Unmanned aerial vehicles (UAVs) have gained significant attention in robotics research during the past decade, despite their presence dating back to 1915. Unmanned Aerial Vehicles (UAVs) are capable of efficiently and successfully carrying out a range of tasks. As a result, the use of many UAVs to fulfill a specific mission has grown into a popular area of research. Researchers have conducted investigations on the use of numerous UAVs in various fields such as remote sensing, disaster relief, force protection, military warfare, and surveillance. Efficiency and robustness are crucial factors for carrying out key operations. Multiple groups of UAVs, through appropriate interaction and concerted procedures, can achieve these objectives. The unpredictable features of UAVs and their reliance on unprotected and widely available wireless networks create challenges in establishing secure communication between a private edge cloud and a UAV. Consequently, secret UAV networks that utilize edge computing necessitate supplementary precautions to safeguard their networks. This research paper talks about a simple, lightweight, certificate-free, heterogeneous online/offline aggregate signing scheme called CL-PFASC. It comes from the discrete logarithm problem. The concert scheme enables UAVs to communicate with a GS without the need for a bilinear coupling operation. We classify the UAVs as identity-based cryptography (IBC) and the ground station GS as public-key infrastructure (PKI). We verify the security features of the suggested scheme using a formal security evaluation method, the random oracle model, under confidentiality and unforgeability. We also evaluate its communication and computation costs and compare them to those of similar existing schemes. The performance and security study indicate that the suggested approach improves both efficiency and security.

Unmanned aerial vehicles (UAVs) have become essential cyber-physical platforms for applications such as surveillance, infrastructure inspection, emergency response, and intelligent transportation. However, their tight coupling among sensing, communication, control, actuation, and swarm coordination also exposes them to sophisticated cyber-physical attacks that are difficult to detect using conventional intrusion detection systems. Existing machine learning, deep learning, graph-based, and federated intrusion detection approaches generally rely on statistical feature representations or temporal patterns, providing limited capability to model causal dependencies among interacting UAV subsystems and to generalize across heterogeneous operating environments. To address these limitations, this paper proposes Fed-CGIDS-UAV, a federated causal graph learning framework for cross-domain intrusion detection in cyber-physical UAV networks. The proposed framework models each telemetry window as a typed causal graph in which nodes represent navigation, sensing, communication, control, actuation, and swarm states, while directed edges capture stable operational dependencies. Intrusions are detected by identifying violations of these learned causal relationships, and the framework provides interpretable node-edge explanations to support root-cause analysis. Furthermore, federated learning enables collaborative model training across distributed UAV clients without sharing raw telemetry, thereby preserving data privacy while improving robustness under heterogeneous operating conditions. The proposed framework was implemented and experimentally evaluated in a controlled simulation environment covering four UAV operating domains and six representative attack classes. All experiments were repeated over five independent runs using different random seeds, and the reported results correspond to the measured average performance. The proposed framework was implemented using Python 3.12 (Python Software Foundation, Wilmington, DE, USA) and PyTorch 2.3 (Meta Platforms, Menlo Park, CA, USA). UAV flight data were generated using Microsoft AirSim 1.9.1 (Microsoft Corporation, Redmond, WA, USA), integrated with PX4 Autopilot v1.14 (Dronecode Foundation, San Francisco, CA, USA) and Gazebo Sim 11 (Open Source Robotics Foundation, Mountain View, CA, USA). Within this simulation-based evaluation, Fed-CGIDS-UAV achieved an accuracy of 0.968, an F1-score of 0.956, and an internal–external stability gap (IESG) of 0.028, outperforming conventional machine learning, deep learning, graph-based, and centralized causal baselines while maintaining competitive computational latency. Although these results demonstrate the effectiveness of the proposed framework under controlled simulation conditions, validation using real-flight UAV telemetry remains an important direction for future research. These results demonstrate that integrating causal graph learning with federated optimization provides an effective and interpretable solution for privacy-preserving intrusion detection in heterogeneous cyber-physical UAV environments.

