Hansheng Lei’s research while affiliated with The University of Texas Rio Grande Valley and other places

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Publications (2)


An illustrative example showing a graph (a) before an attack and (b) after an attack that maximizes the damage with a fixed budget. Gray nodes are the nodes that were attacked. Red nodes are ones disabled by failure propagation. Green nodes are still functional. (c) shows the graph after an attack that minimizes the cost with fixed damage output
Example graphs (left) and solution attack (right) for Barabási–Albert scale-free graphs using uniform distributions for weights
Example graphs (left) and solution attack (right) for Erdős–Rényi graphs using uniform distributions for weights
Change in the number of nodes attacked w.r.t. average node degree for Barabási–Albert scale free graphs using uniform distribution for weights. Solved for maximum damage based on a fixed budget
Change in the execution time w.r.t. average node degree for Barabási–Albert scale free graphs using uniform distribution for weights. Solved for maximum damage based on a fixed budget

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Determining critical nodes in optimal cost attacks on networked infrastructures
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January 2024

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64 Reads

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2 Citations

Discover Internet of Things

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Addison Clark

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Alex Aved

A wide range of critical infrastructures are connected via wide area networks as well as the Internet-of-Thing (IoT). Apart from natural disasters, these infrastructures, providing services such as electricity, water, gas, and Internet, are vulnerable to terrorist attacks. Clearly, damages to these infrastructures can have dire consequences on economics, health services, security and safety, and various business sectors. An infrastructure network can be represented as a directed graph in which nodes and edges denote operation entities and dependencies between entities, respectively. A knowledgeable attacker who plans to harm the system would aim to use the minimum amount of effort, cost, or resources to yield the maximum amount of damage. Their best strategy would be to attack the most critical nodes of the infrastructure. From the defender’s side, the strategy would be to minimize the potential damage by investing resources in bolstering the security of the critical nodes. Thus, in the struggle between the attacker and defender, it becomes important for both the attacker and defender to identify which nodes are most critically significant to the system. Identifying critical nodes is a complex optimization problem. In this paper, we first present the problem model and then propose a solution for computing the optimal cost attack while considering the failure propagation. The proposed model represents one or multiple interconnected infrastructures. While considering the attack cost of each node, the proposed method computes the optimal attack that a rational attacker would make. Our problem model simulates one of two goals: maximizing the damage for a given attack budget or minimizing the cost for a given amount of damage. Our technique obtains solutions to optimize the objective functions by utilizing integer-linear programming while observing the constraints for each of the specified goals. The paper reports an extensive set of experiments using various graphs. The results show the efficacy of our technique in terms of its ability to obtain solutions with fast turnaround times.

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A Consortium Blockchain-Based Approach for Energy Sharing in Distribution Systems

January 2024

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8 Reads

IEEE Transactions on Network and Service Management

Power network disruptions triggered by weather events, malfunction, sabotage, or other phenomena can leave harrowing effects on communities. Microgrids with distributed energy resources have the potential to enable swift localized restoration following the interruption of utility power. However, because of the limited generation and storage capacity of microgrids, service restoration will require prioritizing critical load and optimality of operations to render relief to those in dire need. Moreover, this essentially requires fair energy allocation, trust-free energy exchanges, and the integrity of transactions. This paper proposes a consortium blockchain-based energy sharing approach for service restoration using energy crowdsourced through donation or trade. The proposed approach provides a framework that utilizes microgrids’ supply situation and reputation as consortium admission criteria for optimizing the energy cost of blockchain operations. The proposed approach uses a measure called proof of welfare (PoWel), which solves the rationing problem to produce an energy allocation block. Accordingly, it proposes an algorithm by utilizing weighted rationing for prioritizing critical load restoration and an evolutionary optimization algorithm for maximizing social welfare and minimizing power losses. The winner block selected through consensus intrinsically preserves the network stability while conforming to resource and stability constraints. An extensive performance and security analysis ascertain the effectiveness of the proposed approach.