An Adaptive Machine Learning Model for Cyber Threat Intelligence in IoT Security

An Adaptive Machine Learning Model for Cyber Threat Intelligence in IoT Security.

Introduction. • The rapid growth of IoT devices has led to increased security vulnerabilities. • Existing security models rely on static rules, which fail to detect evolving threats. • Cyber Threat Intelligence (CTI) enhances real-time detection and adaptive defenses. • This study proposes an adaptive ML-based CTI model for IoT security..

Problem Statement. • IoT networks are vulnerable to cyber threats like DDoS and unauthorized access. • Existing security approaches fail to detect zero-day attacks. • The need for a scalable, adaptive, real-time threat mitigation system..

Objectives & Research Questions. General Objective: • Develop an adaptive Cyber Threat Intelligence (CTI) model using machine learning. Specific Objectives: • Analyze limitations of current IoT security models. • Develop a multi-paradigm ML-based CTI model. • Implement and test the model on public datasets. • Evaluate its performance for real-world IoT applications. Research Questions: • How does the model improve IoT security over existing solutions? • Can it detect zero-day attacks effectively?.

Significance of the Study. • Enhances IoT cybersecurity resilience. • Strengthens security for smart cities, healthcare, and industrial IoT. • Supports policymakers in securing IoT networks. • Addresses real-time detection gaps with an adaptive ML model..

Methodology. • Research Design: Experimental approach using real and simulated IoT data. • Data Sources: Public datasets (TON_IoT, CICIDS2017) + NS-3 simulated data. • Model Components: - Supervised Learning (Random Forest) - Unsupervised Learning (DBSCAN) - Deep Learning (CNN, LSTM) - Reinforcement Learning (DQN) • Performance Metrics: Accuracy ≥90%, latency <1 sec..

Expected Outcomes. • Improved detection and mitigation of cyber threats in IoT networks. • Increased adaptability to zero-day and evolving threats. • Scalable, lightweight, and real-time security solutions. • Higher accuracy and efficiency compared to existing models..

Conclusion. • IoT security challenges require proactive and adaptive solutions. • This study proposes a dynamic ML-based CTI model. • The model integrates multiple ML techniques to enhance IoT cybersecurity. • Future work will focus on real-world implementation and improvements..