Black Hole Attack Detection in Healthcare Wireless Sensor Networks Using Independent Component Analysis Machine Learning Technique

Author(s): A. John Clement Sunder*, A. Shanmugam

Journal Name: Current Signal Transduction Therapy

Volume 15 , Issue 1 , 2020

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Graphical Abstract:


Background: Wireless Sensor Networks (WSNs) are self-configured infrastructure-less networks are comprising of a number of sensing devices used to monitor physical or environmental quantities such as temperature, sound, vibration, pressure, motion etc. They collectively transmit data through the network to a sink where it is observed and analyzed.

Materials and Methods: The major issues in WSN are interference, delay and attacks that degrade their performance due to their distributed nature and operation. Timely detection of attacks is imperative for various real time applications like healthcare, military etc. To improve the Black hole attack detection in WSN, Projected Independent Component Analysis (PICA) technique is proposed herewith, which detects black hole attack by analyzing collected physiological data from biomedical sensors.

Results: The PICA technique performs attack detection through Mutual information to measure the dependence in the joint distribution. The dependence among the nodes is identified based on the independent probability distribution functions and mutual probability function.

Conclusion: The black hole attack isolation is then performed through the distribution of the attack separation message. This supports to improve Packet Delivery Ratio (PDR) with minimum delay. The simulation is carried out based on parameters such as black hole attack detection rate (BHADR), Black Hole Attack Detection Time (BHADT), False Positive Rate (FPR), PDR and delay.

Keywords: Wireless Sensor Network, Healthcare, black hole attack detection, biomedical sensors, independent component analysis, Mutual information, attack isolation.

Ren J, Zhang Y, Zhang K, Shen X. Adaptive and channel-aware detection of selective forwarding attacks in wireless sensor networks. IEEE Trans Wirel Commun 2016; 15(5): 3718-31.
Maleh Y, Ezzati A, Qasmaoui Y, Mbida M. A global hybrid intrusion detection system for wireless sensor networks. Procedia Comput Sci 2015; 52: 1047-52.
Singh R, Singh J, Singh R. Fuzzy based advanced hybrid intrusion detection system to detect malicious nodes in wireless sensor networks. Wirel Commun Mob Comput 2017; 2017
Aljumah A, Ahanger TA. Futuristic method to detect and prevent blackhole attack in wireless sensor networks. Int J Comp Sci Net Sec (IJCSNS) 2017; 17(2): 194.
Ozay M, Esnaola I, Yarman Vural FT, Kulkarni SR, Poor HV. Machine learning methods for attack detection in the smart grid. IEEE Trans Neural Netw Learn Syst 2016; 27(8): 1773-86.
[] [PMID: 25807571]
Dorri A. An EDRI-based approach for detecting and eliminating cooperative black hole nodes in MANET. Wirel Netw 2017; 23(6): 1767-78.
Almomani I, Al-Kasasbeh B, Al-Akhras M. WSN-DS: A Dataset for intrusion detection systems in wireless sensor networks. J Sens 2016; 2016
Singh K, Aulakh GK. A novel approach to detect black hole attack in wireless sensor network. Int J Adv Res Eng & Technol 2014; 2(VI): 17-20.
Tripathi M, Gaur MS, Laxmi V. Comparing the impact of black hole and gray hole attack on LEACH in WSN. Procedia Comput Sci 2013; 19: 1101-7.
Li W, Yi P, Wu Y, Pan L, Li J. A new intrusion detection system based on KNN classification algorithm in wireless sensor network. J Electr Comput Eng 2014; 2014
Al Ameen M, Liu J, Kwak K. Security and privacy issues in wireless sensor networks for healthcare applications. J Med Syst 2012; 36(1): 93-101.
[] [PMID: 20703745]
Arya M, Raina EJPS. BFO based optimized positioning for black hole attack mitigation in WSN. Int J Eng Trends Technol. IJETT 2014; Vol. 14.
Panos C, Ntantogian C, Malliaros S, Xenakis C. Analyzing, quantifying, and detecting the blackhole attack in infrastructure-less networks. Comput Netw 2017; 113: 94-110.
Mathur A, Newe T, Rao M. Defence against black hole and selective forwarding attacks for medical WSNs in the IoT. Sensors (Basel) 2016; 16(1): 118.
[] [PMID: 26797620]
He D, Chan S, Tang S. A novel and lightweight system to secure wireless medical sensor networks. IEEE J Biomed Health Inform 2014; 18(1): 316-26.
[] [PMID: 24403430]
Sun X, Yan B, Zhang X, Rong C. An integrated intrusion detection model of cluster-based wireless sensor network. PLoS One 2015; 10(10)e0139513
[] [PMID: 26447696]
Xie M, Hu J, Han S, Chen HH. Scalable hypergrid k-NN-based online anomaly detection in wireless sensor networks. IEEE Trans Parallel Distrib Syst 2013; 24(8): 1661-70.
Shamshirband S, Patel A, Anuar NB, Kiah MLM, Abraham A. Cooperative game theoretic approach using fuzzy Q-learning for detecting and preventing intrusions in wireless sensor networks. Eng Appl Artif Intell 2014; 32: 228-41.
Pathak GR, Patil SH, Tryambake JS. Efficient and trust based black hole attack detection and prevention in WSN. Int J Comp Sci Busi Inform 2014; 14(2)
Sajjad SM, Bouk SH, Yousaf M. Neighbor node trust based intrusion detection system for wsn. Procedia Comput Sci 2015; 63: 183-8.
Wazid M, Katal A, Sachan RS, Goudar RH, Singh DP. Detection and prevention mechanism for blackhole attack in wireless sensor network. Communications and Signal Processing (ICCSP) 576-81.
Bharathi MV, Tanguturi RC, Jayakumar C, Selvamani K. Node capture attack in Wireless Sensor Network: A survey. IEEE International Conference on Computational Intelligence and Computing Research (ICCIC) 2012; 1-3.

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Article Details

Year: 2020
Published on: 31 July, 2020
Page: [56 - 64]
Pages: 9
DOI: 10.2174/1574362413666180705123733

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