An ensemble deep learning-based cyber attack detection system using optimization strategy

Abstract

Background of the studyCyber attack stories become a routine in which new levels of intention are shown by the cyber attacker through sophisticated attacks on networks. The conventional approaches struggle to detect unknown malware and complex attacks. It does not secure the user's individual information. Hence, deep learning methods are used to effectively predict the attacks. Proposed modelThis paper implements an intelligent cyber security mechanism by employing effective mechanisms. Initially, the significant data for this approach is gathered from the benchmark data sources, and then they are given into the optimal feature chosen. Here, the parameters are optimally chosen with the aid of the Modified Puzzle Optimization Algorithm (MPOA) for the successive outcomes. Moreover, the optimally selected attributes are fed into the Ensemble Serial Cascaded Deep learning with Attention Mechanism (ESCDLAM), which is the integration of the 1 Dimensional Convolutional Neural Networks (1DCNN), Recurrent Neural Network (RNN), and Deep Temporal Convolutional Networks (DTCN). Here, the parameter optimization takes place to improve detection performance. DatasetThe experimentation is conducted through a diverse datasets which includes CICIDS 2017 Friday Noon DDoS 0beefa93–4, NSL-KDD-Dataset, and Kddcup99 for the effective experimental analysis for detecting the cyber attacks. Findings of the resultsThe results outcome of the developed model shows 97 % in terms of accuracy, recall, MCC, and specificity. Also, the precision of the developed model attains a value of 95. ConclusionThe outcomes of the approach are compared with the other pre-existing approaches to show its supremacy.