Intelligent intrusion detection system using machine learning and deep learning model in adverse environment

Abstract

Computer networks play a pivotal role in advancing science newlineand technology. With the proliferation of communication channels and an newlineever-growing number of network devices, cybersecurity has emerged as a newlinecritical concern. Safeguarding valuable data from intruders is imperative due to newlinethe heightened vulnerability posed by this expansive network landscape. newlineAttackers continuously evolve their methods, necessitating effective Intrusion newlineDetection Systems (IDSs) capable of identifying and thwarting new infiltration newlinepatterns and attacks. newlineThe extensive integration and connectivity of computing systems newlinehave become essential for improving our daily operations. As vulnerabilities newlinearise, cybersecurity systems are crucial for ensuring secure communication newlineexchanges. Effective transmission security necessitates measures to combat newlineevolving threats and the development of security protocols capable of addressing newlineemerging risks. Despite the initial purpose of firewalls in network security, they newlineoften fail to detect intrusions in real-time. With the emergence of destructive newlinecyber-attacks presenting significant security challenges, there is a need for newlinedependable and adaptable Intrusion Detection Systems (IDS) capable of newlineefficiently monitoring unauthorized access, policy breaches, and malicious newlineactivities. newlineTraditional Machine Learning (ML) methods have been effective in newlineidentifying data patterns and detecting cyber-attacks within Intrusion Detection newlineSystems (IDSs). However, Deep Learning (DL) techniques have emerged as a newlinevaluable tool for developing highly accurate and efficient IDS approaches. newlineThe adoption of deep learning-based cybersecurity methods for intrusion newlinedetection has seen a surge in popularity newline