Year: 2025
Type: article
Abstract: 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.
DOI: 10.14419/h2qnce11
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4415735157
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