Eclipse attacks isolate blockchain nodes by monopolizing their peer-to-peer connections. The attacks were extensively studied in Bitcoin (SP'15, SP'20, CCS'21, SP'23) and Monero (NDSS'25), but their practicality against Ethereum nodes remains underexplored, particularly in the post-Merge settings.

Falls represent a significant public health challenge for older adults, given their high incidence and potential for severe outcomes. Proactive identification of individuals at high fall risk through effective assessment is crucial for implementing targeted prevention strategies. Despite numerous fall risk assessment solutions available, a notable gap persists in objective and readily accessible assessment tools. Addressing this need, we proposed a novel application of video-based gait analysis for fall risk assessment utilizing a single red-green-blue (RGB) camera. Key spatiotemporal gait parameters were derived for risk modeling using two-dimensional human pose estimation and a predefined distance measure. This approach advances beyond existing frameworks that mainly rely on wearable sensors or depth cameras by providing a more practical and accessible alternative. To assess the efficacy of this approach, an experimental study was conducted with 99 community-dwelling older adults. The findings revealed significant differences in gait parameters between individuals categorized into high and low fall risk groups. Among commonly used classification algorithms, support vector machine (SVM) classifier yielded optimal performance, achieving an accuracy of 86.35 % alongside balanced sensitivity (87.55 %) and specificity (85.16 %). Model interpretability was further substantiated by consistent feature importance patterns observed across cross-validation folds, elucidated by SHapley Additive exPlanations (SHAP). Compared to current state-of-the-art fall risk assessment studies, our single RGB camera-based methodology demonstrates a more favorable balance of accuracy and practicality. This solution exhibits substantial promise for wider adoption in facilitating routine and more accessible fall risk screening for the older people in their daily environments.

Cryptomining poses significant security risks, yet traditional detection methods like blacklists and Deep Packet Inspection (DPI) are often ineffective against encrypted mining traffic and suffer from high false positive rates. In this paper, we propose a practical encrypted cryptomining traffic detection mechanism. It consists of a two-stage detection framework, which can effectively provide fine-grained detection results by machine learning and reduce false positives from classifiers through active probing. Our system achieves an F1-score of 0.99 and identifies specific cryptocurrencies with a 99.39% accuracy rate. Extensive testing across various mining pools confirms the effectiveness of our approach, offering a more precise and reliable solution for identifying cryptomining activities.

Cryptomining behaviours pose severe security threats to campus network. However, existing blacklist and DPI-based techniques suffer from delayed blacklist updates and inability to identify encrypted cryptomining traffic. Furthermore, existing encrypted cryptomining traffic detection schemes usually fail to provide detailed information about cryptomining behaviours and do not have a solution to deal with false positives caused by detection models. To meet the needs of campus networks and solve the problems of existing work, this paper proposes an effective and practical encrypted cryptomining traffic detection mechanism in campus network. It consists of a two-stage detection framework, which can effectively provide fine-grained detection results by machine learning and reduce false positives from classifiers through active probing. Based on our collected dataset and extracted time series features, our classifiers detect mining traffic with an 0.99 F 1 score and identify the cryptocurrency being mined with $99.39 \%$ correct recognition rate. Unlike existing schemes, we perform active probing after the traffic classification to reduce false positives. Futhermore, we have extensively evaluated the active probing scheme to verify its effectiveness for different mining pools.