Abstract. The underground malware-based economy is flourishing and it is evident that the classical ad-hoc signature detection methods are becoming insufficient. Malware authors seem to share some source code and malware samples often feature similar behaviors, but such commonalities are difficult to detect with signature-based methods because of an increasing use of numerous freelyavailable randomized obfuscation tools. To address this problem, the security community is actively researching behavioral detection methods that commonly attempt to understand and differentiate how malware behaves, as opposed to just detecting syntactic patterns. We continue that line of research in this paper and explore how formal methods and tools of the verification trade could be used for malware detection and analysis. We propose a new approach to learning and generalizing from observed malware behaviors based on tree automata inference. In particular, we develop an algorithm for inferring k-testable tree automata from system call dataflow dependency graphs and discuss the use of inferred automata in malware recognition and classification.

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A security analyst often needs to understand two runs of the same program that exhibit a difference in program state or output. This is important, for example, for vulnerability analysis, as well as for analyzing a malware program that features different behaviors when run in different environments. In this paper we propose a differential slicing approach that automates the analysis of such execution differences. Differential slicing outputs a causal difference graph that captures the input differences that triggered the observed difference and the causal path of differences that led from those input differences to the observed difference. The analyst uses the graph to quickly understand the observed difference. We implement differential slicing and evaluate it on the analysis of 11 real-world vulnerabilities and 2 malware samples with environment-dependent behaviors. We also evaluate it in an informal user study with two vulnerability analysts. Our results show that differential slicing successfully identifies the input differences that caused the observed difference and that the causal difference graph significantly reduces the amount of time and effort required for an analyst to understand the observed difference.

As online education continues to expand across varied educational sectors, so does the demand for professional development programs to guide academic teaching staff through the processes of developing their capacities to design and teach online courses. To meet these challenges at one higher education institution, a mixed methods research study was implemented to identify the professional learning needs of academic teaching staff for the purposes of developing a tailor-made professional development program. The principles of self-efficacy and threshold concepts were used to inform the design of the study. Data were systematically gathered from the participants to determine self-efficacy, concerns, and questions and experiences of academic teaching staff with online teaching. Findings revealed that academic staff held threshold concepts, skills and attitudes about online teaching. Three groups of staff were identified, all with varying forms of professional development requirements. This case study account demonstrates how an evidence-based project provided the basis for a researchinformed institutional professional development program that is currently guiding academic staff through their development as online course designers and teachers.

We explore how formal methods and tools of the verification trade could be used for malware detection and analysis. In particular, we propose a new approach to learning and generalizing from observed malware behaviors based on tree automata inference. Our approach infers k-testable tree automata from system call dataflow dependency graphs. We show how inferred automata can be used for malware recognition and classification.