Cyber Threat Detection using Machine Learning

Abstract

Cybersecurity threats are increasing rapidly with the growth of internet-based services, cloud computing, and digital communication systems. Traditional security methods are often unable to detect sophisticated and evolving cyber-attacks in real time. To overcome these limitations, this project presents a Cyber Threat Detection System using Machine Learning techniques for identifying malicious activities and improving network security. The system is designed to analyze large volumes of network traffic and detect abnormal behavior that may indicate cyber threats such as phishing attacks, malware, denial-of-service attacks, unauthorized access, and data breaches. The proposed system uses machine learning algorithms to classify network activities into normal and malicious categories based on patterns extracted from the dataset. Data preprocessing techniques such as data cleaning, feature selection, normalization, and encoding are applied to improve the quality and accuracy of the model. Various machine learning algorithms including Decision Tree, Random Forest, Logistic Regression, and Support Vector Machine are used for threat prediction and comparison of performance. The system is trained and tested using cybersecurity datasets to achieve accurate detection results with reduced false positives. In addition to threat detection, the project integrates data visualization techniques using Tableau dashboards to provide real-time graphical analysis of cyber threats, attack trends, and network behavior. The dashboard helps administrators monitor suspicious activities effectively and make faster security decisions. Performance evaluation metrics such as accuracy, precision, recall, and F1-score are used to measure the effectiveness of the proposedmodel.