Review of semi-supervised method for Intrusion Detection System

The increasing number of attacks on computer networks has caused network securities were an important issue since the first network security breaches were discovered in 1980. Currently, the pattern of network attacks becomes more sophisticated and lead to difficulty in detecting the attacks. Failure to prevent the attacks makes privacy, data, and other network resources are threatened. There is numerous network intrusion detection system (NIDS) have been proposed to tackle the network security threats in which the detection methods can be grouped into supervised, unsupervised and semi-supervised. Several artificial intelligence-based methods have also been considered in the NIDS to improve detection accuracies, such as fuzzy, machine learning, support vector machine (SVM) and k-means. Unfortunately, a literature study on semi-supervised intrusion detection system IDS (SS-IDS) is difficult to be found. Indeed, most of IDS literature studies are only focused on supervised and unsupervised detection methods. Consequently, the latest developments and issues on SS-IDS are difficult to be traced quickly. On the other hand, many semi-supervised methods and implementation the methods on IDS have been carried out since 2008. This research conducts a literature study on SS-IDS to tackle the issue by reviewing the developments of SS-IDS from 2008 to 2015. A narrative literature review has been chosen as a method to review the SS-IDS literature. A narrative literature review is a method of scientific publications that addresses specific topics of theoretical viewpoints and contextual. The review results show that the accuracy of the proposed SS-IDS is low. In addition false alarms of the IDS tend to be high.