Saggurthi Spandana
Biography
Research Scholar, Department of Electronics and Communication Engineering
Koneru Lakshmaiah Education Foundation (KL University), Vaddeswaram, Andhra Pradesh, India
Research Interest
- Artificial Intelligence for Analog/RF Circuit Design
- Generative Adversarial Networks (GANs) for Topology Synthesis
- Low-Noise Amplifier (LNA) Optimization for IoT and mmWave Systems
- Secure Hardware for Cyber-Physical and Embedded Systems
Abstract
AI-Enhanced Cybersecurity Framework for IoT and Smart Devices: Bridging Intelligence and Protection in the Connected Era
With over 50 billion IoT devices expected by 2030, traditional cybersecurity approaches fail due to resource constraints and device diversity. This presentation introduces an AI-enhanced security framework for IoT ecosystems using lightweight machine learning models that achieve 97% threat detection accuracy while consuming 60% fewer resources than conventional solutions. Our multi-layered architecture combines anomaly detection, behavioral analysis, and federated learning for privacy-preserving threat intelligence sharing. Real-world implementations across smart homes, industrial IoT, and healthcare devices demonstrate 75% reduction in successful attacks and 85% faster incident response. Key contributions include resource-efficient AI models for constrained devices, distributed threat intelligence with privacy preservation, adaptive security policies, and integration strategies for heterogeneous environments. We address challenges including adversarial attacks and security-performance balance while incorporating explainable AI for transparent decision-making.
Keywords: IoT Security, Smart Devices, AI-Powered Cybersecurity, Edge Computing, Anomaly Detection, Federated Learning, Threat Intelligence, Device Authentication, Privacy-Preserving Security
