Cyber Threat Detection Using S.V.M Algorithm

Cyber Threat Detection Using S.V.M Algorithm

Author Name : Prof. Sai Sudha, Vedant Kshirsagar, Kunal Patil, Shreeraj Pawar, Aniket Palle

ABSTRACT Cybersecurity is a critical concern in today’s digital landscape due to the increasing number and sophistication of cyber threats. Traditional security mechanisms often struggle to keep pace with emerging cyber threats, necessitating the implementation of advanced machine learning techniques for threat detection. Support Vector Machine (SVM), a supervised learning algorithm, has gained prominence in cyber threat detection due to its high classification accuracy, robustness, and ability to handle high-dimensional data.This paper presents an approach to cyber threat detection using the SVM algorithm, leveraging its capability to classify and detect malicious activities efficiently. SVM operates by constructing an optimal hyperplane that maximizes the margin between different classes in a dataset. It is particularly effective in distinguishing between benign and malicious activities, even in complex and high-dimensional cybersecurity datasets. The methodology involves preprocessing raw cybersecurity data, feature extraction, normalization, and training the SVM model using labeled data. Kernel functions such as linear, polynomial, radial basis function (RBF), and sigmoid are evaluated to determine the best-performing model for threat detection.A comparative analysis is conducted between SVM and other machine learning algorithms , such as Decision Trees , Random Forests , Naïve Bayes , and Deep Learning models, to assess the performance of SVM in terms of accuracy, precision, recall, and F1-score. Experimental results indicate that SVM performs exceptionally well in detecting cyber threats, particularly in scenarios with imbalanced datasets. The study also explores hyperparameter tuning techniques, including grid search and cross-validation, to enhance the model's predictive performance.Moreover, the integration of SVM with ensemble learning techniques and hybrid models is discussed to improve detection rates and reduce false positives.