[go: up one dir, main page]
Wikipedia

Paulo Shakarian

Paulo Shakarian is an associate professor at Arizona State University where he leads Lab V2[1] which is focused on neurosymbolic artificial intelligence. His work on artificial intelligence and security has been featured in Forbes, the New Yorker, Slate, the Economist, Business Insider, TechCrunch, CNN and BBC.[2][3] He has authored numerous books on artificial intelligence and the intersection of AI and security. He previously served as a military officer, had experience at DARPA, and co-founded a startup.

Paulo Shakarian
PhD
NationalityAmerican
Alma materU.S. Military Academy, University of Maryland
Occupation(s)Associate professor, author
Known forArtificial intelligence, Cyber security
HonoursBronze Star, Army Commendation Medal

Scientific Work

edit

Current Work

edit

PyReason

edit

In 2023, Shakarian's group released PyReason[4] which is a modern implementation of annotated logic[5] with extensions to support temporal and open-world reasoning. PyReason was used in various collaborations with industry partners. This included work with SSCI where PyReason was used as a "semantic proxy" to replace a simulation for reinforcement learning[6] where it provides a 1000x speedup over native simulation environments for agent policy training and provided transfer of PyReason-trained policies to simulation environments such as AFSIM and SC2. PyReason was also demonstrated as a method for robotic control in a joint ASU-SSCI demonstration.[7] In a separate line of work, under the IARPA HAYSTAC program[8] PyReason was used in a strategy to generate movement trajectories using ideas from abductive inference.[9] Here the authors leveraged properties of logic programming and A* search to generate movement trajectories that met certain criteria but resembled past agent activity.

Earlier Work

edit

Social Network Diffusion

edit

In the 2012 paper “Large social networks can be targeted for viral marketing with small seed sets”,[10] Shakarian introduced a fast, novel method for identifying sets of nodes that can maximize the spread of a contagion in a social network based on the standard “tipping model.” The work was presented in a 2012 ASONAM paper (later extended in a 2013 journal SNAM[11] and described in a 2015 book published by Springer-Nature[12] ). The concept was based around a graph decomposition designed to mimic the inverse of the diffusion process. The work was featured as part of MIT Technology Review’s “Best of 2013” and heralded as solving a “fundamental problem of viral marketing.”[13]

AI for Predicting Hacker Actions

edit

In 2016, Shakarian’s team introduced a data mining framework in the paper “Darknet and deepnet mining for proactive cybersecurity threat intelligence” (Proc. IEE ISI 2016[14] and later described in a book published by Cambridge University press in 2017[15] ) which presented a framework for mining over 40 hacker websites – which not only demonstrated a scalable system for darkweb mining of hacker information, but also allowed for the ability to cross-examine cyber threat actors across multiple online forums – the study identified hundreds of hacker personas who participated in more than three different online marketplaces. The paper became one of the most cited papers of the history of the IEEE ISI conference and received media attention in Forbes[16] and MIT Technology Review.[17]

The following year, Shakarian and his team showed that data gathered from hacker communities on the dark web about specific software vulnerabilities often appeared before the use of zero-day exploits in a paper entitled “Proactive identification of exploits in the wild through vulnerability mentions online”.[18][19] They found that that this information could also be used to create features for machine learning approaches can successfully predict the use of exploits – even when accounting for temporal intermixing of data. The approach was enhanced with follow-on studies were the features were augmented using social network topology data (Proc. ACM CSS 2017[20] ) and the use of language models (Proc. AAAI 2018 [21]).

Career

edit

Shakarian was a major in the U.S Army serving from 2002 to 2014, undertaking two combat tours in Iraq and earning a Bronze Star and the Army Commendation Medal for valor.[3][22] While in the army he was trained in Information assurance and completed a bachelor's degree in computer science at the U.S. Military Academy.[2][22] In 2007 he served as a military fellow at Defense Advanced Research Projects Agency (DARPA). While in uniform, he went on to study a master's degree in computer science at the University of Maryland in 2009, and later a PhD in 2011 under the advisement of V.S. Subrahmanian.[23] His Ph.D. was focused on symbolic artificial intelligence, in particular logic programming, temporal logic, and abductive inference.[24]

After obtaining a PhD he taught at the U.S Military Academy, West Point, as an assistant professor from 2011 to 2014, his final military assignment.[25] In 2014 he took a position as an assistant professor at Arizona State University.[22] He earned his tenure at Arizona State and was promoted to Associate Professor in 2020.[26]

Since 2011 Shakarian has authored six books on subjects relating to his academic career - many of them focused on the intersection between AI, security, and data mining.[27]

In 2017, while maintaining his academic position he co-founded and led (as CEO) Cyber Reconnaissance, Inc., (CYR3CON), a business that specialized in combining artificial intelligence with information mined from malicious hacker communities to avoid cyber-attacks.[2] The company raised $8 million in venture capital[28] and was acquired in 2022.[29]

Notable works

edit

Books

edit
  • Neuro Symbolic Reasoning and Learning.[30]
  • Introduction to Cyber-Warfare: A Multidisciplinary Approach.[27]
  • Diffusion in Social Networks (SpringerBriefs in Computer Science).[27]
  • Darkweb Cyber Threat Intelligence Mining.[27]
  • Artificial Intelligence Tools for Cyber Attribution (SpringerBriefs in Computer Science).[27]
  • Cyber Warfare: Building the Scientific Foundation (Advances in Information Security).[27]
  • Geospatial Abduction: Principles and Practice.[27]

