Secure energy aware routing protocol for IEEE 802.15.4 wireless sensor networks

Purpose The purpose of this paper is to investigate the latest research related to secure routing protocols in Wireless Sensor Network (WSN) and propose a new approach that can achieve a higher security level compared to the existing one. One of the main security issues in WSNs is the security of routing protocols. A typical WSN consists of a large number of small size, low-power, low-cost sensor devices. These devices are very resource-constrained and usually use cheap short-range radios to communicate with each other in an ad hoc fashion thus, achieving security in these networks is a big challenge, which is open for research. Design/methodology/approach The route updates and data messages of the protocol are authenticated using Edwards-curves Digital Signature Algorithm (EdDSA). Routing protocols play an essential role in WSNs, they ensure the delivery of the sensed data from the remote sensor nodes to back-end systems via a data sink. Routing protocols depend on route updates received from neighboring nodes to determine the best path to the sink. Manipulating these updates by inserting rouge nodes in the network that advertise false updates can lead to a catastrophic impact on the compromised WSN performance. Findings As a result, a new secure energy-aware routing protocol (SEARP) is proposed, which uses security enhanced clustering algorithm and EdDSA to authenticate route advertisements and messages. A secure clustering algorithm is also used as part of the proposed protocol to conserve energy, prolong network lifetime and counteract wormhole attacks. Originality/value In this paper, a SEARP is proposed to address network layer security attacks in WSNs. A secure clustering algorithm is also used as part of the proposed protocol to conserve energy, prolong network lifetime and counteract wormhole attacks. A simulation has been carried out using Sensoria Simulator and the performance evaluation has been discussed.

Delta Ruled First Order Iterative Deep Neural Learning for Sybil and Wormhole Attacks Detection in Healthcare Wireless Sensor Network

Detection and isolation of Sybil and wormhole attack nodes in healthcare WSN is a significant problem to be resolved. Few research works have been designed to identify Sybil and wormhole attack nodes in the network. However, the detection performance of Sybil and wormhole attack nodes was not effectual as the false alarm rate was higher. In order to overcome such limitations, Delta Ruled First Order Iterative Deep Learning based Intrusion Detection (DRFOIDL-ID) Technique is proposed. The DRFOIDL-ID Technique includes two main phase namely attack detection and isolation. The DRFOIDL-ID Technique constructs Delta Ruled First Order Iterative Deep Learning in attack detection phase with aim of detecting the occurrence of Sybil and wormhole attacks in healthcare WSN. After detecting the attack nodes, DRFOIDL-ID Technique carried outs isolation process with the objective of increasing the routing performance. During the isolation phase, DRFOIDL-ID Technique keep always the identified Sybil and wormhole attack nodes through transmitting the isolation messages to all sensor nodes in healthcare WSN. Hence, DRFOIDL-ID Technique improves the routing performance with lower packet loss rate. The DRFOIDL-ID Technique conducts the simulation process using factors such as attack detection rate, attack detection time, false alarm rate and packet loss rate with respect to a diverse number of sensor nodes and data packets. The simulation result proves that the DRFOIDL-ID Technique is able to improve the attack detection rate and also reduces the attack detection time as compared to state-of-the-art works.

Detection and prevention of black-hole and wormhole attacks in wireless sensor network using optimized LSTM

Purpose This study aims to present a novel system for detection and prevention of black hole and wormhole attacks in wireless sensor network (WSN) based on deep learning model. Here, different phases are included such as assigning the nodes, data collection, detecting black hole and wormhole attacks and preventing black hole and wormhole attacks by optimal path communication. Initially, a set of nodes is assumed for carrying out the communication in WSN. Further, the black hole attacks are detected by the Bait process, and wormhole attacks are detected by the round trip time (RTT) validation process. The data collection procedure is done with the Bait and RTT validation process with attribute information. The gathered data attributes are given for the training in which long short-term memory (LSTM) is used that includes the attack details. This is used for attack detection process. Once they are detected, those attacks are removed from the network using the optimal path selection process. Here, the optimal shortest path is determined by the improvement in the whale optimization algorithm (WOA) that is called as fitness rate-based whale optimization algorithm (FR-WOA). This shortest path communication is carried out based on the multi-objective function using energy, distance, delay and packet delivery ratio as constraints. Design/methodology/approach This paper implements a detection and prevention of attacks model based on FR-WOA algorithm for the prevention of attacks in the WSNs. With this, this paper aims to accomplish the desired optimization of multi-objective functions. Findings From the analysis, it is found that the accuracy of the optimized LSTM is better than conventional LSTM. The energy consumption of the proposed FR-WOA with 35 nodes is 7.14% superior to WOA and FireFly, 5.7% superior to grey wolf optimization and 10.3% superior to particle swarm optimization. Originality/value This paper develops the FR-WOA with optimized LSTM detecting and preventing black hole and wormhole attacks from WSN. To the best of the authors’ knowledge, this is the first work that uses FR-WOA with optimized LSTM detecting and preventing black hole and wormhole attacks from WSN.

Wormhole Attacks in Wireless Sensor Networks (Wsn) & Internet of Things (IoT): A Review

In the current world people are using the sensing networks called IoT and WSN as the subset of IoT in various applications. The employment of these sensor networks is rapidly increasing. Due to the longer usage of these sensor networks security issues are eventually happening and has the possibility of developing the attacks in the network. In this review, focuses on wormhole attacks in wireless sensor network (WSN) and Internet of Things (IoT) creating a tunnels i.e., wormhole link in between source and the destination node in the network. The classification of wormhole attack in both WSN and IoT are presented based on the mode of attacker. The detection mechanisms of wormhole attack are specified in both WSN and IoT. It hypothesizes the detection strength is more in IoT than the WSN based on the analysis, the parameters of the detection algorithm that the WSN is about 20% while in IoT is 70%.