Vol.66, No.1, 2021, pp.903-917, doi:10.32604/cmc.2020.012255
Robust Hybrid Artificial Fish Swarm Simulated Annealing Optimization Algorithm for Secured Free Scale Networks against Malicious Attacks
  • Ganeshan Keerthana1,*, Panneerselvam Anandan2, Nandhagopal Nachimuthu3
1 Anna University, Chennai, 600025, India
2 Department of ECE, C. Abdul Hakeem College of Engineering and Technology, Vellore, India
3 Department of ECE, Excel Engineering College, Salem, India
* Corresponding Author: Ganeshan Keerthana. Email: gkeerthu21@gmail.com
Received 22 June 2020; Accepted 10 September 2020; Issue published 30 October 2020
Due to the recent proliferation of cyber-attacks, highly robust wireless sensor networks (WSN) become a critical issue as they survive node failures. Scale-free WSN is essential because they endure random attacks effectively. But they are susceptible to malicious attacks, which mainly targets particular significant nodes. Therefore, the robustness of the network becomes important for ensuring the network security. This paper presents a Robust Hybrid Artificial Fish Swarm Simulated Annealing Optimization (RHAFS-SA) Algorithm. It is introduced for improving the robust nature of free scale networks over malicious attacks (MA) with no change in degree distribution. The proposed RHAFS-SA is an enhanced version of the Improved Artificial Fish Swarm algorithm (IAFSA) by the simulated annealing (SA) algorithm. The proposed RHAFS-SA algorithm eliminates the IAFSA from unforeseen vibration and speeds up the convergence rate. For experimentation, free scale networks are produced by the Barabási– Albert (BA) model, and real-world networks are employed for testing the outcome on both synthetic-free scale and real-world networks. The experimental results exhibited that the RHAFS-SA model is superior to other models interms of diverse aspects.
Free scale networks; robustness; malicious attacks; fish swarm algorithm
Cite This Article
G. Keerthana, P. Anandan and N. Nachimuthu, "Robust hybrid artificial fish swarm simulated annealing optimization algorithm for secured free scale networks against malicious attacks," Computers, Materials & Continua, vol. 66, no.1, pp. 903–917, 2021.
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