Identifying Malicious Nodes in Multihop IoT Networks Using Diversity and Unsupervised Learning

Liu, Xin; Abdelhakim, Mai; Krishnamurthy, P.; Tipper, David

doi:10.1109/icc.2018.8422484

Cited by 35 publications

(37 citation statements)

References 7 publications

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“…-We use the K-Means method to cluster nodes into benign group and malicious group. For the performance analysis, we compare our approach with two similar approaches: Perceptron Detection with Enhancement (PDE) [8] and Hard Detection (HD) [9]. The experimental results demonstrate that DCONST could achieve better detection performance.…”

Section: Introductionmentioning

confidence: 99%

DCONST: Detection of Multiple-Mix-Attack Malicious Nodes Using Consensus-Based Trust in IoT Networks

Liu

Meng

2020

Information Security and Privacy

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Section: Introductionmentioning

confidence: 99%

DCONST: Detection of Multiple-Mix-Attack Malicious Nodes Using Consensus-Based Trust in IoT Networks

Liu

Meng

2020

Information Security and Privacy

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“…This is a clear indication of how following a proper SE process could affect the performance of the IDS in WSN. Moreover, conclusions were drawn in regard to applying software engineering processes to IDSs to deliver the required functionalities, with respect to operational constraints, with an improved performance, accuracy and reliability.Security concerns have become particularly acute in systems deployed over wireless networks [7][8][9][10][11][12]. In recent years, advances in micro-electronic systems technology, digital electronics, and wireless communications that have enabled the development of Wireless Sensor Networks (WSNs) [6,13] have been witnessed.…”

mentioning

confidence: 99%

“…However, hostile environments are usually unattended. Due to that, WSNs lack physical protection (e.g., no switches or gateways to monitor the flow of information) resulting a potential of node compromising as well as low network security and protection [10,15,19]. Therefore, it is important to secure such networks from intrusions and attacks, especially in applications where security services are important.…”

mentioning

confidence: 99%

“…Packet-dropping attack drops the received data packets or control messages instead of forwarding them to other nodes, disrupting the normal operation of the network.Security attacks need to be conveniently addressed by detecting and preventing such malicious behaviors. WSN security has drawn the attention of many researchers [7,[9][10][11]. To address and overcome the security issue which is considered one of the main challenges in WSNs, many security solutions were developed including software-based network Intrusion Detection Systems (IDSs) [3,6,9,12].…”

mentioning

confidence: 99%

“…Security concerns have become particularly acute in systems deployed over wireless networks [7][8][9][10][11][12]. In recent years, advances in micro-electronic systems technology, digital electronics, and wireless communications that have enabled the development of Wireless Sensor Networks (WSNs) [6,13] have been witnessed.…”

mentioning

confidence: 99%

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Integrating Software Engineering Processes in the Development of Efficient Intrusion Detection Systems in Wireless Sensor Networks

Almomani

AlRomi²

2020

Sensors

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Applying Software Engineering (SE) processes is vital to critical and complex systems including security and networking systems. Nowadays, Wireless Sensor Networks (WSNs) and their applications are found in many military and civilian systems which make them attractive to security attackers. The increasing risks and system vulnerabilities of WSNs have encouraged researchers and developers to propose many security solutions including software-based Intrusion Detection Systems (IDSs). The main drawbacks of current IDSs are due to the lack of clear, structured software development processes. Unfortunately, a substantial gap has been observed between WSN and SE research communities. Integrating SE and WSNs is an emerging topic that will be expanded as technology evolves and spreads in all life aspects. Consequently, this paper highlighted the importance of Requirement Engineering, Software Design, and Testing when developing IDSs for WSNs. Three software IDS designs were proposed in this study: Scheduling, Broadcast, and Watchdog designs. The three designs were compared in terms of consumed energy and network lifetime. Although the same IDS approach was used, but, by highlighting the design phase and implementing different designs, the network lifetime was increased by 73.6% and the consumed energy was reduced by 20% in some of the designs. This is a clear indication of how following a proper SE process could affect the performance of the IDS in WSN. Moreover, conclusions were drawn in regard to applying software engineering processes to IDSs to deliver the required functionalities, with respect to operational constraints, with an improved performance, accuracy and reliability.

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The Reputation Lag Attack

Sirur¹,

Müller²

2019

IFIP Advances in Information and Communication Technology

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Reputation systems and distributed networks are increasingly common. Examples are electronic marketplaces, IoT and ad-hoc networks. The propagation of information through such networks may suffer delays due to, e.g., network connectivity, slow reporting and ratingupdate delays. It is known that these delays enable an attack called the reputation lag attack. There is evidence of impact of reputation lag attacks on existing trust system proposals. There has not been in-depth formal analysis of the reputation lag attack. Here, we present a formal model capturing the core properties of the attack: firstly, the reputation of an actor failing to reflect their behaviour due to lag and, secondly, a malicious actor exploiting this for their personal gain. This model is then used to prove three key properties of the system and the attacker: if there is no decay of reputation, then the worst-case attacker behaviour is to cooperate initially, then wait, then behave badly; increasing communication between users was found to always be of benefit to the users; performing a specified number of negative interactions given any instance of the system is an NP-hard problem.

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Identifying Malicious Nodes in Multihop IoT Networks Using Diversity and Unsupervised Learning

Cited by 35 publications

References 7 publications

DCONST: Detection of Multiple-Mix-Attack Malicious Nodes Using Consensus-Based Trust in IoT Networks

DCONST: Detection of Multiple-Mix-Attack Malicious Nodes Using Consensus-Based Trust in IoT Networks

Integrating Software Engineering Processes in the Development of Efficient Intrusion Detection Systems in Wireless Sensor Networks

The Reputation Lag Attack

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