Cooperative navigation of multiple unmanned aerial vehicles (UAVs) in disaster search-and-rescue scenarios is challenging due to dense obstacles, partial observability, and strong inter-agent coupling, which often result in path conflicts, collision risks, and limited policy generalization. To address these challenges, this paper proposes a Multi-Agent Deep Deterministic Policy Gradient framework with a Graph-Attention-based Staged Actor (GS-MADDPG). Under a centralized training and decentralized execution paradigm, GNNs are employed to model local interaction relationships among UAVs, enabling effective information aggregation and cooperative decision-making under partial observability. Furthermore, the Actor network is decomposed into perception, goal-guidance, and feature fusion subnetworks, allowing hierarchical decoupling and coordinated integration of local obstacle avoidance behaviors and global navigation objectives. Simulation results conducted in a complex three-dimensional urban environment demonstrate that, compared to traditional methods, GS-MADDPG improves the navigation success rate, robustness, and generalization performance. When the obstacle density reaches 50% and the number of UAVs increases from 2 to 10, the navigation success rate of GS-MADDPG is approximately 40% higher than that of the benchmark algorithm; even in cases with higher obstacle density, GS-MADDPG still achieves a relatively high success rate. This verifies its effectiveness in multi-UAV cooperative navigation for search and rescue tasks.
Li Tan, Hai-Xia Zhao, Jia-Qin Chai et al.· Unmanned Systems· 0 citations
Intrusion detection systems (IDSs) for industrial control networks are commonly evaluated using random stratified splits, placing rows from every recorded attack in both training and test sets. Although convenient, this practice measures a model’s ability to recognise repetitions of patterns it has already seen rather than its ability to detect novel attacks. We revisit supervised and unsupervised machine-learning IDSs on the Secure Water Treatment (SWaT) dataset’s network-traffic modality, extending a prior conference study, and quantify the effect of more rigorous evaluation protocols. We evaluate five model families (XGBoost, a convolutional–MLP hybrid, a bidirectional LSTM classifier, an unsupervised LSTM-Autoencoder, and a temporal convolutional network) under three protocols: stratified random, attack-held-out, and leave-one-attack-out (LOO). Under LOO on a 30-file subsample, every supervised classifier scores below random on the majority of held-out attacks; the unsupervised LSTM-Autoencoder retains the best solo mean of 0.550 with a strongly bimodal per-attack distribution spanning 0.046 to 0.894. A sign-adjusted oracle-bound ensemble flips members whose per-attack AUROC inverts achieves a mean LOO AUROC of 0.844; adding the TCN as a fourth ensemble member does not improve the result, providing evidence that what is needed is an additional detection mode rather than another supervised classifier. We additionally report recall at a 5% false-positive-rate budget, paired Wilcoxon significance tests, and bootstrap confidence intervals. The full preprocessing, evaluation, and ensemble pipeline is released, and we argue that attack-held-out and LOO should be standard protocols for network-traffic IDS benchmarks on SWaT.
Sebastian Mesca, Emil Pricop, G. Stamatescu· Applied Sciences· 1 citation
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
