Recently, the expansion of information technologies and the exponential increase of the digital data have deepened more the security and confidentiality issues in computer networks. In the Big Data era information security has become the main direction of scientific research and Big Data analytics is considered being the main tool in the solution of information security issue. Anomaly detection is one of the main issues in data analysis and used widely for detecting network threats. The potential sources of outliers can be noise and errors, events, and malicious attacks on the network. In this work, a short review of network anomaly detection methods is given, is looked at related works. In the article, a more exact and simple multi-classifier model is proposed for anomaly detection in network traffic based on Big Data. Experiments have been performed on the NSL-KDD data set by using the Weka. The offered model has shown decent results in terms of anomaly detection accuracy.
This study proposed multi-classifier model for increasing anomaly detection accuracy in network traffic. The model consists of the J48, LogitBoost, IBk, AdaBoost, RandomTree classifiers. This work performed a comparative analysis of used classifiers and their combination to see which one will give the best result In study classifiers and their combination have been implemented on NSL-KDD open source dataset using WEKA tool. The results show that the ensemble classifiers provide the better result than using these classifiers individually. The computer network traffic analysis with employment of our model can help network engineers and administrators to create a more reliable network, avoid possible discharges and take precautionary measures.
Anomaly detection, Big data analytics, Network security,
An ensemble of classifiers, IDS, Denial of service.