References

edit
  1. ^ "Lab V2". labs.engineering.asu.edu.
  2. ^ a b c CYR3CON.AI. "About". www.cyr3con.ai. Retrieved August 7, 2019.{{cite web}}: CS1 maint: numeric names: authors list (link)
  3. ^ a b "Paulo Shakarian". New America. Retrieved August 7, 2019.
  4. ^ Aditya, D. (2023). "PyReason: Software for Open World Temporal Logic". AAAI-Make. arXiv:2302.13482.
  5. ^ Kifer, Michael; Subrahmanian, V.S. (1992). "Theory of generalized annotated logic programming and its applications". Journal of Logic Programming. doi:10.1016/0743-1066(92)90007-P.
  6. ^ Mukherji, K.; Parkar, D.; Pokala, L.; Aditya, D.; Shakarian, P.; Dorman, C. (2024). "Scalable Semantic Non-Markovian Simulation Proxy for Reinforcement Learning". IEEE Icsc. arXiv:2310.06835.
  7. ^ "PyReason Sim-to-Real Demo". December 11, 2023 – via YouTube.
  8. ^ "HAYSTAC". www.iarpa.gov.
  9. ^ Bavikadi, D.; et al. (2024). "Geospatial Trajectory Generation via Efficient Abduction: Deployment for Independent Testing". 40th Intl. Conference on Logic Programming (ICLP).
  10. ^ Shakarian, P.; Paulo, D. (2012). "Large Social Networks Can be Targeted for Viral Marketing with Small Seed Sets". 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. pp. 1–8. arXiv:1205.4431. doi:10.1109/ASONAM.2012.11. ISBN 978-1-4673-2497-7.
  11. ^ Shakarian, Paulo; Eyre, Sean; Paulo, Damon (December 1, 2013). "A scalable heuristic for viral marketing under the tipping model". Social Network Analysis and Mining. 3 (4): 1225–1248. doi:10.1007/s13278-013-0135-7 – via Springer Link.
  12. ^ Diffusion in Social Networks. SpringerBriefs in Computer Science. 2015. doi:10.1007/978-3-319-23105-1. ISBN 978-3-319-23104-4 – via link.springer.com.
  13. ^ "Best of 2013: US Military Scientists Solve the Fundamental Problem of Viral Marketing". MIT Technology Review.
  14. ^ Nunes, Eric; Diab, Ahmad; Gunn, Andrew; Marin, Ericsson; Mishra, Vineet; Paliath, Vivin; Robertson, John; Shakarian, Jana; Thart, Amanda; Shakarian, Paulo (2016). "Darknet and deepnet mining for proactive cybersecurity threat intelligence". 2016 IEEE Conference on Intelligence and Security Informatics (ISI). pp. 7–12. arXiv:1607.08583. doi:10.1109/ISI.2016.7745435. ISBN 978-1-5090-3865-7.
  15. ^ Robertson, John; Diab, Ahmad; Marin, Ericsson; Nunes, Eric; Paliath, Vivin; Shakarian, Jana; Shakarian, Paulo (September 24, 2017). Darkweb Cyber Threat Intelligence Mining. Cambridge University Press. doi:10.1017/9781316888513. ISBN 978-1-107-18577-7.
  16. ^ Murnane, Kevin. "Machine Learning Goes Dark And Deep To Find Zero-Day Exploits Before Day Zero". Forbes.
  17. ^ "Machine-Learning Algorithm Combs the Darknet for Zero Day Exploits, and Finds Them". MIT Technology Review.
  18. ^ Almukaynizi, Mohammed; Nunes, Eric; Dharaiya, Krishna; Senguttuvan, Manoj; Shakarian, Jana; Shakarian, Paulo (2017). "Proactive identification of exploits in the wild through vulnerability mentions online". 2017 International Conference on Cyber Conflict (CyCon U.S.). pp. 82–88. doi:10.1109/CYCONUS.2017.8167501. ISBN 978-1-5386-2379-4.
  19. ^ "Google Scholar". scholar.google.com.
  20. ^ Almukaynizi, Mohammed; Grimm, Alexander; Nunes, Eric; Shakarian, Jana; Shakarian, Paulo (October 19, 2017). "Predicting Cyber Threats through Hacker Social Networks in Darkweb and Deepweb Forums". Proceedings of the 2017 International Conference of the Computational Social Science Society of the Americas. Association for Computing Machinery. pp. 1–7. doi:10.1145/3145574.3145590. ISBN 978-1-4503-5269-7 – via ACM Digital Library.
  21. ^ Tavabi, Nazgol; Goyal, Palash; Almukaynizi, Mohammed; Shakarian, Paulo; Lerman, Kristina (April 27, 2018). "DarkEmbed: Exploit Prediction with Neural Language Models". Proceedings of the AAAI Conference on Artificial Intelligence. 32 (1). doi:10.1609/aaai.v32i1.11428 – via ojs.aaai.org.
  22. ^ a b c "Paulo Shakarian | iSearch". isearch.asu.edu. Retrieved August 7, 2019.
  23. ^ "Home | VS Subrahmanian". vssubrah.github.io.
  24. ^ Shakarian, Paulo (2011). "Spatio-Temporal Reasoning about Agent Behavior". UMD dissertation.
  25. ^ "Coming Home: This West Point grad is using AI and Big Data for national security". Business Insider.
  26. ^ "Paulo Shakarian Associate Professor at Arizona State University". December 21, 2020.
  27. ^ a b c d e f g "Books by Paulo Shakarian". www.amazon.com. Retrieved August 7, 2019.
  28. ^ "AI Cyber Attack Prediction Platform CYR3CON Secures $8.2 Million Financing" (Press release). July 14, 2020.
  29. ^ "Albuquerque cybersecurity firm acquires Arizona machine learning startup".
  30. ^ Neuro Symbolic Reasoning and Learning.
edit
Retrieved from "https://en.wikipedia.org/w/index.php?title=Paulo_Shakarian&oldid=1253805763"