Thesis Abstract

Abstract
Cloud computing has become an integral part of modern computing systems, offering scalability, flexibility, and cost-effectiveness. However, the increasing adoption of cloud computing has also led to a rise in cyber attacks targeting cloud environments. Detecting and preventing these attacks is crucial to ensure the security and integrity of data stored in the cloud. Machine learning techniques have shown promise in enhancing cybersecurity measures by enabling the detection of anomalous behavior and patterns indicative of cyber attacks. This thesis aims to explore the application of machine learning algorithms to detect and prevent cyber attacks in cloud computing environments. Chapter 1 provides an introduction to the research topic, discussing the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. The literature review in Chapter 2 examines existing research on cyber attacks in cloud computing and the application of machine learning for cybersecurity. Chapter 3 outlines the research methodology, including data collection, preprocessing, feature selection, model training, and evaluation metrics. Chapter 4 presents a detailed discussion of the findings from implementing machine learning algorithms for cyber attack detection and prevention in cloud environments. Various machine learning models such as supervised learning, unsupervised learning, and deep learning techniques are evaluated for their effectiveness in identifying and mitigating cyber threats. The results are analyzed, and insights are drawn to understand the performance and limitations of different approaches. In conclusion, Chapter 5 summarizes the key findings of the study and provides insights into the potential of machine learning for enhancing cybersecurity in cloud computing environments. The research contributes to the growing body of knowledge on cybersecurity and provides practical recommendations for implementing machine learning solutions to combat cyber threats effectively. Overall, this thesis underscores the importance of proactive measures to safeguard cloud infrastructure and data from malicious activities, highlighting the role of machine learning as a valuable tool in cybersecurity defense strategies.

Thesis Overview

The project titled "Applying Machine Learning to Detect and Prevent Cyber Attacks in Cloud Computing Environments" aims to address the increasing threat of cyber attacks within cloud computing environments by leveraging the power of machine learning algorithms. Cloud computing has become an integral part of modern business operations, offering scalability, flexibility, and cost-efficiency. However, the shared nature of cloud resources and the vast amounts of sensitive data stored in the cloud make it an attractive target for cyber attackers. The research will focus on developing and implementing machine learning models to enhance the security posture of cloud computing environments. By analyzing historical data on cyber attacks and network traffic patterns, the machine learning algorithms will be trained to detect anomalies and potential threats in real-time. These models will be designed to adapt and evolve to new attack vectors and patterns, providing a proactive defense mechanism against cyber threats. The project will also explore the use of machine learning for predictive analysis, enabling the identification of potential vulnerabilities and weaknesses in cloud systems before they are exploited by attackers. By leveraging advanced analytics and pattern recognition techniques, the research aims to provide cloud service providers and organizations with actionable insights to strengthen their security measures and mitigate the risk of cyber attacks. Key components of the research will include a comprehensive literature review to examine existing approaches and technologies in the field of cybersecurity and machine learning. The methodology will involve data collection, preprocessing, model training, evaluation, and deployment within a simulated cloud computing environment. The findings will be thoroughly analyzed and discussed, highlighting the effectiveness and limitations of the machine learning models in detecting and preventing cyber attacks. Overall, this project seeks to contribute to the advancement of cybersecurity practices in cloud computing environments through the innovative application of machine learning techniques. By enhancing threat detection capabilities and proactive defense mechanisms, the research aims to empower organizations to safeguard their critical data and infrastructure from evolving cyber threats in the digital age.