INTERNET OF THINGS (IOT) NETWORKS: AI-POWERED SECURE INTRUSION DETECTION

    Many industries have changed due to the quick spread of IoT devices, which have improved efficiency and connectedness. But this increase has also brought up serious security flaws, which makes IoT networks a prime target for hackers. The creation of AI-powered intrusion detection systems (IDS) designed especially for Internet of Things contexts is examined in this overview of the literature. These systems may examine enormous volumes of data produced by IoT devices by utilizing machine learning algorithms, spotting unusual patterns that may point to security breaches. The paper evaluates the efficacy of current machine learning methods in real-time anomaly detection and response by classifying them into supervised, unsupervised, and reinforcement learning approaches. Along with cutting-edge strategies like feature selection and hybrid models to improve detection accuracy with the least amount of resources, the main challenges such as computational and energy limitations are also covered. In the end, this assessment emphasizes the need for a system of many levels of protection that not only address current threats but also anticipate challenges posed by evolving cyberattacks techniques. By combining knowledge from current research, the results hope to guide the development of more resilient and flexible AI-powered intrusion detection systems, assisting in the safe deployment of IoT networks for a range of applications.

• Publication year

  2025

• Venue

  International Journal of Applied Mathematics

• Publication date

  2025-11-03

• Fields of study

  Not labeled

• Identifiers
  DOI 10.12732/ijam.v38i9s.842
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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