The communication link that holds a UAV swarm together is one of the priority targets in modern electronic warfare, because without it the swarm loses the collective behaviour that makes it useful in the first place; when the link fails, coordinated action can break down within seconds. This systematic review looks at three main layers of defence that the research community has developed: waveform-level protection through Frequency Hopping Spread Spectrum (FHSS) and Direct Sequence Spread Spectrum (DSSS), channel-based protection through Physical Layer Security (PLS), and adaptive AI-based cognitive radio, including multi-agent reinforcement learning, which lets swarms detect and react to jamming with less need for human intervention. These three layers are not really competing with each other - each one deals with a different part of the threat (resisting jamming, resisting interception, and adapting to a changing adversary) - and this review concentrates on the gaps in deployment readiness and technical integration between them, working toward a communication security architecture for military UAV swarms that is closer to being usable in practice. The main finding of the review is a cautious one: every approach discussed here shows measurable gains in simulation, but the field has not yet produced a standardized, layered security architecture that both scales to swarm size and can hold up against an adversary that is itself AI-capable.
Fraud conducted through chat applications has grown into a serious global threat affecting millions of people each year. Most detection systems still classify each message in isolation rather than reasoning over the conversation as a whole, which limits their ability to recognise the gradual manipulation that characterises social-engineering fraud. This paper presents StageGuard, a hierarchical deep-learning architecture that models the full trajectory of a fraudulent conversation. The framework builds on the Manipulation Stage Progression Model (MSPM), a five-stage taxonomy grounded in Cialdini's principles of psychological influence [4], in which offenders build trust gradually across turns before exploiting it. On this basis we define the Stage Transition Anomaly Score (STAS), a single-valued, human-readable feature that quantifies how abruptly a conversation moves between stages and serves as an interpretable indicator of manipulative intent. BERT-base-uncased encodes each turn, and the resulting turn embeddings are processed by a bidirectional LSTM [6], [19] with Bahdanau attention [7] that captures temporal dependencies across the conversation. The attention context vector is fused with the STAS and urgency features before a multi-layer classifier produces the fraud decision, and training uses a multi-task objective that jointly optimises fraud detection and per-turn stage classification. Under a leakage-free protocol in which messages are partitioned into disjoint train, validation and test pools before conversations are constructed, and on a realistic task in which both classes contain spam so that content alone is insufficient, StageGuard attains an F1-macro of 0.9919 and an ROC-AUC of 0.9998. The hierarchical design substantially outperforms flat baselines that treat the conversation as a single text (F1 up to 0.952); ablation and McNemar tests indicate that STAS does not change accuracy significantly, and its value is instead the per-turn interpretability it provides for human operators. We further report bootstrap confidence intervals, five-fold cross-validation and an external-domain evaluation that exposes a sharp generalisation gap, which we discuss candidly as a limitation.
This paper presents a technical and OSINT-based analysis of the German 8.8 cm FlaK (Flugzeugabwehrkanone) family of weapons, integrating engineering examination of all major weapon subsystems with a methodological demonstration of open-source intelligence techniques for historical weapons research. The study covers the complete weapon architecture - including the barrel and liner system, breech mechanism, recoil and recuperator assemblies, cruciform mount, and the layered optical fire-control hierarchy - as well as the ammunition family for the FlaK system. Analytical sections address terminal ballistics of both high-explosive (HE) and armor-piercing (AP) ammunition. A five-stage OSINT research workflow (scoping, discovery, verification, preservation, and synthesis) structures the entire investigation. All technical claims are traced to primary sources, principally TM E9-369A (1943), TM 9-1985-3 (1953), OP 1666 (1946), and BRL Report No. 517 (1944), supplemented by official museum records, Nuremberg trial transcripts, corporate histories, and Bundesarchiv archival holdings. The study concludes that the FlaK’s operational supremacy was a systems-engineering achievement, and that disciplined OSINT methodology, when combined with quantitative ballistic analysis, substantially strengthens technical-historical weapons research.
Unmanned Aerial Systems (UAS) are increasingly deployed in defense, security, and critical infrastructure missions where reliable communications and navigation are essential for operational effectiveness. The purpose of this paper is to analyze the convergence of cyber threats and electronic warfare against UAS and to develop a comparative security framework for improving communication resilience in defense-relevant operating environments. The study makes three explicit contributions: it constructs a structured threat taxonomy aligned with the cyber-electromagnetic threat space; it introduces an operationalized comparative assessment procedure using a five-criterion weighted scoring matrix; and it proposes a formalized state-based resilience framework with explicit indicator-driven transition thresholds. The research applies a theoretical-analytical and comparative method based on a synthesis of peer-reviewed literature, with primary applicability to small-to-medium tactical UAS relying on MAVLink-based communication in resource-constrained embedded environments. Key threats are categorized and examined, including man-in-the-middle attacks, command and code injection, denial-of-service operations, Global Navigation Satellite System (GNSS) spoofing, and radio-frequency (RF) jamming. Security postures are compared across five criteria using an explicit five-point scoring scale and criterion weights. The weighted composite scores indicate that adaptive and risk-informed approaches (3.65/5.00) show structurally stronger analytic alignment with hybrid threat conditions than layered defense models (2.90/5.00) or static architectures (2.20/5.00) within the defined framework. The major conclusion is that UAS communication security benefits from resilience-driven and adaptive design principles supported by structured comparative assessment and formalized state-transition logic, providing a practical basis for future implementation planning and policy development in contested electromagnetic and cyber environments.
Quantum communication and machine learning convergence is one of the promising directions in the development of communication systems that could be secure, efficient, and scalable. The modern communication networks employ quantum communication technologies, such as quantum key distribution (QKD), entangement, and teleportation, that can ensure high security and allow the transmission of reliable data. Combined with machine learning, these technologies can be used to process data better, provide better security, and perform better in many applications, including the Internet of Things (IoT), intelligent communication networks, health care, and artificial intelligence. This paper will provide a review of the key principles of quantum communication and the application of machine learning in communication systems, and how the two concepts can be applied to advantage. Besides that, the paper discusses critical technical problems, such as scalability, system integration, and the unavailability of standardization, that constrain the present application of hybrid quantum-classical systems. In addition, the future research directions are mentioned, specifically quantum-enhanced federated learning and the construction of a quantum internet. In general, this paper offers an analytical and systematic review of the field, highlighting the existing challenges, as well as the opportunities that may be explored in the future to establish next-generation intelligent communication systems.
