Development of a threat detection system for network attacks

Abstract

In today’s world the structure and dynamic interactions in the large network systems has become substantially complex. The threats and security attacks are currently spread everywhere and are tend to increase significantly in the future with the Internet of Things (IoT). The late detection of security threats causes a significant increase in the risk of irreparable damages, disabling any defense attempt. In this new era of security, information security professionals must deliver a very effective, real-time defense that can predict inherent threats to all the critical assets. All attacks will leave detectable traces, even though most of them will be complex and very hard to analyze. Threat monitoring systems, must have the capacity to observe activities in big data collected from networks and detect the threats. In order to provide the most secured network environment and network traffic monitoring threat detection systems must handle the real-time data. An accurate and reliable TDS will be automated that will be able to improve the traditional methods in order to fulfill the goals quickly and detect malicious activity and act accordingly. We focus on a robust classification method that includes an efficient SVM classifier will be used to handle network security concerning big network traffic.