A Deep Learning Based Anomaly Detection Approach for Intelligent Autonomous Systems
Primary Investigator:
Bharat Bhargava
Miguel Villarreal-Vasquez, Bharat Bhargava
Abstract
Advanced modern exploits characterize by their sophistication in stealthy attacks. Code-reuse attacks such as return-oriented programming and memory disclosure attacks allow attackers executing malicious instruction sequences on victim systems without injecting external code.
This research proposes a new Deep Learning based anomaly detection technique that probabilistically models program control flows for behavioral reasoning and live monitoring.
We aim to answer the binary classification problem of given a sequence of function calls whether or not the sequence should occur? The models are built with Recurrent Neural Networks (RNN) such as Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) implemented on top of PyTorch.