talk: Towards Contextual Security of AI-enabled platforms, 1-2 pm ET 3/22

Tue, 16 Mar 2021 21:29:37 -0400
ai, cybersecurity, events, machine-learning, news, talks

Towards Contextual Security of AI-enabled platforms

Dr. Nidhi Rastogi
Rensselaer Polytechnic Institute

1-2:00pm ET, Monday, 22 March 2021

via WebEx

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., and 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., and Ma, Q. (2021). TINKER: Knowledge graph for threat intelligence. In ACL- IJCNLP’21, Under Review.

Dr. Nidhi Rastogi is a Research Scientist at Rensselaer Polytechnic Institute. Her research is at the intersection of cybersecurity, artificial intelligence, large-scale networks, graph analytics, and data privacy. She has papers accepted at top venues such as USENIX, TrustCom, ISWC, Wireless Telecommunication Symposium, and Journal of Information Policy. For the past two years, Dr. Rastogi has been the lead PI for three cybersecurity, privacy research projects and a contributor to one healthcare AI project. For her contributions to cybersecurity and encouraging women in STEM, Dr. Rastogi was recognized in 2020 as an International Women in Cybersecurity by the Cyber Risk Research Institute. She was a speaker at the SANS cybersecurity summit and the Grace Hopper Conference. Dr. Rastogi is the co-chair for DYNAMICS workshop (2020-) and has served as a committee member for DYNAMICS’19, IEEE S&P’16 (student PC), invited reviewer for IEEE Transactions on Information Forensics and Cybersecurity (2018,19), FADEx laureate for the 1st French-American Program on Cyber-Physical Systems’16, Board Member (N2Women 2018-20), and Feature Editor for ACM XRDS Magazine (2015-17). Before her Ph.D. from RPI, Dr. Rastogi also worked in the industry on heterogeneous wireless networks (cellular, 802.1x, 802.11) and network security through engineering and research positions at Verizon and GE Global Research Center, and GE Power. She has interned at IBM Zurich, BBN Raytheon, GE GRC, and Yahoo, which provides her a quintessential perspective in applied industrial research and engineering.