Applying Machine Learning Algorithms for Intrusion Detection in Cloud Computing Environments

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.