Title: AI-powered Anomaly Detection for IoT Security

Authors:  Dr.  Abigail

Abstract: The rapid proliferation of Internet of Things (IoT) devices has introduced new security challenges due to their diverse nature, limited resources, and often insecure communication protocols. Traditional security measures are often inadequate to detect sophisticated cyberattacks targeting IoT networks. This research explores the application of Artificial Intelligence (AI) techniques for anomaly detection to enhance IoT security. We investigate various machine learning algorithms, including supervised, unsupervised, and reinforcement learning approaches, to analyze network traffic, device behavior, and sensor data for identifying malicious activities. The goal is to develop intelligent systems that can automatically detect anomalies indicative of intrusions, malware infections, or denial-of-service attacks. We evaluate the performance of different AI models based on metrics like detection accuracy, false positive rate, and adaptability to evolving threats. Furthermore, we address the challenges of deploying AI-based anomaly detection systems in resource-constrained IoT environments, exploring techniques like edge computing and model optimization. The results demonstrate the potential of AI-powered anomaly detection to provide proactive and adaptive security for IoT networks, contributing to a more resilient and trustworthy IoT ecosystem. This research contributes to the growing field of AI-driven cybersecurity and offers insights into building robust security solutions for the increasingly interconnected world of IoT.

Keywords: IoT Security, Anomaly Detection, Artificial Intelligence (AI), Machine Learning, Deep Learning, Cybersecurity, Intrusion Detection, Malware Detection

International Journal of Applied Pattern Recognition, Vol. 5, No. 1, 2017 

Received: 14 Feb 2017

Accepted: 15 April 2017

Published online: 11 June 2017