Zero-knowledge proofs (ZKPs) enable a prover to convince a verifier of knowledge of a secret without revealing it. The ZKP for the square-root problem has many applications in network and cloud security, such as user authentication and privacy-preserving cloud storage auditing. Classical protocols for the quadratic residuosity (square-root) relation require multiple iterations to reach negligible soundness error, incurring latency and communication costs that are critical in cloud settings. This paper proposes a new single-round zero-knowledge proof (SR-ZKP) for the square-root problem that achieves the same soundness as iterative schemes by increasing the challenge length. The protocol requires only one execution of a 4-message protocol (request, commit, challenge, response) and can be transformed into a one-message non-interactive ZKP via the Fiat-Shamir heuristic. The completeness, soundness, and zero-knowledge properties of the proposed scheme are formally proven. The results of this study show that the proposed protocol can achieve approximately 97% reduction in communication overhead and latency, when compared to an 80-round iterative ZKPs with RSA modulus.. of size 2048 bits. This provides a substantial advantage for cloud applications.
Evasive poisoning attacks such as semantic backdoors pose a growing threat to federated learning because they mimic benign client updates and evade detectors under secure aggregation. We introduce an unsupervised, per-client structural check that runs after each local round, before aggregation, requiring only compact statistical summaries derived from each client update after a geometric transformation that removes dependence on the global model. Client-update statistics are compared against a calibrated benign reference, and deviations are detected through statistical distances. Energy and Wasserstein-1 jointly define an operational pattern where threshold exceedances across projections reveal structural deviations even in apparently benign updates. Evaluated on canonical backdoor scenarios from Bagdasaryan et al., the method detects both strong and stealthy attacks in the first local round through consistent multi-projection threshold excesses, while benign updates show only isolated ones. The procedure is lightweight, unsupervised, compatible with secure aggregation, and does not require trigger datasets. By providing early per-client warnings before aggregation, it complements classical defenses such as norm clipping, differential privacy, and robust aggregation, enabling proactive mitigation of poisoning in federated learning.
This study constructed a dataset targeting character product images, that are an important source of information about customer behavior, for business marketing. The images were collected independently and assigned detailed tagging information based on hypotheses, with the aim of training the models and contributing to multifaceted evaluation. Additionally, experiments using classification models demonstrated that the constructed dataset and tagging information contributed to verifying the characteristics of the models.
Encrypted traffic classification has emerged as a critical component of modern network management and cloud security services. While Virtual Private Networks (VPNs) ensure user privacy by encrypting communications, this encryption also complicates traditional traffic identification. Recent research demonstrates that Machine Learning (ML) and Deep Learning (DL) techniques can effectively classify VPN versus non-VPN traffic even without payload inspection. However, to align with trustworthy cloud service requirements, these ML/DL approaches must also preserve user privacy and assure security. In this paper, we review the state-of-the-art ML/DL methods for encrypted VPN traffic classification, emphasizing techniques that enhance trust, including privacy-preserving federated learning, adversarially robust models, and explainable AI. In addition, this work aims to discover the most significant features affecting the VPN classification and identifying the best-performing ML and DL models on available VPN classification datasets. We include studies that focused on characterizing the VPN traffic besides classifying the secure traffic into VPN and non-VPN.
This work presents a dual-modality unmanned aerial vehicle (UAV) detection and classification approach designed to address challenges in aerial surveillance under low-visibility and cluttered environmental conditions. The core contributions of this work are twofold. First, we introduce a novel, high-quality multimodal dataset comprising both real-world and synthetically generated UAV imagery across red-green-blue (RGB) and infrared (IR) channels. The dataset was curated through a rigorous pipeline that includes multi-source video collection, frame extraction, noise reduction, simulation-based class balancing, and scene-aware clustering to prevent data leakage. Second, we present a comprehensive benchmarking of detection and classification models using this dataset. Detection models were finetuned using You Only Look Once Version 11 Nano (YOLOv11n), one for RGB and another for IR, achieving mAP@0.5 scores of 93.5% and 98.2%, respectively. The proposed approach also performs classification into fixed-wing and rotary-wing UAV types to support more informed threat evaluation. Integration of the Slicing Aided Hyper Inference (SAHI) framework increased true positives by 24.7% (RGB) and 2.4% (IR), significantly enhancing detection performance.
