Network Intrusion Detection Using Supervised Machine Learning Technique

— In the modern computer world, use of the internet is increasing day by day. However, the increasing use of the internet creates some security issues. These days, such new type of security attacks occurs every day and it is not easy to detect and prevent those attacks effectively. One common method of attack involves sending large amount of request to site or server and server will be unable to handle such huge requests and site will be offline for many days. This type of attack is called distributed denial of service (DDOS) attack, which act as a major security threat to internet services and most critical attack for cyber security world. Detection and prevention of Distributed Denial of Service Attack (DDoS) becomes a crucial process for the commercial organizations that uses the internet. Different approaches have been adapted to process traffic information collected by monitoring stations (Routers and Servers) to distinguish malicious traffic such as DDoS attack from normal traffic in Intrusion Detection Systems (IDS). In general, Machine learning techniques can be designed and implemented with the intrusion systems to protect the organizations from malicious traffic. Specifically, supervised clustering techniques allow to effectively distinguishing the normal traffic from malicious traffic with good accuracy. In this paper, machine learning algorithms are used to detect DDoS attacks collected from “KDD cup 99 Dataset”, pre- processing and feature selection technique is used on the dataset to enhance the performance of the classifiers and reduce the detection time. The classification algorithms such as C4.5 decision tree and Navie Bayes is applied on the training dataset and the implementation of the algorithm is done using spyder tool. The performance comparison of algorithms is shown using confusion matrix and it is found that C4.5 decision is more efficient in detection of DDOS attack. The proposed method can be used as DDoS defense system.