The convergence of unmanned aerial systems, industrial control infrastructure, communication networks, and cyber-physical processes has created heterogeneous environments in which intrusion detection must operate across diverse traffic characteristics, computational constraints, and attack classes. Conventional intrusion detection approaches may struggle to maintain reliable discrimination when network conditions, device behavior, and attack distributions change. This paper proposes an adaptive ensemble-based intrusion detection framework integrating heterogeneous detection models, evolutionary optimization, explainable artificial intelligence (XAI), and context-sensitive decision fusion for drone and industrial cyber-physical systems. The theoretical foundation combines ensemble learning with genetic-algorithm-based optimization, motivated by established work on genetic algorithms and optimization-oriented communication-system design (Katoch, Chauhan and Kumar, 2021; Kramer and Kramer, 2017). The framework further incorporates adaptive model selection and explainability so that security operators can distinguish benign operational deviations from malicious activity. The directly related work on explainable ensemble intrusion detection in heterogeneous drone and industrial networks provides an important positioning reference for this research (Islam, Ahmed, Ishtiaq et al., 2026). The proposed methodology emphasizes multi-class detection, feature-level diversity, adaptive weighting, evolutionary optimization, and explanation consistency. Analytical findings indicate that the combination of complementary classifiers and adaptive weighting can theoretically address weaknesses associated with single-model detection, while XAI can improve operational interpretability. However, the framework also introduces computational, calibration, and explanation-consistency challenges. The study therefore positions adaptive ensemble detection as a promising architecture for security-sensitive cyber-physical environments where detection accuracy must be balanced against latency, computational cost, and human interpretability.  

Adaptive Ensemble Learning, Intrusion Detection, Explainable Artificial Intelligence, Drone Networks

Ebada, M., Elkelesh, A., Cammerer, S. and ten Brink, S. (2018, May) Scattered EXIT charts for finite length LDPC code design (In 2018 IEEE International Conference on Communications (ICC)), 1-7.

Elkelesh, A., Ebada, M., Cammerer, S., Schmalen, L. and Ten Brink, S. (2019) Decoder-in-the-loop: Genetic optimization-based LDPC code design (IEEE access), 7, 141161-141170.

Elkelesh, A., Ebada, M., Cammerer, S. and Ten Brink, S. (2019) Decoder-tailored polar code design using the genetic algorithm (IEEE Transactions on Communications), 67(7), 4521-4534.

Eremia, M., Liu, C. C. and Edris, A. A. (2016) Advanced solutions in power systems: HVDC, FACTS, and Artificial Intelligence (John Wiley & Sons).

Ferraz, O., Subramaniyan, S., Chinthala, R., Andrade, J., Cavallaro, J. R., Nandy, S. K. and Falcao, G. (2021) A survey on high-throughput non-binary LDPC decoders: ASIC, FPGA, and GPU architectures (IEEE Communications Surveys & Tutorials), 24(1), 524-556.

Katoch, S., Chauhan, S. S. and Kumar, V. (2021) A review on genetic algorithm: past, present, and future (Multimedia tools and applications), 80, 8091-8126.

Kramer, O. and Kramer, O. (2017) Genetic algorithms (Springer International Publishing).

Rao, K. D. (2015) Channel coding techniques for wireless communications (Berlin, Germany: Springer India).

Shao, S., Hailes, P., Wang, T. Y., Wu, J. Y., Maunder, R. G., Al-Hashimi, B. M. and Hanzo, L. (2019) Survey of turbo, LDPC, and polar decoder ASIC implementations (IEEE Communications Surveys & Tutorials), 21(3), 2309-2333.

Van Wonterghem, J., Alloum, A., Boutros, J. J. and Moeneclaey, M. (2016, November) Performance comparison of short-length error-correcting codes (In 2016 Symposium on Communications and Vehicular Technologies (SCVT)), 1-6.

Wijekoon, V. B., Viterbo, E., Hong, Y., Micheloni, R. and Marelli, A. (2019) A novel graph expansion and a decoding algorithm for NB-LDPC codes (IEEE Transactions on Communications), 68(3), 1358-1369.

Zhou, X., and Shi, W. (2024) Research on the optimisation of whitelisting technology for network firewall in industrial control system using genetic algorithm (International Journal of Communication Networks and Distributed Systems), 30(1), 30-41.

Islam, M.S., Ahmed, F., Ishtiaq, W. et al. Advanced explainable ensemble models for multi-class intrusion detection in heterogeneous drone and industrial networks. J. Inf. Secur. 2026, 14 (2026).

K. Ramamurthy, N. Bellamkonda and N. Amanmadov, "ScalePulse: Combinatorial Llm Framework for Scalability Constraint," SoutheastCon 2026, Huntsville, AL, USA, 2026, pp. 1-6, doi: 10.1109/SoutheastCon63549.2026.11476075

Philip, P. G. (2025). Explainable Artificial Intelligence (XAI) for Project Governance: Improving Transparency and Stakeholder Trust in Automated Project Decision. Journal of Project Management Studies, 2(1), 37–55. https://doi.org/10.58425/jpms.v2i1.570