Improved Network Intrusion Detection Systems (NIDS) using Data Mining Techniques

The subject of Intrusion Detection System (IDS) is a very interesting research topic actively pursued by many investigators. The goal of intrusion detection is to monitor network assets and to detect anomalous behaviour and misuse. Intrusion Detection Systems aimto identify attacks with a high detection rate and a low false alarm rate. Intrusion Detection Systems (IDS) can monitor users, applications, networks, or combinations of the three, in order to detect well-known and unknown attacks. In this research work, I read many papers inwhich I found that some papers used supervised machine learning method. In this method, SVM algorithm was used and kernel function was also used. Using this concept, when intrusion detection system was built, its accuracy was between 70% to 81%. Apart from this, whenI run generic algorithm and another model based on signature, its performance was between 85% to 91%. Similarly, when I used generic algorithm and another model based on anomaly, its performance was the best. Apart from this, I run some other models in which unsupervised method was used. In this, FCM algorithm was used which follows DBSCAN algorithm.it reduce the false positive rate. In my research paper, I have used a hybrid method in which I have created an intrusion detection model using SVM, FCM and DBSCAN algorithm which not only reduces the false positive rate but also improves network security. Keywords: Data Mining, Intrusion Detection, Classification, False Positive, Anomaly based algorithm, Machine Learning, Deep Learning, NSL-KDD Data set.