Thesis Abstract

Abstract
Internet of Things (IoT) devices are becoming increasingly pervasive in our daily lives, offering convenience and connectivity in various domains such as healthcare, smart homes, and industrial automation. However, the rapid proliferation of IoT devices has raised concerns about their security vulnerabilities, as they are often targeted by malicious actors for unauthorized access and data breaches. In this context, the use of machine learning algorithms presents a promising approach to enhance the security of IoT devices by detecting and mitigating potential threats in real-time. This thesis investigates the application of machine learning algorithms to bolster the security of IoT devices, aiming to provide a proactive defense mechanism against cyber threats. The research focuses on developing a comprehensive framework that integrates machine learning techniques with IoT security protocols to identify and respond to security incidents effectively. By leveraging the capabilities of machine learning models, the proposed framework can analyze patterns in IoT device behavior, detect anomalies, and trigger timely security measures to prevent potential attacks. Chapter 1 provides an introduction to the research topic, outlining the background, problem statement, objectives, limitations, scope, significance, and structure of the thesis. It also defines key terms relevant to the study, setting the foundation for the subsequent chapters. Chapter 2 conducts a thorough literature review, examining existing research on IoT security, machine learning algorithms, and their integration for enhancing cybersecurity. The review identifies key challenges and opportunities in this domain, informing the development of the research methodology in Chapter 3. Chapter 3 details the research methodology employed in this study, including data collection, model selection, training, and evaluation processes. The chapter also discusses the experimental setup and validation techniques used to assess the effectiveness of the proposed security framework. Chapter 4 presents a comprehensive discussion of the findings obtained from the experimental analysis, highlighting the performance of the machine learning algorithms in detecting security threats and mitigating risks in IoT environments. The chapter also discusses the implications of the results and potential areas for further research. Chapter 5 concludes the thesis by summarizing the key findings, discussing the contributions to the field of IoT security, and outlining recommendations for future work. The study underscores the importance of leveraging machine learning algorithms to fortify the security of IoT devices and mitigate evolving cyber threats effectively. In conclusion, this thesis contributes to the ongoing efforts to enhance the security of IoT devices through the application of machine learning algorithms. By developing a proactive security framework that leverages advanced data analytics and anomaly detection techniques, the research offers a valuable approach to safeguarding IoT ecosystems and ensuring the integrity and confidentiality of sensitive data transmitted by connected devices.

Thesis Overview