Social Virtual Reality (VR) platforms provide immersive social experiences but also expose users to serious risks of online harassment. Existing safety measures are largely reactive, while proactive solutions that detect harassment behavior during an incident often depend on sensitive biometric data, raising privacy concerns. In this paper, we present HarassGuard, a vision-language model (VLM) based system that detects physical harassment in social VR using only visual input. We construct an IRB-approved harassment vision dataset, apply prompt engineering, and fine-tune VLMs to detect harassment behavior by considering contextual information in social VR. Experimental results demonstrate that HarassGuard achieves competitive performance compared to state-of-the-art baselines (i.e., LSTM/CNN, Transformer), reaching an accuracy of up to 88.09% in binary classification and 68.85% in multi-class classification. Notably, HarassGuard matches these baselines while using significantly fewer fine-tuning samples (200 vs. 1,115), offering unique advantages in contextual reasoning and privacy-preserving detection.

Blockchain data has become a popular subject in studying various aspects of blockchains including the security of underlying mechanisms. However, the main chain block data, usually available from block explorer services, does not serve as a sufficient source of transaction and block dynamics that are only visible from a large-scale event measurement. In this paper, the transaction and block arrival events of the two popular public blockchains, i.e., Bitcoin and Ethereum, are measured to investigate the hidden dynamics of blockchain networks. We share our key findings and security implications including a false universal assumption of previous mining related studies and an invalid transaction propagation problem that can be exploited to launch a Denial-of-Service attack on a network.

Public blockchain records are widely studied in various aspects such as cryptocurrency abuse, anti-money-laundering, and monetary flow of businesses. However, the final blockchain records, usually available from block explorer services or querying locally stored data of blockchain nodes, do not provide abundant and dynamic event logs that are only visible from a live large-scale measurement. In this paper, we collect the network logs of three popular permissionless blockchains, that is, Bitcoin, Ethereum, and EOS. The discrepancy between observed events and the public block data is studied via a noble analysis model provided with the soundness of measurement. We share our key findings including a false universal assumption of previous mining-related studies and the block/transaction arrival characteristics.

Network scanning is the primary procedure preceding many network attacks. Until recently, network scanning has been widely studied to report a continued growth in volume and Internet-wide trends including the underpinning of distributed scannings by lingering Internet worms. It is, nevertheless, imperative to keep us informed with the current state of network scanning, for factual and comprehensive understanding of the security threats we are facing, and new trends to serve as the presage of imminent threats.