Reliable drone video signal processing depends on precise identification of modulation type to ensure effective demodulation. Automatic modulation classification (AMC) plays a key role in this process by extracting meaningful features from complex I/Q data. Although deep learning-based approaches have advanced AMC, two challenges still remain: (i) limited support for drone-relevant modulation types and (ii) the need for stable, high-quality generative models for robust data augmentation. This letter proposes the adoption of diffusion transformers (DiT), which capture intricate signal characteristics in diverse drone communication scenarios, including long-range communications, mobile drone networks, and high data rate video transmission. Experimental results demonstrate that DiT improves both the accuracy and robustness of AMC in drone video signal processing scenarios.

As cyberattack operators have progressed to encompass group and nation-state levels, the nature of attacks has evolved into more sophisticated forms such as cyber campaigns. In response to these large-scale campaigns, tactical cyber threat intelligence (CTI) which focuses on tactics, techniques, and procedures (TTPs) has gained significant attention. However, the data-driven aspects of tactical CTI confront two primary challenges: (i) the extreme scarcity of campaign data and (ii) the difficulty of effectively integrating security domain knowledge. To this end, this paper presents MuCamp, a novel campaign generation method that operates in the context of limited campaign data while also considering the unique characteristics of large-scale attacks. The proposed method assumes that campaigns are TTP sequences, and based on this assumption, it generates valid campaign variants by replacing target TTP words withTTPsynonyms, and preserves the strategic goals of the seed campaigns. MuCamp offers a scalable and interpretable augmentation strategy, enhancing CTI effectiveness under data scarcity and facilitating rapid adaptation to evolving threat landscapes. We also prepared a dataset consisting of 858 real-world campaigns labeled by security experts, including 14 tactics and 206 techniques, enabling reliable performance evaluation. Experimental results demonstrate that each component of MuCamp contributes to embedding-based group attribution by improving the separability of the correct group from alternative candidates, while effectively reflecting domain knowledge.

Automatic modulation classification (AMC) has been envisioned as a significant element for security issues at the physical layer due to its indispensable role in accurate communications. Recent attention to deep learning has impacted the AMC, which exhibits exceptional performance without manual feature engineering. To guarantee the accuracy and robustness of deep learning-based AMC, data augmentation is a critical issue. While existing studies have used several deep generative models to handle the data insufficiency, these studies face three challenges including low scalability, lengthy training time, and limited accuracy improvement. To this end, this paper presents UniQGAN, a novel unified generative architecture that models I/Q constellation diagrams from various signal-to-noise ratios (SNRs) using a single model. The proposed method enables the generation of high-quality data with a scalable generator, while requiring reduced training time. At the core of UniQGAN aremulti-conditions embeddingandmulti-domains classificationtechniques that leverage both SNR and modulation type during the optimization process to enable unified modeling. Using abundant high-quality training data, UniQGAN accelerates the enhanced AMC with high performance and adversarial robustness. Experimental results demonstrate that the data generation by UniQGAN achieves superiority in terms of scalability, training time, and accuracy.