Taylor Sailfish Optimizer-Based Deep Stacked Auto Encoder for Blackhole Attack Detection in Wireless Sensor Network
DOI:
https://doi.org/10.13052/jwe1540-9589.21316Keywords:
Blackhole attack, Taylor series, Deep stacked autoencoder, SailFish Optimizer, Routing.Abstract
Sensor nodes in Wireless sensor network (WSN) are distributed over a large area for sensing the pressure, temperature, humidity, and so on. They are at risk due to several attacks. In an attack like a black hole, the malicious node captures the whole data without any consideration of the active route, thus the source node are secured for communication. Hence, a new method name, Taylor SailFish Optimizer (TaylorSFO) is proposed to predict blackhole attacks in WSN. The training of the Deep stacked autoencoder is done through proposed Taylor-SFO, which is the integration of Taylor Series, and SailFish Optimizer (SFO). The newly developed Taylor-SFO is then applied for routing and blackhole attack detection at the WSN base station. Overall, two phases are included in the proposed model, which involves routing and blackhole attack detection at the base station. Initially, the WSN nodes are given to the routing module. Here, the routing is done based on the proposed TaylorSFO. Energy, distance as well as delay are the three fitness parameters considered for the routing. The proposed method shows the lowest delay of 21.23 ms, minimal FNR of 0.083, minimal FPR of 0.134, highest PDR of 94.87%, the highest throughput rate of 119.98 kbps, respectively.
Downloads
References
J. Sebastan Terence and Geethanjali Purushothaman, “A novel technique to detect malicious packet dropping attacks in wireless sensor networks”, Journal of Information Processing Systems, vol. 15, no. 1, 2019.
Abdullah Aljumah, Tariq Ahamed Ahanger, “Futuristic Method to Detect and Prevent Blackhole Attack in Wireless Sensor Networks”, IJCSNS International Journal of Computer Science and Network Security, vol. 17, no. 2, February 2017.
Hanane Kalkha, Hassan Satori, and Khalid Satori, “Preventing Black Hole Attack in Wireless Sensor Network Using HMM”, Procedia computer science, vol. 148, pp. 552–561, 2019.
Deepak C. Mehetre, S. Emalda Roslin, and Sanjeev J. Wagh, “Detection and prevention of black hole and selective forwarding attack in clustered WSN with Active Trust”, Cluster Computing, vol. 22, no. 1, pp. 1313–1328, 2019.
A. John Clement Sunder, and A. Shanmugam, “Jensen–Shannon Divergence Based Independent Component Analysis to Detect and Prevent Black Hole Attacks in Healthcare WSN”, Wireless Personal Communications, vol. 107, no. 4, pp. 1607–1623, 2019.
Anastasia Tsiota, Dionysis Xenakis, Nikos Passas, and Lazaros Merakos, “On Jamming and Black Hole Attacks in Heterogeneous Wireless Networks”, IEEE Transactions on Vehicular Technology, vol. 68, no. 11, pp. 10761–10774, 2019.
M. Rajesh Babu, S. Moses Dian, Siva Chelladurai, and Mathiyalagan Palaniappan, “Proactive Alleviation Procedure to Handle Black Hole Attack and Its Version”, The Scientific World Journal, 2015.
Bindu Rani, Harkesh Sehrawat, and Vikas Siwach, “Blackhole attack in wireless sensor network (WSN) using AODV protocol”, International Journal of Advanced Science and Technology, vol. 29, no. 4, pp. 349–359, 2020.
M Moshaddique Al Ameen & Jingwei Liu and Kyungsup Kwak, “Security and Privacy Issues in Wireless Sensor Networks for Healthcare Applications,” Journal of medical systems, vol. 36, no. 1, pp. 93–101, 2012.
R. Beghdad and A. Lamraoui, “Boundary and holes recognition in wireless sensor networks,” Journal of Innovation in Digital Ecosystems, vol. 3, no. 1, pp. 1–14, 2016.
Vrinda Gupta, and Rajoo Pandey, “An improved energy aware distributed unequal clustering protocol for heterogeneous wireless sensor networks,” An International Journal of Engineering Science and Technology, vol. 19, no. 2, pp. 1050–1058, 2016.
Gurjot Singh and Jagdeep Singh, “Prevention of Blackhole Attack in Wireless Sensor Network using IPSec Protocol, “International Journal of Advanced Research in Computer Science, vol. 4, no. 11, December 2013.
Abhijeet Salunke and Dayanand Ambawade, “Dynamic Sequence Number Thresholding Protocol for Detection of Blackhole attack in Wireless Sensor Network”, In proceedings of 2015 International Conference on Communication, Information and Computing Technology (ICCICT), Mumbai, India, January 2015.
Anu Bala, Munish Bansal, and Jagpreet Singh, “Performance Analysis of MANET under Blackhole Attack”, In proceedings of First International Conference on Networks & Communications, 2009.
Abhishek Pandey and R.C. Tripathi, “A Survey on Wireless Sensor Networks Security”, International Journal of Computer Applications, vol. 3, pp. 43–49, June 2010.
