Deep Learning Techniques for Enhancing Cybersecurity in IoT Networks

The progression of internet technologies, such as Industry 4.0, IoT (Internet of Things), Industrial IoT, Medical of Things (MoI), and Web ‎‎3.0, has led to a continual increase in cybersecurity issues. These technologies improve users' lives by making devices accessible online. ‎This is sometimes referred to as the Internet of Everything, or IoE. With the rise in internet users and connected gadgets, fraudsters might ‎take advantage of susceptible hosts. The networked gadgets display specific weaknesses that enable their exploitation. Malicious software ‎such as malware, botnets, and intrusions can be utilized to compromise these devices. The ability of fileless malware to infiltrate a system ‎without leaving evidence of exploitation has been utilized by astute, systematic, and targeted hackers. The malicious software exploits the ‎system's vulnerabilities. As per OWASP (Open Web Application Security Project ¹, the ten most exploited vulnerabilities in 2021 ‎encompass server-side request forgery, injection, insecure design, compromised authentication, software and data integrity failure, security ‎misconfiguration, broken access control, and inadequate logging and monitoring. Machine learning and deep learning methodologies exhibit ‎promise in identifying the substantial cybersecurity dangers mentioned above. As a result, deep learning models were developed to identify ‎and categorize these risks.