Detection of Malicious Network Traffic Attacks Using Support Vector Machine

Network security plays an important role in an increasingly related society. For the safety and security of information system, detection of network traffic attacks is crucial. This research work focuses on the use of Support Vector Machines (SVM) algorithm as a machine learning tool for the detection of network traffic attacks. SVM is a flexible and adaptable classification algorithm that has shown its fruitfulness in multiple domains, including network security. This paper elaborates the utilization of SVM to observe between normal and malicious network traffic patterns. In this paper, we prepare multiple categories of network attacks, such as Denial-of-Service (DoS), User-to-Root (U2R), and Remote-to-Local (R2L) attacks. For training and evaluation purpose, KDD Cup 99 dataset is used. Several number of performance metrics, like accuracy, precision, recall, as well as F1-score, are also used to evaluate the correctness of the SVM-based intrusion detection system. The results illustrate the capability of SVM in detection of network traffic, attaining high accuracy rates while accurately solving the issues of complex attack patterns where the F1-Score for the Linear kernal and RBF 96.65 and 99.24 respectively. In addition to this, the research also highlights the significance of feature engineering along with hyperparameter engaged in develop SVM models for specified network security. In the end, this study highlights the usefulness of SVM as an essential tool in network security to identify network traffic attacks.