Deep Learning Techniques for Enhancing Cybersecurity in IoT ‎Networks

  • Authors

    • Bhavuk Samrat Chitkara Center for Research and Development, Chitkara University, Himachal Pradesh, India
    • Dr. Trapty Agarwal Associate Professor, Maharishi School of Engineering & Technology, Maharishi University of Information Technology, Uttar Pradesh, ‎India
    • Shashikant Deepak Assistant Professor, uGDX, ATLAS SkillTech University, Mumbai, India
    • Dr. Bharat Jyoti Ranjan Sahu Associate Professor, Center for Cyber Security, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India
    • Dr. S. Emalda Roslin Professor, Department of Electronics and Communication Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamil ‎Nadu, India
    • G. N. Mamatha Assistant Professor, Department of Electronics and Communication Engineering, Faculty of Engineering and Technology, JAIN ‎‎(Deemed-to-be University), Ramnagar District, Karnataka, India
    • Bhanu Juneja Center of Research Impact and Outcome, Chitkara University, Rajpura, Punjab, India
    https://doi.org/10.14419/h2qnce11

    Received date: May 2, 2025

    Accepted date: May 31, 2025

    Published date: October 31, 2025

  • Cyber Security; ML; DL and Risk; Security

  • 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.

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  • How to Cite

    Samrat , B. ., Agarwal , D. T. ., Deepak , S. ., Sahu , D. B. J. R. ., Roslin , D. S. E. ., Mamatha , G. N. ., & Juneja , B. . (2025). Deep Learning Techniques for Enhancing Cybersecurity in IoT ‎Networks. International Journal of Basic and Applied Sciences, 14(SI-1), 319-324. https://doi.org/10.14419/h2qnce11