Kevin S. Chan | Xuanli Lin

Resource-Aware Intrusion Detection in Infrastructure Networks: A Game-Theoretic Approach

Fri, 07 Aug 2026 00:00:00 +0000

Abstract

Infrastructure networks increasingly rely on distributed sensing to detect intrusions before attackers reach valuable assets. Yet sensing devices, communication resources, and edge server capacity are limited, while intelligent attackers can adapt their routes to the deployed defense. Motivated by integrated sensing and communication (ISAC), we study how sensing and processing resources should be allocated under strategic interaction between a defender and an attacker. We formulate their interaction as a graph security game in which the defender deploys sensing actions under resource and false alarm constraints, while the attacker selects routes to valuable targets. We consider simultaneous play and settings in which the attacker observes either a pure defender configuration or a mixed defender strategy. Our analysis characterizes the existence, structure, and computational complexity of the Nash and Stackelberg equilibria, showing how the attacker’s observation of the defense affects equilibrium behavior and when optimal strategies become difficult to compute. We develop algorithms that construct effective pure configurations and refine restricted games for mixed Nash and mixed Stackelberg play. On enumerable instances, their solutions have small mean normalized differences from fully enumerated references; the methods also apply when exhaustive strategy enumeration is impractical. We also identify conditions under which Nash and mixed Stackelberg payoffs are ordered or coincide.

Preprint, first submitted 2026-08-07.

AEGIS: Throughput-Guaranteed Resilient Routing via a Conditional Value-at-Risk Approach

Thu, 16 Apr 2026 00:00:00 +0000

Abstract

The past decade has witnessed significant progress in next-generation wireless networks. Resilient routing is essential for maintaining reliability in mission-critical network services, particularly in dynamic and adversarial environments. Traditional traffic engineering (TE) approaches rely on pre-computed paths. Still, they face performance limitations when the number of pre-computed paths is small and scalability challenges when the number is large. This study seeks to answer the fundamental question: “How can we achieve throughput-guaranteed resilient routing under network failures without pre-computing routing paths?” We propose AEGIS, a novel throughput-guaranteed resilient routing scheme leveraging a conditional value-at-risk (CVaR) approach, which proactively guarantees the required throughput under normal conditions and enables recovery during network failures. Specifically, we propose an optimization problem that minimizes the CVaR of total throughput loss across all the failure situations while respecting user budget and network constraints. The above optimization problem is non-differentiable and non-linear; we then reformulate it as an equivalent linear program (LP) and develop an optimal solution. However, the above solution will induce cyclic flows due to resource reservation behaviors. To achieve a more resource-efficient routing, we propose a bisection approach to obtain a CVaR upper bound so that the corresponding routing is acyclic. Extensive numerical evaluations demonstrate the trade-offs among various approaches and highlight the advantages of AEGIS.

Multi-Hop Relay-Aided Task Offloading for Tactical Edge Computing

Mon, 06 Oct 2025 00:00:00 +0000

Abstract

Recent years have witnessed explosive growth in the deployment of IoT devices over tactical edge networks, which demand flexible task offloading strategies to accommodate limited connectivity and computing resources. While many existing works assume direct access to edge servers, such assumptions often break down in adversarial and dynamic environments. This work introduces a novel edge task offloading framework that enables multi-hop communication and enables task execution not only at edge servers but also at relay nodes and local devices. We formulate the problem of joint task offloading, resource allocation, and time frame/slot assignment, under a time-division multiple access (TDMA) uplink scheme, as a nonlinear integer program to maximize total rewards, subject to various communication and resource constraints. To solve it optimally, we first construct a feasibility-aware directed graph that captures valid communication links based on signal-to-noise ratio (SNR) constraints, and then design a dynamic programming (DP) algorithm that integrates slot-minimizing offloading path selection with the assignment of the largest feasible number of time frames for each user to reduce total time slots consumption without violating task completion deadlines. To improve scalability, efficient heuristics are developed using scaling and rounding techniques. Extensive experiments demonstrate that our proposed DP algorithm achieves optimal task offloading and resource allocation, while our heuristic algorithms offer a computationally efficient alternative with competitive performance.

Joint Optimization of Task Offloading and Resource Allocation in Tactical Edge Networks

Mon, 28 Oct 2024 00:00:00 +0000

Abstract

Recent years have witnessed explosive growth in deploying IoT devices over tactical edge networks. Due to the limited computing resources of local edge devices, there is an urgent need to offload the computing tasks to edge servers. A central question here is “How can we design offloading strategies and resource allocation plans so that all the computing resource constraints of servers and communication constraints between devices and servers are satisfied?” In this paper, we formulate the problem of jointly optimizing task offloading and resource allocation (JOA) in tactical edge networks as maximizing the total task completion rewards subject to various resource constraints. We design two algorithms to solve the JOA problem. The first is an exact algorithm using a search tree, where the branch-cutting criterion is well-crafted based on the property of the JOA problem. The second is a meta-heuristic algorithm based on the simulated annealing approach. We conduct numerical evaluations and demonstrate that the proposed exact algorithm can obtain the optimal task offloading strategy and resource allocation plan. The heuristic algorithm can efficiently provide competitive performance.