STPA-SafeSec: Safety and Security Analysis for Cyber-Physical Systems

Cyber-physical systems tightly integrate physical processes and information and communication technologies. As today’s critical infrastructures, e.g., the power grid or water distribution networks, are complex cyber-physical systems, ensuring their safety and security becomes of paramount importance. Traditional safety analysis methods, such as HAZOP, are ill-suited to assess these systems. Furthermore, cybersecurity vulnerabilities are often not considered critical, because their effects on the physical processes are not fully understood. In this work, we present STPA-SafeSec, a novel analysis methodology for both safety and security. Its results show the dependencies between cybersecurity vulnerabilities and system safety. Using this information, the most effective mitigation strategies to ensure safety and security of the system can be readily identified. We apply STPA-SafeSec to a use case in the power grid domain, and highlight its benefits.

Threat Analysis of BlackEnergy Malware for Synchrophasor based Real-time Control and Monitoring in Smart Grid

The BlackEnergy malware targeting critical infrastructures has a long history. It evolved over time from a simple DDoS platform to a quite sophisticated plug-in based malware. The plug-in architecture has a persistent malware core with easily installable attack specific modules for DDoS, spamming, info-stealing, remote access, boot-sector formatting etc. BlackEnergy has been involved in several high profile cyber physical attacks including the recent Ukraine power grid attack in December 2015. This paper investigates the evolution of BlackEnergy and its cyber attack capabilities. It presents a basic cyber attack model used by BlackEnergy for targeting industrial control systems. In particular, the paper analyzes cyber threats of BlackEnergy for synchrophasor based systems which are used for real-time control and monitoring functionalities in smart grid. Several BlackEnergy based attack scenarios have been investigated by exploiting the vulnerabilities in two widely used synchrophasor communication standards: (i) IEEE C37.118 and (ii) IEC 61850-90-5. Specifically, the paper addresses reconnaissance, DDoS, man-in-the-middle and replay/reflection attacks on IEEE C37.118 and IEC 61850-90-5. Further, the paper also investigates protection strategies for detection and prevention of BlackEnergy based cyber physical attacks.

Towards a threat assessment framework for apps collusion

App collusion refers to two or more apps working together to achieve a malicious goal that they otherwise would not be able to achieve individually. The permissions based security model (PBSM) for Android does not address this threat, as it is rather limited to mitigating risks due to individual apps. This paper presents a technique for assessing the threat of collusion for apps, which is a first step towards quantifying collusion risk, and allows us to narrow down to candidate apps for collusion, which is critical given the high volume of Android apps available. We present our empirical analysis using a classified corpus of over 29000 Android apps provided by Intel Security.

Dynalog: an automated dynamic analysis framework for characterizing android applications

Android is becoming ubiquitous and currently has the largest share of the mobile OS market with billions of application downloads from the official app market. It has also become the platform most targeted by mobile malware that are becoming more sophisticated to evade state-of-the-art detection approaches. Many Android malware families employ obfuscation techniques in order to avoid detection and this may defeat static analysis based approaches. Dynamic analysis on the other hand may be used to overcome this limitation. Hence in this paper we propose DynaLog, a dynamic analysis based framework for characterizing Android applications. The framework provides the capability to analyse the behaviour of applications based on an extensive number of dynamic features. It provides an automated platform for mass analysis and characterization of apps that is useful for quickly identifying and isolating malicious applications. The DynaLog framework leverages existing open source tools to extract and log high level behaviours, API calls, and critical events that can be used to explore the characteristics of an application, thus providing an extensible dynamic analysis platform for detecting Android malware. DynaLog is evaluated using real malware samples and clean applications demonstrating its capabilities for effective analysis and detection of malicious applications.