Forensic Attacks Analysis and the Cyber Security of Safety-Critical Industrial Control Systems

Industrial Control Systems (ICS) and SCADA (Supervisory Control And Data Acquisition) applications monitor and control a wide range of safety-related functions. These include energy generation where failures could have significant, irreversible consequences. They also include the control systems that are used in the manufacture of safety-related products. In this case bugs in an ICS/SCADA system could introduce flaws in the production of components that remain undetected before being incorporated into safety-related applications. Industrial Control Systems, typically, use devices and networks that are very different from conventional IP-based infrastructures. These differences prevent the re-use of existing cyber-security products in ICS/SCADA environments; the architectures, file formats and process structures are very different. This paper supports the forensic analysis of industrial control systems in safety-related applications. In particular, we describe how forensic attack analysis is used to identify weaknesses in devices so that we can both protect components but also determine the information that must be analyzed during the aftermath of a cyber-incident. Simulated attacks detect vulnerabilities; a risk-based approach can then be used to assess the likelihood and impact of any breach. These risk assessments are then used to justify both immediate and longer-term countermeasures

Robustness of Image-Based Malware Analysis

In previous work, “gist descriptor” features extracted from images have been used in malware classification problems and have shown promising results. In this research, we determine whether gist descriptors are robust with respect to malware obfuscation techniques, as compared to Convolutional Neural Networks (CNN) trained directly on malware images. Using the Python Image Library (PIL), we create images from malware executables and from malware that we obfuscate. We conduct experiments to compare classifying these images with a CNN as opposed to extracting the gist descriptor features from these images to use in classification. For the gist descriptors, we consider a variety of classification algorithms including k-nearest neighbors, random forest, support vector machine, and multi-layer perceptron. We find that gist descriptors are more robust than CNNs, with respect to the obfuscation techniques that we consider