Effective intrusion detection for Internet of Things (IoT) environments requires balancing predictive performance, resource efficiency, and interpretability—particularly in real-world deployments where traffic distributions and attack scenarios vary. While many studies report near-perfect detection on benchmark datasets, this often overlooks model stability under distribution shifts. This paper addresses this gap by introducing a stability-focused evaluation of lightweight, explainable intrusion detection models using compact IoT-23 scenarios and a constrained set of 14 connection-level features for interpretability. Four lightweight models—logistic regression, random forest, XGBoost, and LightGBM—are assessed within a unified pipeline. Beyond standard internal validation, we implement a strict cross-scenario evaluation framework featuring a fully unseen malware capture. Our proposed Internal–External Stability Gap (IESG) framework, enhanced with normalized and multi-metric measures, highlights the degradation in consistency between internal and external metrics. Surprisingly, even models with high internal F1 scores (up to 0.9994) may experience considerable drops in external macro-F1 and specificity, exposing weaknesses in conventional evaluation. Experimentally, LightGBM provides the best trade-off between performance and compactness (606 KB) and shows the smallest stability gap for malicious detection. Nevertheless, all models show reduced balanced performance under scenario shift, underscoring that deployment readiness hinges on stability under changing conditions. Feature ablation reveals that leveraging high-impact features, such as port information, can boost internal accuracy at the expense of generalization. In summary, we demonstrate that while lightweight models deliver strong detection, only those proven stable across scenarios are viable for real-world IoT intrusion detection. Our evaluation framework offers a practical, interpretable tool for assessing model robustness.

Preserving the privacy of sensitive data such as mercantile, medical, political or financial might utilize encryption, these encrypted data, in some cases, may be needed to be decrypted by other party, for this issue, the dual receiver encryption, DRE, scheme is constructed. Since the development of the first DRE and until now, several DRE schemes have been constructed, some of these schemes are based on bilinear pairings and others are lattice based. The lattice based DRE schemes have larger key size and does not corroborate the public verifiability whereas pairing based schemes suffers from higher computational cost. In the present work, we propose a construction of DRE without pairing to avoid the computational complexity problems. The proposed scheme is based on the decisional Diffie-Hellman security assumption. Our proposed scheme is secure under chosen cipher text attack. In addition, the suggested scheme achieves public verifiability and reduces computational complexity. In terms of computational complexity, our scheme saves about 53% compared to others with pairing in literature.

Cisco originated a term fog computing which decentralized computing process on the cloud central to be closer to IOT data. Fog computing provides an efficient data storage and processing. Fog computing still suffer from some security and privacy. Security problems solved by proxy re-encryption schemes but it suffers from heavy computational operations because of using so many paring operations in delegation and delegated sides. Parings operations causes heavy computations. This paper suggests a new hybrid proxy re-encryption scheme without parings using both the symmetric and asymmetric cryptography schemes. The security proof of the proposed scheme has been done under Indistinguishability-Chosen Plaintext Attack. The suggested scheme is highly computationally efficient when compared with other schemes in literature.

Dual‐receiver proxy re‐encryption is a cryptographic technique that enables secure data sharing among multiple authorized users or entities. It has gained significant attention for its ability to manage access permissions, data confidentiality, and streamline communication channels. These schemes have been widely used in various applications, including healthcare systems, cloud computing, Internet of Things (IoT) systems, collaborative environments, and secure communication channels. This paper aims to propose two proxy re‐encryption schemes for dual receivers without pairings. The first is dual receiver lightweight proxy re‐encryption without pairings (DR‐LWPRE‐WP), which uses a public key scheme to reduce computational complexity. The second is dual receiver hybrid proxy re‐encryption without pairings (DR‐HPRE‐WP), which incorporates public key and symmetric key schemes. Both schemes offer protection against selected plaintext attacks based on the decisional Diffie‐Hellman principle. The DR‐LWPRE‐WP scheme reduces computation by approximately 53% compared to pairing‐based schemes, making it suitable for lightweight applications like the Internet of Things. The computational efficacy of these schemes offers significant benefits for resource‐constrained environments and practical implementations.