Anomaly detection is a common application of machine learning. Out-of-distribution (OOD) detection in particular is a semi-supervised anomaly detection technique where the detection method is trained only on the inlier (in-distribution) samples-unlike the fully supervised variant, the distribution of the outlier samples are never explicitly modeled in OOD detection tasks. In this work, we design a novel GAN-based OOD detection network specifically designed to protect a cyber-physical signal systems from novel Trojan malware called non-control data (NCD) attack that evades conventional malware detection techniques. Inspired in part by the classical locally most powerful (LMP) test in statistical inferences, the proposed LMP-GAN trains the OOD detector (discriminator) by generating OOD samples that are aimed at making maximal alteration to the inlier samples while evading detection. We experimentally compare the results to the state-of-the-art anomaly detection methods to demonstrate the benefits and the appropriateness of the LMP-GAN OOD detector.

The availability of sophisticated IT tools has provided computer system attackers with the capacity to develop dangerous metamorphic or polymorphic malware . Such malware presents behaviors similar to biological viruses enabling them to evade detection from conventional anti-malware methods. To circumvent this challenge, we carried out this research experiment to build and train machine learning models, and our proposed models achieved 99% & 99.6% accuracy in detecting and classifying PE-oriented malware. This excellent performance of our proposed models is undoubtedly a function of several factors but majorly on the synthesized DNA datasets (from our previous research works), which we used to train our proposed models. The synthesized DNA datasets are composed of salient features (dynamic and static) extracted from behaviors of nonmalicious programs (.exe, .dll, etc.) before and after infection by malware using reverse engineering and tracing tools. Then using a encoding algorithm and bioinformatics tools, we synthesized these features into a DNA-like representation (data sets) akin to the biological Deoxyribonucleic acid(DNA).

Software defined radio (SDR) provides significant advantages over traditional analog radio systems and are becoming increasingly relied on for ”mission critical” applications. This along with risk of trojans, single-event upsets and human error creates the necessity for fault tolerant systems. Redundancy has been traditionally used to implement fault tolerance but incurs a substantial area overhead which is undesirable in most applications. Advancements in field-programmable gate array and system on a chip technologies have made implementing machine learning (ML) algorithms within embedded systems feasible. In this paper we explore the use of ML to implement fault tolerance in an SDR. Our approach, which we call adaptive component-level degeneracy (ACD), uses a ML model to learn the functionality of an SDR component. Once trained, the model can detect when the component is compromised and mitigate the issue with its own output. We demonstrate the ability of our model to learn multiple simulated SDR components. We compare the one-dimensional convolutional neural network and bidirectional recurrent neural network architectures at modeling time series components. We also implement ACD within a real-time SDR system using GNU Radio Companion. The results show great potential for the utilization of ML techniques for improving embedded system reliability.