Thesis Abstract

Abstract
The rapid proliferation of Internet of Things (IoT) devices in various domains has led to an increased risk of cybersecurity threats. As these devices become more interconnected and integrated into critical infrastructure, the need for effective cybersecurity measures to detect and mitigate threats becomes paramount. This research project focuses on the application of machine learning techniques for detecting cybersecurity threats in IoT devices. The primary objective is to develop a robust and efficient system that can identify and respond to potential threats in real-time, thus enhancing the overall security of IoT ecosystems. The research begins with a comprehensive introduction that outlines the background of the study, the problem statement, objectives, limitations, scope, significance, and the structure of the thesis. A detailed literature review in Chapter Two explores existing studies and technologies related to machine learning and cybersecurity in IoT environments. This chapter provides a foundation for understanding the current state of the field and identifies gaps that this research aims to address. Chapter Three presents the research methodology, detailing the approach taken to design, implement, and evaluate the machine learning models for threat detection in IoT devices. This chapter covers data collection, preprocessing, feature selection, model training, evaluation metrics, and validation techniques. Additionally, ethical considerations and potential biases in the research process are discussed. Chapter Four delves into the discussion of findings, presenting the results of the experiments conducted to evaluate the performance of the machine learning models in detecting cybersecurity threats in IoT devices. The chapter analyzes the effectiveness, efficiency, and scalability of the proposed system, highlighting strengths, weaknesses, and areas for future improvement. Finally, Chapter Five offers a conclusion and summary of the research project, consolidating the key findings, contributions, and implications of the study. The conclusions drawn from the research are discussed, along with recommendations for future work in this area. Overall, this thesis contributes to the advancement of cybersecurity in IoT environments by demonstrating the potential of machine learning techniques for enhancing threat detection capabilities. In conclusion, this research project aims to address the growing cybersecurity challenges in IoT devices through the application of machine learning techniques. By developing an innovative system for threat detection, this study contributes to the ongoing efforts to safeguard IoT ecosystems and protect critical infrastructure from malicious attacks.

Thesis Overview

The project titled "Applying Machine Learning Techniques for Detecting Cybersecurity Threats in Internet of Things (IoT) Devices" aims to address the growing concerns surrounding the security of IoT devices. As the number of connected devices continues to rise, so does the potential vulnerability to cyber threats. This research seeks to leverage machine learning algorithms to enhance the detection and mitigation of cybersecurity threats within the IoT ecosystem. The Internet of Things (IoT) has revolutionized the way we interact with technology, allowing for seamless connectivity and data exchange across various devices. However, this interconnectedness also introduces new challenges, particularly in terms of security. IoT devices are often resource-constrained and lack robust security measures, making them prime targets for cyber attacks. Traditional security solutions are not always effective in this context, necessitating the exploration of alternative approaches such as machine learning. Machine learning offers the potential to detect and respond to cybersecurity threats in real-time, providing a proactive defense mechanism against malicious activities. By analyzing patterns in data generated by IoT devices, machine learning algorithms can identify anomalous behavior indicative of a security breach. Through the implementation of supervised and unsupervised learning techniques, this research aims to develop a model capable of accurately detecting and classifying different types of cybersecurity threats within the IoT environment. The research methodology will involve collecting and preprocessing a diverse dataset of IoT device interactions to train and evaluate the machine learning model. Various algorithms such as decision trees, support vector machines, and neural networks will be explored to identify the most effective approach for threat detection. Additionally, the performance of the model will be assessed based on metrics such as accuracy, precision, recall, and F1 score to ensure its reliability in real-world scenarios. The findings of this research are expected to contribute to the enhancement of IoT security practices by providing a data-driven and intelligent solution for detecting cybersecurity threats. By leveraging machine learning techniques, IoT devices can be better equipped to defend against a wide range of malicious activities, ultimately safeguarding sensitive data and ensuring the integrity of the IoT ecosystem. In conclusion, the project on "Applying Machine Learning Techniques for Detecting Cybersecurity Threats in Internet of Things (IoT) Devices" represents a significant step towards improving the security posture of IoT devices. By harnessing the power of machine learning, this research aims to fortify the defenses of IoT networks and mitigate the risks associated with cyber threats, ultimately fostering a more secure and resilient IoT environment.