Artificial Intelligence Empowered Data Analytics To Enhance The Detection And Prevention Of Security Breaches In Iot Networks
DOI:
https://doi.org/10.31838/NJAP/08.01.24Keywords:
IoT, AI, machine learning, Security, Cyber system, Network management.Abstract
The rapid growth of Internet of Things (IoT) devices has caused massive disruption in multiple industries by facilitating seamless connection and information sharing. However, this wide acceptance introduced serious security issues that have affected IoT networks rendering them susceptible to various cyber threats. The research introduces an alternative method whereby artificial intelligence tools are used to secure IoT networks thus simplifying data management and preventing threats from arising. The suggestion is a combination of advanced Machine Learning Algorithms (MLA) and real-time analytics for improving detection and prevention capabilities of security breaches in an IoT network. Whereas a mixture of supervised and unsupervised learning approaches formulated as hybrid Artificial Intelligence model can accurately detect well-known as well as unknown risks. A dataset with labeled instances of cyber-attacks has been employed to train the supervised part so that it can identify identified patterns of malicious activities. Unsupervised learning remains vigilant on the network traffic so as to detect any abnormality or dangers at their inception. A key differentiator for our solution is its AI-assisted decentralized data management Framework (AI-DDMF) based on blockchain technology. This provides integrity and confidentiality of the data by creating an indelible register for each transaction and interaction within the IoT network. To minimize the system’s response time when faced with imminent threats, it uses edge computing which reduces latency while increasing scalability. Furthermore, this paper proposes a new AI-based encryption scheme for sensitive data transferred through the channels called as intelligent adaptive threat changing mechanism. This paper examines how creative data management approaches backed by artificial intelligence can protect IoT systems against cyber-attacks. The suggested solutions are more holistic, easily implementable unlike traditional methods thus safeguarding the next generation of IoT ecosystems. The AI-driven IoT security framework demonstrated impressive performance, achieving a detection rate of 96.31%, scalability at 97%, flexible security at 98.7%, system resilience at 98.3%, and a reduction in response time from 30% to 45%.
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