

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Internet of things (IoT) is a contemporary technology, which links a tremendous number of devices with each other to ease the life via many useful services such as information exchange, monitoring, and control. IoT comprises different types of entities such as sensors and RFID tags, which mostly deployed in unattended, sensitive, and hostile territories. Due to rapid scalability and high heterogeneity, traditional security approaches fails to provide adequate security mechanisms for the current IoT infrastructure. The possibility of insecure and unattended deployment make some of IoT's entities subject to be captured physically by the attackers. As a result, the victim device can be exploited as a gateway to compromise the entire network. Furthermore, an entity may not work correctly because of resources constraints or instability of network's link. Recently, trust and reputation (TR) extended in IoT to monitor the behaviors deviation of IoT entities. Many TR models introduced, to incorporate the trust concepts in IoT as a new security paradigm. In this study, we provide thematic taxonomy for trust in IoT, considering several issues such as understanding of trust entity roles, trust properties, trust applications, levels of trust management, trust metrics, trust computation schemes and attacks on TR. Finally, the survey presents advances and open research challenges in the IoT's trust.

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Section: Biased Recommendationsmentioning

confidence: 99%

Trust and reputation for Internet of Things: Fundamentals, taxonomy, and open research challenges

Ahmed

Hamid

Gani

et al. 2019

Journal of Network and Computer Applications

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“…LT construction is given in algorithm I in section 4.3. We assume that the network is free from bad mouthing attack [22], so the compromised node cannot manipulate LT values and compromised nodes do not work in cooperative fashion [23].…”

Section: Network Model and System Assumptionmentioning

confidence: 99%

Behavior based Routing Misbehavior Detection in Wireless Sensor Networks

Terence¹,

Geethanjali²

2019

KSII TIIS

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Sensor networks are deployed in unheeded environment to monitor the situation. In view of the unheeded environment and by the nature of their communication channel sensor nodes are vulnerable to various attacks most commonly malicious packet dropping attacks namely blackhole, grayhole attack and sinkhole attack. In each of these attacks, the attackers capture the sensor nodes to inject fake details, to deceive other sensor nodes and to interrupt the network traffic by packet dropping. In all such attacks, the compromised node advertises itself with fake routing facts to draw its neighbor traffic and to plunge the data packets. False routing advertisement play vital role in deceiving genuine node in network. In this paper, behavior based routing misbehavior detection (BRMD) is designed in wireless sensor networks to detect false advertiser node in the network. Herein the sensor nodes are monitored by its neighbor. The node which attracts more neighbor traffic by fake routing advertisement and involves the malicious activities such as packet dropping, selective packet dropping and tampering data are detected by its various behaviors and isolated from the network. To estimate the effectiveness of the proposed technique, Network Simulator 2.34 is used. In addition packet delivery ratio, throughput and end-to-end delay of BRMD are compared with other existing routing protocols and as a consequence it is shown that BRMD performs better. The outcome also demonstrates that BRMD yields lesser false positive (less than 6%) and false negative (less than 4%) encountered in various attack detection.

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“…In Sections 4.2 and 4.3, we present results of our various simulation scenarios, respectively. We also consider the algorithm proposed in [ 21 ], which detected inaccurate noise data, and the mechanism proposed in [ 27 ], which can be against bad mouthing attack to the reputation system. We take these algorithms for comparison to evaluate the performance of ARM in Section 4.4.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…In Scenario D, the numbers of abnormal and normal participants are in equivalent. Additionally, we draw inspiration from the bad mouthing attack [ 27 ] and assume that the malicious participants will collude with each other to reduce the reputation of normal participants.…”

Section: Performance Evaluationmentioning

confidence: 99%

See 1 more Smart Citation

Improving Data Quality with an Accumulated Reputation Model in Participatory Sensing Systems

Wang

et al. 2014

Sensors

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The ubiquity of mobile devices brings forth a sensing paradigm, participatory sensing, to collect and interpret sensory information from the environment. Participants join in multifarious sensing tasks and share their data. The sensing result can be obtained in light of shared data. It is not uncommon that some corrupted data is provided by participants, which makes sensing result unreliable accordingly. To address this nontrivial issue, we proposed the accumulated reputation model (ARM) to improve the accuracy of the sensing result. In ARM, participants' reputation will be computed and accumulated based on their sensing data. The sensing data from reputable participants make higher contributions to the sensing result. ARM performs well on calculating accurate sensing results, even in extreme scenarios, where there are many inexperienced or malicious participants.

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Detecting Bad-Mouthing Attacks on Reputation Systems Using Self-Organizing Maps

Cited by 22 publications

References 4 publications

Trust and reputation for Internet of Things: Fundamentals, taxonomy, and open research challenges

Trust and reputation for Internet of Things: Fundamentals, taxonomy, and open research challenges

Behavior based Routing Misbehavior Detection in Wireless Sensor Networks

Improving Data Quality with an Accumulated Reputation Model in Participatory Sensing Systems

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