Plant and Salamander Inspired Network Attack Detection and Data Recovery Model

Abstract

The number of users of the Internet has been continuously rising, with an estimated 5.1 billion users in 2023, which comprises around 64.7% of the total world population. This indicates the rise of more connected devices to the network. On average, 30,000 websites are hacked daily, and nearly 64% of companies worldwide experience at least one type of cyberattack. As per IDC’s 2022 Ransomware study, two-thirds of global organizations were hit by a ransomware attack that year. This creates the desire for a more robust and evolutionary attack detection and recovery model. One aspect of the study is the bio-inspiration models. This is because of the natural ability of living organisms to withstand various odd circumstances and overcome them with an optimization strategy. In contrast to the limitations of machine learning models with the need for quality datasets and computational availability, bio-inspired models can perform in low computational environments, and their performances are designed to evolve naturally with time. This study concentrates on exploring the evolutionary defence mechanism in plants and understanding how plants react to any known external attacks and how the response mechanism changes to unknown attacks. This study also explores how regenerative models, such as salamander limb regeneration, could build a network recovery system where services could be automatically activated after a network attack, and data could be recovered automatically by the network after a ransomware-like attack. The performance of the proposed model is compared to open-source IDS Snort and data recovery systems such as Burp and Casandra.

A Hypertuned Lightweight and Scalable LSTM Model for Hybrid Network Intrusion Detection

“…Federated learning has been used for network data security [4,30], healthcare data security [31], game theory [32], vehicular data [33,34], and smart city applications [35]. Studies have found that data poisoning attacks and other cyberattacks can also compromise this technique [36][37][38][39]. Furthermore, traditional machine learning approaches have a limited processing complexity and need help discovering complicated non-linear relationships in large datasets.…”