Deep Learning-based Attack Classification Schemes for Mobile Adhoc Networks

  • Gurpreet Singh, Ganpat Joshi

Abstract

In this paper, initially, we have modeled MANETs attack detection issues as a multi-class classification problem by considering target labels are as genuine, black hole attack. Many machine learning models are applied on the obtained dataset. Thereafter, a deep learning model is proposed and implemented to classify black hole attacker nodes. The routing technique implemented in this paper is Ad hoc On-Demand Distance Vector (AODV). Finally, comparisons are drawn among the proposed and the existing schemes by considering various performance metrics. Experimental results reveal that the proposed scheme outperforms the competitive schemes in terms of various performance metrics.

Published
2020-10-01
How to Cite
Gurpreet Singh, Ganpat Joshi. (2020). Deep Learning-based Attack Classification Schemes for Mobile Adhoc Networks. International Journal of Control and Automation, 13(4), 1292-1302. Retrieved from https://sersc.org/journals/index.php/IJCA/article/view/32612
Section
Articles