The Internet of Drones (IoD) integrates autonomous aerial platforms with security, logistics, agriculture, and disaster relief. Decision-making in IoD suffers in real-time adaptability, platform interoperability, and scalability. Conventional decision frameworks with heuristic algorithms and narrow Artificial Intelligence (AI) falter in complex environments. To mitigate these, in this study, an augmented decision model is proposed, combining large language models (LLMs) and retrieval-augmented generation (RAG) for enhancing IoD intelligence. Centralized intelligence is achieved by processing environment factors, mission logs, and telemetry, with real-time adaptability. Efficient retrieval of contextual information through RAG is merged with LLMs for timely, correct decision-making. Contextualized decision-making vastly improves adaptability in uncertain environments for a drone network. With LLMs and RAG, the model introduces a scalable, adaptable IoD operations solution. It enables the development of autonomous aerial platforms in industries, with future work in computational efficiency, ethics, and extending operational environments. In-depth analysis with the collection of drone telemetry logs and operational factors was conducted. Decision accuracy, response time, and contextual relevance were measured to gauge system effectiveness. The model’s performance increased remarkably, with a BLEU of 0.82 and a cosine similarity of 0.87, proving its effectiveness for operational commands. Decision latency averaged 120 milliseconds, proving its suitability for real-time IoD use cases.

By identifying and responding to any malicious behavior that could endanger the system, the Intrusion Detection System (IDS) is crucial for preserving the security of the Industrial Internet of Things (IIoT) network. The benefit of anomaly-based IDS is that they are able to recognize zero-day attacks due to the fact that they do not rely on a signature database to identify abnormal activity. In order to improve control over datasets and the process, this study proposes using an automated machine learning (AutoML) technique to automate the machine learning processes for IDS. Our ground-breaking architecture, known as AID4I, makes use of automatic machine learning methods for intrusion detection. Through automation of preprocessing, feature selection, model selection, and hyperparameter tuning, the objective is to identify an appropriate machine learning model for intrusion detection. Experimental studies demonstrate that the AID4I framework successfully proposes a suitable model. The integrity, security, and confidentiality of data transmitted across the IIoT network can be ensured by automating machine learning processes in the IDS to enhance its capacity to identify and stop threatening activities. With a comprehensive solution that takes advantage of the latest advances in automated machine learning methods to improve network security, AID4I is a powerful and effective instrument for intrusion detection. In preprocessing module, three distinct imputation methods are utilized to handle missing data, ensuring the robustness of the intrusion detection system in the presence of incomplete information. Feature selection module adopts a hybrid approach that combines Shapley values and genetic algorithm. The Parameter Optimization module encompasses a diverse set of 14 classification methods, allowing for thorough exploration and optimization of the parameters associated with each algorithm. By carefully tuning these parameters, the framework enhances its adaptability and accuracy in identifying potential intrusions. Experimental results demonstrate that the AID4I framework can achieve high levels of accuracy in detecting network intrusions up to 14.39% on public datasets, outperforming traditional intrusion detection methods while concurrently reducing the elapsed time for training and testing.

The increasing sophistication of cyberattacks makes Intrusion Detection Systems (IDSs) essential, yet the high dimensionality of modern network traffic hinders accuracy and efficiency. We conduct a comparative study of multi-objective feature selection for IDS using four bio-inspired metaheuristics—Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO)—on the X-IIoTID dataset. GA achieved the highest accuracy (99.60%) with the lowest FPR (0.39%) using 34 features. GWO offered the best accuracy–subset balance, reaching 99.50% accuracy with 22 features (65.08% reduction) within 0.10 percentage points of GA while using ~35% fewer features. PSO delivered competitive performance with 99.58% accuracy, 32 features (49.21% reduction), FPR 0.40%, and FNR 0.44%. ACO was the fastest (total training time 3001 s) and produced the smallest subset (7 features; 88.89% reduction), at an accuracy of 97.65% (FPR 2.30%, FNR 2.40%). These results delineate clear trade-off regions of high accuracy (GA/PSO/GWO), balanced (GWO), and efficiency-oriented (ACO) and underscore that algorithm choice should align with deployment constraints (e.g., edge vs. enterprise vs. cloud). We selected this quartet because it spans distinct search paradigms (hierarchical hunting, evolutionary recombination, social swarming, pheromone-guided foraging) commonly used in IDS feature selection, aiming for a representative, reproducible comparison rather than exhaustiveness; extending to additional bio-inspired and hybrid methods is left for future work.

Industrial Internet of Things (IIoT) is a rapidly growing field, where interconnected devices and systems are used to improve operational efficiency and productivity. However, the extensive connectivity and data exchange in the IIoT environment make it vulnerable to cyberattacks. Intrusion...