Dr. Benjamin Miller of MIT Lincoln Laboratory

Identifying shortest paths between nodes in a network is a common graph analysis problem that is important for many applications involving routing of resources. An adversary that can manipulate the graph structure could alter traffic patterns to gain some benefit (e.g., make more money by directing traffic to a toll road). This presentation considers a recently published attack in which an adversary removes edges from a graph to make a particular path the shortest between its terminal nodes. We develop a defense against such attacks by modifying the weights of the graph that users observe. The defender must balance inhibiting the attacker against any negative effects of the defense on benign users. Specifically, the defender's goals are: (a) to recommend the shortest paths possible to users, (b) for the lengths of the shortest paths in the published graph to be close to those of the same paths in the true graph, and (c) to minimize the probability of an attack. We formulate the defense as a Stackelberg game in which the defender is the leader and the attacker is the follower. In this context, we also consider a zero-sum version of the game, in which the defender's goal is to minimize cost while achieving the minimum possible attack probability. We show that this problem is NP-hard and propose heuristic solutions based on increasing edge weights along target paths in both the zero-sum and non-zero-sum settings. We present defense results with both synthetic and real network datasets and show that these methods often reach the lower bound of the defender's cost.

Biography:

Dr. Benjamin A. Miller is a technical staff member in the Artificial Intelligence Technology and Systems Group. His research is focused on the development and application of statistical signal processing and machine learning algorithms to large, combinatorial, heterogeneous datasets.

In 2005, Ben joined MIT Lincoln Laboratory as an associate staff member in the Embedded Digital Systems (later Embedded and High Performance Computing) Group. In this role, he developed novel algorithms for real-time linearization of radio-frequency electronics, researched methods and models for signal recovery in multisensor compressed sensing, and developed efficient spectral techniques for the detection of anomalies in large graphs. In 2012, he became a technical staff member in the Computing and Analytics Group, where he continued his research on the theoretical and computational aspects of anomaly detection in large networks and other dynamic, combinatorial structures. In 2015, Ben joined the Cyber Analytics and Decision Systems Group and focused his research on machine learning in the context of cyber security. He was admitted to the Lincoln Scholars Program in 2018 to pursue a PhD with a focus on robust artificial intelligence applied to complex network data.

Ben is a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE) and the Association for Computing Machinery, and is an active member of the Society for Industrial and Applied Mathematics and the IEEE Signal Processing Society. He holds seven patents and is author or coauthor of 49 peer-reviewed conference, workshop, and journal papers on nonlinear signal processing, compressive sensing, and graph mining and analytics.

Ben received the PhD degree in Network Science from Northeastern University in 2023. He received the BS degree (with highest honors) and the MS degree in Computer Science in 2005 from the University of Illinois at Urbana-Champaign.