Ai-Powered Cybersecurity Framework for Secure Data Transmission in Iot Network

Internet of Things security is attracting a growing attention from both academic and industry communities. Indeed, IoT devices are prone to various security attacks varying from Denial of Service (DoS) to network intrusion and data leakage. This paper presents a novel machine learning (ML) based security framework that automatically copes with the expanding security aspects related to IoT domain. This framework leverages both Software Defined Networking (SDN) and Network Function Virtualization (NFV) enablers for mitigating different threats. This AI framework combines monitoring agent and AIbased reaction agent that use ML-Models divided into network patterns analysis, along with anomalybased intrusion detection in IoT systems. The framework exploits the supervised learning, distributed data mining system and neural network for achieving its goals. Experiments results demonstrate the efficiency of the proposed scheme. In particular, the distribution of the attacks using the data mining approach is highly successful in detecting the attacks with high performance and low cost. Regarding our anomaly-based intrusion detection system (IDS) for IoT, we have evaluated the experiment in a real Smart building scenario using one-class SVM. The detection accuracy of anomalies achieved 99.71%. A feasibility study is conducted to identify the current potential solutions to be adopted and to promote the research towards the open challenges. The rapid proliferation of the Internet of Things (IoT) has introduced significant security challenges, particularly in ensuring secure data transmission across interconnected devices. Traditional security approaches struggle to keep up with the evolving threat landscape due to the dynamic and resource-constrained nature of IoT networks. This paper proposes an AI-powered cybersecurity framework that integrates machine learning (ML), deep learning (DL), and anomaly detection techniques to enhance data security in IoT environments. The framework employs realtime threat detection, adaptive encryption, and intelligent intrusion prevention to mitigate cyber threats effectively. A combination of behavioral analysis, network traffic monitoring, and AI-driven predictive modeling is used to identify and prevent malicious activities.