A Protocol Layer Trust-Based Intrusion Detection Scheme for Wireless Sensor Networks

Wang, Jian; Jiang, Shuai; Fapojuwo, Abraham O.

doi:10.3390/s17061227

Cited by 31 publications

(35 citation statements)

References 38 publications

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“…WSN is widely used in many applications such as industrial monitoring, environmental monitoring, forest monitoring, health care, and military. e architecture of WSN is categorized into clustered and flat [1]. In the flat architecture, the sensor nodes (SNs) communicate with the base station (BS) directly or by relaying the data through other nodes.…”

Section: Introductionmentioning

confidence: 99%

“…ese signals jam the network, and a genuine node denies to give services because it is busy in receiving the signals. e codes in the SN can also be modified, and the node can be tampered or replaced with an untrusted node [1,7]. At the MAC layer [10,11], the attacker disrupts the network availability by obtaining unfair channel priority, collisions, etc.…”

Section: Introductionmentioning

confidence: 99%

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LB-IDS: Securing Wireless Sensor Network Using Protocol Layer Trust-Based Intrusion Detection System

Ghugar

Pradhan

Bhoi

et al. 2019

Journal of Computer Networks and Communications

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Wireless sensor network (WSN) faces severe security problems due to wireless communication between the nodes and open deployment of the nodes. The attacker disrupts the security parameters by launching attacks at different layers of the WSN. In this paper, a protocol layer trust-based intrusion detection system (LB-IDS) is proposed to secure the WSN by detecting the attackers at different layers. The trust value of a sensor node is calculated using the deviation of trust metrics at each layer with respect to the attacks. Mainly, we consider trustworthiness in the three layers such as physical layer trust, media access control (MAC) layer trust, and network layer trust. The trust of a sensor node at a particular layer is calculated by taking key trust metrics of that layer. Finally, the overall trust value of the sensor node is estimated by combining the individual trust values of each layer. By applying the trust threshold, a sensor node is detected as trusted or malicious. The performance of LB-IDS is evaluated by comparing the results of the three performance parameters such as detection accuracy, false-positive rate, and false-negative rate, with the results of Wang’s scheme. We have implemented jamming attack at the physical layer, back-off manipulation attack at the MAC layer, and sinkhole attack at the network layer using simulations. We have also implemented a cross-layer attack using the simulation where an attacker simultaneously attacks the MAC layer and network layer. Simulation results show that the proposed LB-IDS performs better as compared with Wang’s scheme.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

LB-IDS: Securing Wireless Sensor Network Using Protocol Layer Trust-Based Intrusion Detection System

Ghugar

Pradhan

Bhoi

et al. 2019

Journal of Computer Networks and Communications

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“…Furthermore, unlike traditional wireless networks, WSNs have very limited computation, communication, power, and memory resources. Therefore, lightweight, energy‐efficient security schemes with minimum bandwidth overhead are required to protect WSNs from malicious attacks …”

Section: Introductionmentioning

confidence: 99%

An efficient weighted trust‐based malicious node detection scheme for wireless sensor networks

Zawaideh¹,

Salamah

2018

Int J Communication

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The aim of wireless sensor networks (WSNs) is to gather sensor data from a monitored environment. However, the collected or reported information might be falsified by faults or malicious nodes. Hence, identifying malicious nodes in an effective and timely manner is essential for the network to function properly and reliably. Maliciously behaving nodes are usually detected and isolated by reputation and trust-based schemes before they can damage the network. In this paper, we propose an efficient weighted trust-based malicious node detection (WT-MND) scheme that can detect malicious nodes in a clustered WSN.The node behaviors are realistically treated by accounting for false-positive and false-negative instances. The simulation results confirm the timely identification and isolation of maliciously behaving nodes by the WT-MND scheme. The effectiveness of the proposed scheme is afforded by the adaptive trustupdate process, which implicitly performs trust recovery of temporarily malfunctioning nodes and computes a different trust-update factor for each node depending on its behavior. The proposed scheme is more effective and scalable than the related schemes in the literature, as evidenced by its higher detection ratio (DR) and lower misdetection ratio (MDR), which only slightly vary with the network's size. Moreover, the scheme sustains its efficient characteristics without significant power consumption overheads.

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“…The popularity of TRMs in ensuring security in WSN is due to the heavy computations associated with cryptography‐based protocols that cannot be supported by the resource constrained network . Numerous schemes have been proposed with the aim of aiding sensor nodes to better establish excellent trust and reputation among themselves . Most of the solutions for several complicated problems such as security have been attempted using engineering approaches.…”

Section: Introductionmentioning

confidence: 99%

The adoption of socio‐ and bio‐inspired algorithms for trust models in wireless sensor networks: A survey

Nunoo‐Mensah

Boateng

Gadze

2017

Int J Communication

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The security of a wireless sensor network is greatly increased with increasing levels of trustworthiness of nodes on the network. There are a number of trust models proposed that use various trust evaluation methods. The paper categorises trust evaluation methods for wireless sensor networks into socio-inspired, bio-inspired, and analytical methods. Biologically inspired methods and socio constructs are rarely used in the design of trust schemes as opposed to analytical methods (probability, agent based, fuzzy, etc). The paper discusses existing bio-and socio-inspired-based trust schemes implemented for wireless sensor networks, viz quality-based distance vector protocol, enhanced bio-inspired trust and reputation model for wireless sensor network, bio-inspired trust routing protocol, machine learning-based bio-inspired trust and reputation model, socio-psychological trust and reputation model, and finally reputation framework for sensor network. The contributions, trust evaluation methods and limitations of recent trust models (i.e. analytical based, biological and socio-inspired models) are discussed in this paper. The pros and cons of analytical, socio, and biologically inspired algorithms for computing are outlined to easily ascertain the relevance of adopting either biological or socio-inspired approaches for solving trust-related problems in wireless sensor networks. KEYWORDSbio-inspired algorithms, socio-inspired algorithms, trust models, wireless sensor network Int J Commun Syst. 2018;31:e3444.wileyonlinelibrary.com/journal/dac

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A Protocol Layer Trust-Based Intrusion Detection Scheme for Wireless Sensor Networks

Cited by 31 publications

References 38 publications

LB-IDS: Securing Wireless Sensor Network Using Protocol Layer Trust-Based Intrusion Detection System

LB-IDS: Securing Wireless Sensor Network Using Protocol Layer Trust-Based Intrusion Detection System

An efficient weighted trust‐based malicious node detection scheme for wireless sensor networks

The adoption of socio‐ and bio‐inspired algorithms for trust models in wireless sensor networks: A survey

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