Cluster based Detection and Reduction Techniques to Identify Wormhole Attacks in Underwater Wireless Sensor Networks

Murgod, Tejaswini R.; Meenakshi, S.

doi:10.14569/ijacsa.2020.0110708

Cited by 3 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the scheme does not consider the influence of an underwater acoustic channel on detection results. A detection technique for identifying underwater wormhole attacks is proposed in [ 17 ]. The round-trip time of a wormhole attack is shorter than that of normal links, which can be used to judge the existence of wormhole links.…”

Section: Related Workmentioning

confidence: 99%

DOIDS: An Intrusion Detection Scheme Based on DBSCAN for Opportunistic Routing in Underwater Wireless Sensor Networks

Zhang

Wang

et al. 2023

Sensors

View full text Add to dashboard Cite

In Underwater Wireless Sensor Networks (UWSNs), data should be transmitted to data centers reliably and efficiently. However, due to the harsh channel conditions, reliable data transmission is a challenge for large-scale UWSNs. Thus, opportunistic routing (OR) protocols with high reliability, strong robustness, low end-to-end delay, and high energy efficiency are widely applied. However, OR in UWSNs is vulnerable to routing attacks. For example, sinkhole attack nodes can attract traffic from surrounding nodes by forging information such as the distance to the sink node. In order to reduce the negative impact of malicious nodes on data transmission, we propose an intrusion detection scheme (IDS) based on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm for OR (DOIDS) in this paper. DOIDS is based on small-sample IDS and is suitable for UWSNs with sparse node deployment. In DOIDS, the local monitoring mechanism is adopted. Every node in the network running DOIDS can select the trusted next hop. Firstly, according to the behavior characteristics of common routing attack nodes and unreliable underwater acoustic channel characteristics, DOIDS selected the energy consumption, forwarding, and link quality information of candidate nodes as the detection feature values. Then, the collected feature information is used to detect potential abnormal nodes through the DBSCAN clustering algorithm. Finally, a decision function is defined according to the time decay function to reduce the false detection rate of DOIDS. It makes a final judgment on whether the potential abnormal node is malicious. The simulation results show that the algorithm can effectively improve the detection accuracy rate (3% to 15% for different scenarios) and reduce the false positive rate, respectively.

show abstract

Section: Related Workmentioning

confidence: 99%

DOIDS: An Intrusion Detection Scheme Based on DBSCAN for Opportunistic Routing in Underwater Wireless Sensor Networks

Zhang

Wang

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Murgod et al [18] presented cluster based wormhole attacks detection and reduction technique in UWSNs environments. Energy Efficient Hybrid Optical-Acoustic Cluster Based Routing (EEHRC) Protocol was used to detect the presence of wormhole attack and prevent the UWSN from the attack.…”

Section: Classical Approachesmentioning

confidence: 99%

Secured Route Selection Using E-ACO in Underwater Wireless Sensor Networks

Deepak¹,

Mukeshkrishnan²

2022

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

Adaptive convolutional-LSTM neural network with NADAM optimization for intrusion detection in underwater IoT wireless sensor networks

S Arivumani,

2024

Eng. Res. Express

View full text Add to dashboard Cite

Underwater IoT networks rely on sensor nodes to continuously monitor and collect real-time data from marine environments. The nodes in the underwater networks face security challenges in terms of intrusions. Intrusions are common in conventional wireless sensor networks as well as in underwater sensor networks. Intrusions like data tampering, node insertion, unauthorized access compromises the security and privacy of the network. The existing Intrusion Detection Systems (IDS) faces limitations while detecting intrusions in Underwater IoT Wireless Sensor Networks (UIoTWSN) due to the dynamic condition of underwater environment. To overcome this, a novel IDS for UIoTWSN is proposed by combining the features of advanced deep learning techniques in an optimized manner. The proposed hybrid model comprises convolutional LSTM network with NADAM optimizer to analyze the spatial and temporal features to detect the intrusions. To handle the dynamic nature of underwater sensor network and to improve the convergence speed of the proposed IDS, NADAM optimizer is integrated in this research work. Experimentations of the proposed model validates the higher accuracy of 96.7%, precision of 94.5% and recall of 95.2% which is better than the conventional techniques.

show abstract

Cluster based Detection and Reduction Techniques to Identify Wormhole Attacks in Underwater Wireless Sensor Networks

Cited by 3 publications

References 10 publications

DOIDS: An Intrusion Detection Scheme Based on DBSCAN for Opportunistic Routing in Underwater Wireless Sensor Networks

DOIDS: An Intrusion Detection Scheme Based on DBSCAN for Opportunistic Routing in Underwater Wireless Sensor Networks

Secured Route Selection Using E-ACO in Underwater Wireless Sensor Networks

Adaptive convolutional-LSTM neural network with NADAM optimization for intrusion detection in underwater IoT wireless sensor networks

Contact Info

Product

Resources

About