Benjamin W. K. Hung

Optimization-based influencing of village social networks in a counterinsurgency

This paper considers the nonlethal targeting assignment problem in the counterinsurgency in Afghanistan, the problem of deciding on the people whom US forces should engage through outreach, negotiations, meetings, and other interactions in order to ultimately win the support of the population in their area of operations. We propose two models: 1) the Afghan COIN social influence model, to represent how attitudes of local leaders are affected by repeated interactions with other local leaders, insurgents, and counterinsurgents, and 2) the nonlethal targeting model, a nonlinear programming (NLP) optimization formulation that identifies a strategy for assigning k US agents to produce the greatest arithmetic mean of the expected long-term attitude of the population. We demonstrate in an experiment the merits of the optimization model in nonlethal targeting, which performs significantly better than both doctrine-based and random methods of assignment in a large network.

Investigative simulation: towards utilizing graph pattern matching for investigative search

This paper proposes the use of graph pattern matching for investigative graph search, which is the process of searching for and prioritizing persons of interest who may exhibit part or all of a pattern of suspicious behaviors or connections. While there are a variety of applications, our principal motivation is to aid law enforcement in the detection of homegrown violent extremists. We introduce investigative simulation, which consists of several necessary extensions to the existing dual simulation graph pattern matching scheme in order to make it appropriate for intelligence analysts and law enforcement officials. Specifically, we impose a categorical label structure on nodes consistent with the nature of indicators in investigations, as well as prune or complete search results to ensure sensibility and usefulness of partial matches to analysts. Lastly, we introduce a natural top-k ranking scheme that can help analysts prioritize investigative efforts. We demonstrate performance of investigative simulation on a real-world large dataset.

Detecting radicalization trajectories using graph pattern matching algorithms

This paper outlines our on-going efforts to address the radicalization detection problem, the automated or semi-automated task of dynamically detecting and tracking behavioral changes in individuals who undergo the process of increasingly espousing jihadist beliefs and transition to the use of violent action in support of those beliefs. Leveraging the notion that personal trajectories towards violent radicalization exist, we take a graph pattern matching approach to track individual-level indicators using data fused from available public and government/law enforcement databases. We show that our approach provides analysts with the ability to find full or partial matches against a query pattern of radicalization, and a means to quantify the pace of the appearance of the indicators that may help prioritize investigative efforts and resources to prevent planned attacks.

Pattern Matching Trajectories for Investigative Graph Searches

Investigative graph search is the process of searching for and prioritizing entities of interest that may exhibit part or all of a pattern of attributes or connections for a latent behavior. In this work we formulate a related sub-problem of determining the pattern matching trajectories of such entities. The goal is to not only provide analysts with the ability to find full or partial matches against a query pattern, but also a means to quantify the pace of the appearance of the indicators. This technology has a variety of potential applications such as aiding in the detection of homegrown violent extremists before they carry out acts of domestic terrorism, detecting signs for post-traumatic stress in veterans, or tracking potential customer activities and experiences along a consumer journey. We propose a vectorized graph pattern matching approach that calculates the multi-hop class similarities between nodes in query and data graphs over time. By tracking partial match trajectories, we provide another dimension of analysis in investigative graph searches to highlight entities on a pathway towards a pattern of a latent behavior. We demonstrate the performance of our approach on a real-world BlogCatalog dataset of over 470K nodes and 4 million edges, where 98.56% of nodes and 99.65% of edges were filtered out with preprocessing steps, and successfully detected the trajectory of the top 1,327 nodes towards a query pattern.

Simulation of auto design performance in the market to meet fuel efficiency standards

This research effort demonstrates an analysis and simulation methodology a manufacturer could employ to determine the recommended vehicle architecture that maximizes profit, while complying with government corporate average fuel economy (CAFE) standards. We scope our target system to a Jeep multipurpose vehicle (MPV) segment by Fiat Chrysler, which is a crucial part of the manufacturers product portfolio. We utilize multi-criteria decision analysis (MCDA) using the criteria of cost, acceptance, effectiveness, fuel type, and electrical factor to rank many possible fuel efficient technology configurations. Employing discrete event simulation, a select group of configurations are analyzed for profitability based upon market acceptance and economic performance. The discrete event simulation studies the expected profit distribution with variability in market acceptance and cost categories of production, research and development, warranty, and recall. This variability is treated as a risk and coupled with profit level and technology effectiveness lead to a final recommendation.

INSiGHT: A system to detect violent extremist radicalization trajectories in dynamic graphs

Abstract The number and lethality of violent extremist plots motivated by the Salafi-jihadist ideology have been growing for nearly the last decade in many parts of the world including both the U.S and Western Europe. While detecting the radicalization of violent extremists is a key component in preventing future terrorist attacks, it remains a significant challenge to law enforcement due to the issues of both scale and dynamics. We propose the development of a radicalization trend detection system as a risk assessment assistance technology that relies on data mined from public data and government databases for individuals who exhibit risk indicators for extremist violence, and enables law enforcement to monitor those individuals at the scope and scale that is lawful, and accounts for the dynamic indicative behaviors of the individuals and their associates rigorously and automatically. We frame our approach to monitoring the radicalization pattern of behaviors as a unique dynamic graph pattern matching problem, and develop a technology called INSiGHT ( In vestigative S earch for G rap h - T rajectories) to help identify individuals or small groups with conforming subgraphs to a radicalization query pattern, and follow the match trajectories over time. This paper presents the overall INSiGHT architecture and is aimed at assisting law enforcement and intelligence agencies in monitoring and screening for those individuals whose behaviors indicate a significant risk for violence, and allow for the better prioritization of limited investigative resources. We demonstrated the performance of INSiGHT on a variety of datasets, to include small synthetic radicalization-specific datasets and a real behavioral dataset of time-stamped radicalization indicators of recent U.S. violent extremists.

INSiGHT: A system for detecting radicalization trajectories in large heterogeneous graphs

Detecting the radicalization of violent extremists is a key component in preventing future terrorist attacks, but it remains a significant challenge to law enforcement. Framing our approach as a unique dynamic graph pattern matching problem, we address this challenge by introducing an analyst-in-the-loop framework and related technology called INSiGHT (Investigative Search for Graph-Trajectories) to help identify individuals or small groups with conforming subgraphs to a radicalization query pattern, and follow the match trajectories over time. INSiGHT is aimed at developing tools for assisting law enforcement and intelligence agencies in monitoring and screening for those individuals whose behaviors indicate a significant risk for violence, and allow for the better prioritization of limited investigative resources. We demonstrate the validity of INSiGHT on two small synthetic datasets, and confirm that we can scale to and provide consistent results for a large, real world proxy dataset of over 470K nodes and 4 million edges.