Thesis Abstract

Abstract
Cloud computing has revolutionized the way organizations handle data and computing resources, offering scalability and flexibility. However, the security of cloud environments remains a significant concern due to the potential for unauthorized access and attacks. Intrusion detection systems (IDS) play a crucial role in safeguarding cloud infrastructures by identifying and responding to suspicious activities. Traditional signature-based IDS solutions are limited in detecting new and complex attacks, highlighting the need for more advanced approaches. This thesis focuses on the application of machine learning algorithms for enhancing intrusion detection capabilities in cloud computing environments. The research aims to design and implement a machine learning-based IDS system that can effectively detect and respond to various types of intrusions. The study will investigate different machine learning techniques, such as supervised learning, unsupervised learning, and deep learning, to analyze network traffic patterns and identify anomalies indicative of potential threats. Chapter 1 provides an introduction to the research topic, presenting the background, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. Chapter 2 presents a comprehensive literature review, covering ten key studies related to machine learning-based intrusion detection in cloud computing environments. In Chapter 3, the research methodology is detailed, outlining the steps involved in designing, implementing, and evaluating the machine learning-based IDS system. The chapter covers aspects such as data collection, feature selection, model training, evaluation metrics, and performance testing methods, among others. Chapter 4 delves into the discussion of findings, presenting the results of experiments conducted to evaluate the effectiveness and efficiency of the proposed machine learning-based IDS system. The chapter analyzes the performance metrics, compares different machine learning algorithms, and discusses the practical implications of the results. Finally, Chapter 5 provides a conclusion and summary of the thesis, highlighting the key findings, contributions, limitations, and future research directions. The research findings demonstrate the potential of machine learning algorithms in enhancing intrusion detection capabilities in cloud computing environments, paving the way for more robust and adaptive security solutions. In conclusion, this thesis contributes to the field of cybersecurity by proposing a novel approach to intrusion detection in cloud computing environments. By leveraging machine learning algorithms, organizations can strengthen their security posture and proactively defend against evolving cyber threats in the dynamic cloud environment.

Thesis Overview

The project titled "Applying Machine Learning Algorithms for Intrusion Detection in Cloud Computing Environments" aims to address the critical issue of securing cloud computing environments against potential intrusions. With the increasing reliance on cloud computing for storing sensitive data and running critical applications, the need for robust intrusion detection systems has become paramount. Traditional rule-based intrusion detection systems often struggle to keep pace with the evolving nature of cyber threats, leading to the exploration of machine learning algorithms as a more effective approach to detecting and mitigating intrusions in cloud environments. This research project will delve into the application of various machine learning algorithms, such as supervised learning, unsupervised learning, and deep learning, for the purpose of intrusion detection in cloud computing environments. By leveraging the power of machine learning, the goal is to develop a system that can autonomously detect and respond to potential security breaches in real-time, thereby enhancing the overall security posture of cloud-based systems. The research overview will involve a comprehensive literature review to explore existing studies, methodologies, and technologies related to intrusion detection in cloud computing. It will also include the design and implementation of a prototype system that integrates machine learning algorithms for intrusion detection, as well as the evaluation of its performance in a simulated cloud environment. Furthermore, the research methodology will outline the specific steps and procedures involved in the development and evaluation of the intrusion detection system. This will include data collection, preprocessing, feature selection, model training, testing, and performance evaluation metrics to assess the effectiveness and efficiency of the machine learning algorithms in detecting intrusions. The discussion of findings will present a detailed analysis of the results obtained from the experimental evaluation of the intrusion detection system. This will include a comparison of different machine learning algorithms in terms of their detection accuracy, false positive rates, computational efficiency, and scalability in a cloud computing environment. In conclusion, this research project will contribute to the advancement of intrusion detection systems in cloud computing environments by demonstrating the efficacy of machine learning algorithms in enhancing security and mitigating cyber threats. The findings and insights gained from this study have the potential to inform the development of more robust and adaptive security solutions for cloud-based systems, ultimately contributing to a safer and more secure cloud computing ecosystem.