Anomaly Detection in IoT Networks Using Machine Learning Algorithms

Thesis Abstract

Abstract
The rapid growth of Internet of Things (IoT) devices has revolutionized various industries, providing numerous benefits such as improved efficiency, automation, and convenience. However, the increasing complexity and scale of IoT networks have also raised concerns about security vulnerabilities and potential threats. One critical aspect of IoT network security is anomaly detection, which involves identifying abnormal behavior or activities that deviate from the expected patterns. In this thesis, we propose a novel approach for anomaly detection in IoT networks using machine learning algorithms. The primary objective of this research is to develop an effective anomaly detection system that can accurately identify and classify anomalies in IoT networks. To achieve this goal, we conducted an extensive review of existing literature to understand the current state-of-the-art techniques and methodologies in anomaly detection. The literature review highlighted the limitations of traditional rule-based approaches and the advantages of machine learning algorithms in handling complex and dynamic IoT environments. In the research methodology section, we outline the steps involved in designing and implementing the anomaly detection system. This includes data collection, preprocessing, feature extraction, model selection, training, and evaluation. We also discuss the selection criteria for machine learning algorithms, such as support vector machines, random forests, and neural networks, based on their suitability for anomaly detection tasks in IoT networks. The findings from our experiments demonstrate the effectiveness of the proposed anomaly detection system in accurately detecting various types of anomalies in IoT network traffic. We evaluated the performance of different machine learning algorithms using metrics such as accuracy, precision, recall, and F1-score. The results indicate that certain algorithms outperform others in terms of detection accuracy and computational efficiency. In the discussion section, we analyze the implications of our findings and compare them with existing research. We also identify potential challenges and future research directions for improving anomaly detection in IoT networks. The discussion emphasizes the importance of adaptive and scalable anomaly detection systems to address the evolving threats and vulnerabilities in IoT environments. In conclusion, this thesis contributes to the field of IoT network security by proposing a robust anomaly detection system based on machine learning algorithms. The results demonstrate the feasibility and effectiveness of using advanced computational techniques to enhance the security and resilience of IoT networks. This research opens up new avenues for developing intelligent and proactive security mechanisms to protect IoT devices and infrastructure from malicious activities.