Fractal based cognitive neural network to detect obfuscated and indistinguishable internet threats

State of the art network intrusion detection systems are heavily influenced by signature based techniques for detecting threats which are extracted from raw packet captures and firewall logs. With the recent emergence of cloud computing and big data analytics, supervised machine learning is also being used to detect deviations of the network traffic patterns from already-known normal patterns. Subsequently, these anomalies are analyzed by human experts to differentiate legitimate anomalies, also known as true positives, from enormous false anomalies and reconfigure the machine learning system accordingly. Using machine learning for cyber security is relatively a difficult topic compared to other application domains primarily because of the dynamically fast changing threat landscape which is also extremely complex. Our main claim is that the proposed methodology significantly improves the classification performance of neural networks by detecting obfuscated malicious samples that masquerade the behavior of normal samples and thus are indistinguishable on Platonic Euclidean feature space. It is achieved by transforming a traditional single-scale (Euclidean scale) based error curve to information fractal dimension based multiscale error curve and subsequent design changes in the backpropagation algorithm. The performance comparison is provided by incorporating our proposed methodology in a fundamental gradient descent based neural network and shows promising results. Our claims are supported by experimental results and the subsequent analyses.