Biblio

The increase of cyber attacks in both the numbers and varieties in recent years demands to build a more sophisticated network intrusion detection system (NIDS). These NIDS perform better when they can monitor all the traffic traversing through the network like when being deployed on a Software-Defined Network (SDN). Because of the inability to detect zero-day attacks, signature-based NIDS which were traditionally used for detecting malicious traffic are beginning to get replaced by anomaly-based NIDS built on neural networks. However, recently it has been shown that such NIDS have their own drawback namely being vulnerable to the adversarial example attack. Moreover, they were mostly evaluated on the old datasets which don't represent the variety of attacks network systems might face these days. In this paper, we present Reconstruction from Partial Observation (RePO) as a new mechanism to build an NIDS with the help of denoising autoencoders capable of detecting different types of network attacks in a low false alert setting with an enhanced robustness against adversarial example attack. Our evaluation conducted on a dataset with a variety of network attacks shows denoising autoencoders can improve detection of malicious traffic by up to 29% in a normal setting and by up to 45% in an adversarial setting compared to other recently proposed anomaly detectors.

As the worldwide internet has non-stop developments, it comes with enormous amount automatically generated malware. Those malware had become huge threaten to computer users. A comprehensive malware family classifier can help security researchers to quickly identify characteristics of malware which help malware analysts to investigate in more efficient way. However, despite the assistance of the artificial intelligent (AI) classifiers, it has been shown that the AI-based classifiers are vulnerable to so-called adversarial attacks. In this paper, we demonstrate how the adversarial settings can be applied to the classifier of malware families classification. Our experimental results achieved high successful rate through the adversarial attack. We also find the important features which are ignored by malware analysts but useful in the future analysis.