Authors : Katikam Mahesh; Dr. Kunjam Nageswara Rao

Volume/Issue : Volume 9 - 2024, Issue 10 - October

Google Scholar : https://tinyurl.com/yh7zuz76

Scribd : https://tinyurl.com/yhnhu3mt

DOI : https://doi.org/10.38124/ijisrt/IJISRT24OCT580

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Millions of digital devices total the Internet of Things (IoT), and this allows very easy interaction from users connecting the devices. IoT is one of the tech sectors that is expanding most rapidly, but it can also be very vulnerable to hazards. Infections and abnormal placement on the Internet of Things (IoT) framework is an increasing threat in the field of technology. In view of the growing IoT foundation usage across all industries, attacks and dangers on these systems have also grown proportional. Leveraging typical machine learning methods, cyber-attack detection plays a critical role in avoiding damage from cyberattacks on IoT devices. IoT Cyber Attacks are Not Detected by ANN Artificial Neural Networks Using Deep Learning Techniques CNN-DRL (Convolutional Neural Networks-Deep Reinforcement Learning) Hybrid Approach: Detects Attacks, including Distributed Denial of Service (DDoS), Zero-day, and Eavesdropping Attacks.

Keywords : Cyber Attacks, Internet of Things (IoT), Convolutional Neural Networks, Deep Reinforcement Learning.

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