Internet of Drones (IoD) is a dynamic network architecture in which multiple drones, equipped with communication, sensing, and computation capabilities, are interconnected through Internet of Things (IoT) technologies to perform coordinated tasks autonomously. This infrastructure enables seamless real-time data exchange and collaborative operations across diverse applications, ranging from surveillance to delivery services, while ensuring adaptability, scalability, and security in dynamic aerial environments. However, the IoD introduces new security challenges, as drones are highly vulnerable to various cyberthreats and cyberattacks. Existing Intrusion Detection Systems (IDS) for IoD face several limitations, including high false positive rates, resource constraints of drones, limited adaptability to evolving attack patterns, and a lack of standardized datasets for benchmarking, despite ongoing research efforts. Moreover, there is a lack of a comprehensive study that systematically consolidates existing research. In this paper, we present a systematic literature review to examine the current research area of intrusion detection systems for IoD, focusing on the effectiveness of implemented machine learning models, employed datasets, existing challenges and limitations, as well as emerging trends and future research directions. This review follows PRISMA guidelines, with peer-reviewed journal articles and conference papers selected as the inclusion criteria. Publications relevant to the topic are sourced from a range of databases, including Scopus, IEEE Xplore, ScienceDirect, SpringerLink, ACMDigital Library, and MDPI, covering a 10-year period from 2014 to 2024. From an initial pool of 1,909 records, 62 relevant reports are selected to address the identified research questions. The selected studies are categorized according to publication year, venue, journal, drone domain, IDS type, utilized algorithms, datasets, attack classifications, and software environments. Additionally, a comparative analysis across various factors is presented.

The Internet of Drones (IoD) is increasingly adopted in mission-critical applications, but it remains vulnerable to a wide range of cyberattacks due to its open communication channels and resource-constrained nodes. Existing Intrusion Detection Systems (IDS) often neglect class imbalance and computational limitations, resulting in poor generalization and impractical deployment. This study aims to develop an efficient and deployable IDS framework for the IoD by addressing two key challenges: (i) the severe class imbalance in real-world traffic datasets, and (ii) the high computational complexity of Deep Learning (DL)-based models. We propose a two-phase methodology. In Phase 1, validated undersampling strategies are applied to rebalance the CIC-IoT2023 dataset, after which baseline classifiers are trained to assess detection performance. In Phase 2, the best-performing models serve as teacher networks in a multi-teacher knowledge distillation (MTKD) framework—implemented with average-weighted and confidence-weighted variants—to train lightweight Multi-Layer Perceptron (MLP) student models of varying capacities. These students are optimized for deployment on resource-constrained IoD edge devices. Effectiveness is assessed using both classification and computational efficiency metrics. Results show that Instance Hardness Threshold undersampling consistently outperformed Random Under Sampler across all baseline models, enhancing detection of minority attack classes. The best-performing models—LSTM and XGBoost—were selected as teachers for MTKD. Applying average-weighted and confidence-weighted MTKD to MLP1x8 increased accuracy by 0.03% and 0.09%, respectively, while confidence-weighted also reduced inference time by 0.0006 s. These gains were achieved without increasing the computational footprint, as the model retained its minimal size (0.0039 MB) and low parameter count (448). The proposed two-phase framework provides a practical solution for building lightweight and accurate IDS models suitable for real-time deployment in resource-constrained drone environments.

Resource allocation for cognitive Vehicle-to-Everything (V2X) networks is challenging due to dynamic spectrum sharing, strong interference coupling, and stringent latency constraints for safety-critical Vehicle-to-Vehicle (V2V) traffic. Although recent Multi-Agent Reinforcement Learning (MARL) approaches report promising gains, many evaluations are conducted at limited and fixed network scales, which restricts insights into scalability under dense spectrum reuse. This paper investigates cooperative multi-agent learning for interference-aware and deadline-constrained V2X resource management. We propose a Q-value Transformation (QTRAN)-based value decomposition framework under centralized training with decentralized execution (CTDE) for joint resource-block and power allocation among V2V agents. The proposed approach is implemented in a realistic V2V/V2I simulator incorporating Manhattan grid mobility, fast fading, explicit cross-tier and co-channel interference, and per-link payload/deadline dynamics. Beyond communication-level performance, improved timely delivery of V2V safety messages can support cooperative maneuvering, collision avoidance, platooning, and infrastructure-assisted traffic management. Extensive simulations across varying numbers of V2V agents benchmark QTRAN against independent learning baselines including MARL and centralized single-agent learning (SARL). Results show that QTRAN improves performance compared with the selected learning baselines and enhances the throughput–reliability trade-off under interference-coupled spectrum reuse. For instance, at $N_{\mathrm{V}2\mathrm{V}}=20$, QTRAN achieves a V2V rate of $0.194\pm 0.004$ and a V2I rate of $9.117\pm 0.213$, while reaching a V2V success rate of $0.812\pm 0.017$ with a low Deadline Miss Ratio of $0.001\pm 0.000$. At higher density ($N_{\mathrm{V}2\mathrm{V}}=50$), QTRAN sustains strong reliability (V2V success rate of $0.719\pm 0.006$ and Completion Ratio of $0.716\pm 0.006$) while maintaining competitive infrastructure throughput (V2I rate of $9.251\pm 0.114$). These results indicate that QTRAN effectively captures non-linear interference interactions, enabling coordinated decentralized spectrum and power decisions under the adopted density-based evaluation setting, thereby enhancing V2V reliability and throughput in cognitive Internet of Vehicles.