The rapid development of Internet of Things (IoT) technology, together with mobile network technology, has created a never-before-seen world of interconnection, evoking research on how to make it vaster, faster, and safer. To support the ongoing fight against the malicious misuse of networks, in this paper we propose a novel algorithm called AMDES (unmanned aerial system multifractal analysis intrusion detection system) for spoofing attack detection. This novel algorithm is based on both wavelet leader multifractal analysis (WLM) and machine learning (ML) principles. In earlier research on unmanned aerial systems (UAS), intrusion detection systems (IDS) based on multifractal (MF) spectral analysis have been used to provide accurate MF spectrum estimations of network traffic. Such an estimation is then used to detect and characterize flooding anomalies that can be observed in an unmanned aerial vehicle (UAV) network. However, the previous contributions have lacked the consideration of other types of network intrusions commonly observed in UAS networks, such as the man in the middle attack (MITM). In this work, this promising methodology has been accommodated to detect a spoofing attack within a UAS. This methodology highlights a robust approach in terms of false positive performance in detecting intrusions in a UAS location reporting system.

High-precision onboard orbit propagation, often unavailable due to high demands of onboard resources, is crucial for autonomous optical link pointing and acquisition in large-scale low Earth orbit satellite constellations. For such large-scale constellations, an advantageous feature for efficient and accurate orbit propagation is the correlation or similarity in the orbit perturbations experienced by multiple satellites. Yet, this correlation has not been fully utilized in existing orbit propagation methods. In this work, we propose a cross-referential orbit propagation framework that leverages multiple historical reference arcs from other satellites within the same constellation to improve prediction accuracy and reliability. The framework achieves error reduction comparable to that of ensemble learning by aggregating the predictions from a single lightweight model under varying reference inputs, thereby preserving a simple and compact model architecture. To ensure the generalizability of this compact model, we further introduce a network architecture termed the Compressive Decoder for Orbit Propagation (CDOP). The CDOP predicts low-dimensional representations of the propagated orbits, from which the full time series are subsequently decoded. By incorporating modules from pre-trained compressive autoencoders, the CDOP mitigates overfitting while maintaining a low inference cost. The proposed method is validated on simulated Walker constellations with different geometries. The results demonstrate an average 24 h position error of approximately 200 m, with an inference cost 30 times lower than that of a reduced-dynamic numerical propagator. The framework is computationally lightweight, generalizes well across different initial conditions, and is well suited for onboard deployment in autonomous optical link acquisition.