Intrusion Prediction and Detection with Deep Sequence Modeling

With the wide adoption of the internet and its applications in recent years, many antagonists have been exploiting information exchange for malicious activities. Intrusion detection and prevention systems are widely researched areas, rightly so being an integral part of network security. Adoption of IDSs and IPSs in networks have shown significant results while expanding research from software solutions to hardware-based solutions, promoting such defensive techniques even further. As with all recent computing trends, Machine Learning and Deep Learning techniques have become extremely prevalent in intrusion detection and prediction systems. There have been attempts to improve state of the art, but none is projecting any significant improvement over the current systems. Traditional systems alert the user after an intrusion has occurred, steps can be taken to stop further expansion of the intrusion, but in most cases, it is too late. Hence catering to this issue, this paper proposes system call prediction using a Recurrent Neural Network (RNNs) and Variational Autoencoding modelling techniques to predict sequences of system calls of a modern computer system. The proposed model makes use of ADFA intrusion dataset to learn long term sequences of system-call executed during an attack on a Linux based web server. The model can to effectively predict and classify sequences of system-calls most likely to occur during a known or unknown (zero-day) attacks.