Jeremy Brown and Xiaojiang Du, “Detection of selective forwarding attacks in heterogeneous sensor networks”, In proceedings of IEEE International Conference on Communications, pp. 1583–1587, 2008.
Christoforos Panos, Chirstoforos Ntantogian, Stefanos Malliaros, Christos Xenakis, “Analyzing, Quantifying, and Detecting the Blackhole attack in Infrastructure-less Networks”, Computer Networks, vol. 113, pp. 94–110, 2017.
Mete Ozay, Iñaki Esnaola, Fatos Tunay Yarman Vural, Sanjeev R. Kulkarni, and H. Vincent Poor, “Machine Learning Methods for Attack Detection in the Smart Grid”, IEEE transactions on neural networks and learning systems, vol. 27, no. 8, pp. 1773–1786, 2016.
EsraSahin and IlkerHamzaoglu, “An Efficient Intra Prediction Hardware Architecture for H.264 Video Decoding”, In proceedings of 10th Euromicro Conference on Digital System Design Architectures, Methods and Tools, IEEE, pp. 448–454, August 2007.
S. Shadravan, H.R. Naji, and V.K. Bardsiri, “The Sailfish Optimizer: A novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems”, Engineering Applications of Artificial Intelligence, vol. 80, pp. 20–34, 2019.
Guifang Liu, HuaiqianBao, and Baokun Han, “A Stacked Autoencoder-Based Deep Neural Network for Achieving Gearbox Fault Diagnosis”, Mathematical Problems in Engineering, 2018.
KDD Cup 1999 dataset taken from, “http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html”, accessed on July 2020.
Balachandra M., Prema K.V. and Makkithaya K., “Multiconstrained and multipath QoS aware routing protocol for MANETs”, Wireless networks, vol. 20, no. 8, pp. 2395–2408, 2014.
Yadav A.K. and Tripathi S, “QMRPRNS: Design of QoS multicast routing protocol using reliable node selection scheme for MANETs”, Peer-to-Peer Networking and Applications, vol. 10, no. 4, pp. 897–909, 2017.
Jayapriya, K. and Mary, N.A.B., “Employing a novel 2-gram subgroup intra pattern (2GSIP) with stacked auto encoder for membrane protein classification”, Molecular biology reports, vol. 46, no. 2, pp. 2259–2272, 2019.
Deepali Virmani, and Pranav Gupta, “Adaptive Exponential Trust-Based Algorithm in Wireless Sensor Network to Detect Black Hole and Gray Hole Attacks,” Emerging Research in Computing, Information, Communication and Applications, pp. 65–73, 2016.
Venkata Abhishek Kanthuru, and Kakelli Anil Kumar, “Black Hole Detection and Mitigation Using Active Trust in Wireless Sensor Networks,” Advances in Distributed Computing and Machine Learning, vol. 127, pp. 25–34, 2020.
Ila Kaushik, and Nikhil Sharma, “Black Hole Attack and Its Security Measure in Wireless Sensors Networks,” Handbook of Wireless Sensor Networks: Issues and Challenges in Current Scenario’s, vol. 1132, pp. 401–416, 2020.
Dhananjay Bisen, Bhavana Barmaiya, Ritu Prasad, Praneet Saurabh, “Detection and Prevention of Black Hole Attack Using Trusted and Secure Routing in Wireless Sensor Network,” Advances in Intelligent Systems and Computing, vol. 1179, 2020.
Vahid Heydari, and Seong-Moo Yoo, “Timeout Period Analysis to Detect Black Hole Attack in Multihop Wireless Ad Hoc Networks,” International Journal of Wireless Information Networks, vol. 25, pp. 15–29, 2018.
Ashish Chaturvedi M. Senthil Kumar, “Energy-Efficient Coverage and Prolongs for Network Lifetime of WSN using MCP,” European Journal of Scientific Research (EJSR), vol. 95, no. 2, 2013.
M. Senthil Kumar, “Energy Efficient Techniques for Transmission of Data in Wireless Sensor Networks,” Journal of Computing Technologies (JCT), vol. 5, no. 2, 2016.
Avishek Choudhury, Onur Asan, “Role of artificial intelligence in patient safety outcomes: systematic literature review,” JMIR medical informatics, vol. 8, no. 7, pp. e18599, 2020.
Suresh Babu Chandanapalli,Sreenivasa Reddy E,Rajya Lakshmi D, “Convolutional Neural Network for Water Quality Prediction in WSN”, Journal of Networking and Communication Systems, vol. 2, no. 3, pp. 40–47, 2019.
Samar Abdulrahman Juma Al Raisi, “A Review on Congestion Management Methodologies and its Applications”, Journal of Computational Mechanics, Power System and Control, vol. 3, no. 3, 2020.
K.Srinivas, “Cluster Based Dense using Hybrid Genetic and Grasshopper Optimization algorithm in WSN”, Journal of Networking and Communication Systems, vol. 4, no. 3, 2021.
