talk: Contextual Security of AI-enabled platforms

The explosive growth of Internet-connected and AI-enabled devices and data produced by them has introduced significant threats. For example, malware intrusions (SolarWinds) have become perilous and extremely hard to discover, while data breaches continue to compromise user privacy (Zoom credentials exposed) and endanger personally identifiable information. My research takes a holistic approach towards systems and platforms to address security-related concerns using contextual and explainable models.

In this talk, I will present ongoing work that analyzes and improves the cybersecurity posture of Internet-connected systems and devices using automated, trustworthy, and contextual AI-models. Specifically, my research in malware threat intelligence gathers diverse information from varied datasets - system and network logs, source code, and text. In [1], an open-source ontology (MALOnt) contextualizes threat intelligence by aggregating malware-related information into classes and relations. TINKER [2, 3] – the first open-source malware knowledge graph, instantiates MALOnt classes and enables information extraction, reasoning, analysis, detection, classification, and cyber threat attribution. At present, the research is addressing the trustworthiness of information sources and extractors.

1. Rastogi, N., Dutta, S., Zaki, M. J., Gittens, A., & Aggarwal, C. (2020). MALOnt: An ontology for malware threat intelligence, In KDD’20 Workshop at International workshop on deployable machine learning for security defense. Springer, Cham.

2. Rastogi, N., Dutta, S., Christian, R., Gridley, J., Zaki, M. J., Gittens, A., & Aggarwal, C.  (2021). Knowledge graph generation and completion for contextual malware threat intelligence. In USENIX Security’21 Accepted.

3. Yee, D., Dutta, S., Rastogi, N., Gu, C., & Ma, Q. (2021). TINKER: Knowledge graph for threat intelligence. In ACL- IJCNLP’21 Under Review.