@Article{cmes.2026.083880,
AUTHOR = {Li-Woei Chen, Kun-Lin Tsai, Fang-Yie Leu, Chao-Tung Yang, Wei-Zong Liang},
TITLE = {A Lightweight Time-Indexed Secure Communication Framework with Intrusion Detection Modeling for Resource-Constrained UAV Swarm Networks},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {148},
YEAR = {2026},
NUMBER = {1},
PAGES = {0--0},
URL = {http://www.techscience.com/CMES/v148n1/68207},
ISSN = {1526-1506},
ABSTRACT = {Unmanned aerial vehicle (UAV) swarm networks are increasingly deployed in surveillance, disaster response, and intelligent transportation systems, where secure and efficient communication is critical under resource-constrained environments. However, conventional public-key-based security mechanisms introduce excessive computational overhead, while standalone intrusion detection systems are insufficient to defend against dynamic and multi-vector attacks in swarm networks. To address these challenges, in this paper, a lightweight time-indexed secure communication framework with intrusion detection modeling (TSCID) is proposed for resource-constrained UAV swarm networks. The proposed TSCID integrates a time-indexed session key derivation mechanism with lightweight authenticated encryption to ensure confidentiality, integrity, replay resistance, and session-key isolation with low computational cost. A security and communication-overhead analysis under standard symmetric-key cryptographic assumptions is conducted to evaluate the practicality and lightweight characteristics of the proposed framework. To enhance resilience against network-level attacks, an intrusion detection module based on deep neural networks is incorporated and optimized through structured pruning, enabling real-time anomaly detection on edge-class UAV devices. Experimental results demonstrate that TSCID reduces communication latency by up to 35% and energy consumption by nearly 30% compared with conventional public-key-based security mechanisms, while the lightweight intrusion detection model achieves over 92% detection accuracy with less than 4% false positives. Analytical and experimental results confirm that the proposed framework provides an efficient and secure solution for real-time UAV swarm communication under strict resource constraints.},
DOI = {10.32604/cmes.2026.083880}
}