Scientific Work
Current Work
PyReason
In 2023, Shakarian's group released PyReason which is a modern implementation of annotated logic 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 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. In a separate line of work, under the IARPA HAYSTAC program PyReason was used in a strategy to generate movement trajectories using ideas from abductive inference. 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
Social Network Diffusion
In the 2012 paper “Large social networks can be targeted for viral marketing with small seed sets”, 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 and described in a 2015 book published by Springer-Nature ). 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.”AI for Predicting Hacker Actions
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 and later described in a book published by Cambridge University press in 2017 ) 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 and MIT Technology Review. 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”. They found 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 ) and the use of language models (Proc. AAAI 2018 ).Career
Shakarian was a major in the U.S Army serving from 2002 to 2014, undertaking two combat tours in Iraq and earning aNotable works
Books
* Neuro Symbolic Reasoning and Learning. * Introduction to Cyber-Warfare: A Multidisciplinary Approach. * Diffusion in Social Networks (SpringerBriefs in Computer Science). * Darkweb Cyber Threat Intelligence Mining. * Artificial Intelligence Tools for Cyber Attribution (SpringerBriefs in Computer Science). * Cyber Warfare: Building the Scientific Foundation (Advances in Information Security). * Geospatial Abduction: Principles and Practice.References
External links