Statistical vertical reduction-based data abridging technique for big network traffic dataset

Security is the major concern in the world of Internet. Traditionally, encryption, firewall, and other security countermeasures are used to secure the data. However, in the modern era of technology, the Intrusion Detection System (IDS) plays a major role in the field of security to detect the attack type. IDS are tuned in such a way that it learns from historical network traffic data and detects normal as well as abnormal event connection from the monitored system. Nevertheless, due to the huge size of historical data, this system can suffer from issues like accuracy, false alarms and execution time. In this paper, a new abridging algorithm is proposed, which is able to vertically reduce the size of network traffic dataset without affecting its statistical characteristics. In the literature, vertical data reduction i.e. features selection techniques are always used to reduce dataset, but this paper evaluates the effect of vertical reduction, which has not been examined significantly. Apart from abridging of vertical instances, Infinite Feature Selection technique is used to extract the relevant features from the dataset and Support Vector Machine classifier is used to classify normal and anomalous instances. The performance of the proposed system is evaluated on different datasets like NSL-KDD and Kyoto University benchmark dataset using various parameters like accuracy, the number of instances reduced, recall, precision, f1-score, t-value and execution time.