Project Abstract

Abstract
Cyber attacks have become a significant threat to individuals, organizations, and governments, resulting in financial losses, data breaches, and disruption of critical services. Early detection of cyber attacks is crucial to prevent or minimize the damage caused by such malicious activities. In this research project, we aim to develop a Machine Learning-based system for the early detection of cyber attacks. The proposed system will leverage advanced machine learning algorithms to analyze network traffic patterns and identify potential security threats in real-time. The research will commence with a comprehensive review of existing literature related to cyber security, machine learning, and intrusion detection systems. This literature review will provide a foundation for understanding the current state of the art in cyber security and machine learning techniques used for threat detection. The research methodology will involve collecting and analyzing network traffic data from various sources, including simulated attack scenarios and real-world datasets. The data will be preprocessed to extract relevant features and train machine learning models for classification and anomaly detection. The performance of these models will be evaluated using metrics such as accuracy, precision, recall, and F1-score. The findings of this research will be presented and discussed in Chapter 4, where we will analyze the effectiveness of the developed Machine Learning-based system in detecting cyber attacks early. The discussion will include the strengths and limitations of the system, as well as potential areas for improvement and future research directions. In conclusion, this research project aims to contribute to the field of cyber security by providing a novel approach to early detection of cyber attacks using Machine Learning techniques. The proposed system has the potential to enhance the security posture of organizations and individuals by proactively identifying and mitigating security threats. By leveraging the power of Machine Learning, we strive to create a more secure and resilient cyber environment in the face of evolving cyber threats. Keywords Cyber security, Machine Learning, Intrusion Detection, Network Security, Early Detection, Threat Detection.

Project Overview