Network Packet Status-Aware and Ping-Integrated Attack Classification Along with Alert Generation Using Esw-Mlp and S3-Fuzzy
Author(s):
Amaresan Venkatesan
Recently, there has been a rapid increase in attacks along with network data, thereby posing a significant threat to network security. During attack prediction, none of the traditional systems concentrated on packet status identification. Thus, by using Entropy Softsin Wrapper based Multi-Layer Perceptron (ESWMLP) and Standard S Shaped Fuzzy (S3-Fuzzy), this paper proposes a packet status-aware attack prediction and ping-enabled Alert Generation (AG) in a network. Initially, the Canadian Institute for Cybersecurity Android Malware 2017 (CICAndMal2017) dataset is gathered and pre-processed. Then, the features are extracted, and optimal features are selected employing the Tent Chaotic-Chicken Swarm Optimization Algorithm (TC-CSOA). Next, the selected features are subjected to ESW-MLP, where the attack types are classified. Similarly, from the traffic dataset, the features are extracted, followed by feature selection. Thus, by using ESW-MLP, the packet status is identified. Similarly, the similarity between the features is estimated. Then, the AG is done based on S3-Fuzzy. Besides, via TC-CSOA-based load balancing, the network collision is diminished. Next, Ping-based PSI and Attack Classification (AC) are carried out on the switch, followed by AG. As per the experimental findings, the proposed approach had higher supremacy with 98.63% accuracy.
