In recent years, various international security events have occurred frequently and interacted between real society and cyberspace. Traditional traffic monitoring mainly focuses on the local anomalous status of events due to a large amount of data. BGP-based event monitoring makes it possible to perform differential analysis of international events. For many existing traffic anomaly detection methods, we have observed that the window-based noise reduction strategy effectively improves the success rate of time series anomaly detection. Motivated by this, we propose an unsupervised anomaly detection model, MAD-MulW, which introduces a multi-window serial framework. The W-GAT module adaptively updates sample weights within the window to reduce noise, while the W-LAE module captures temporal trends through predictive reconstruction, enhancing inter-class separation. Our model has been experimentally validated on multiple BGP anomalous events with an average F1 score of over 90%, which demonstrates the significant improvement effect of the stage windows and adaptive strategy on the efficiency and stability of the timing model. The source code is available at://github.com/2024ChenYP/MAD-MulW.

The last decade has witnessed the rapid growth of open-source software (OSS). Still, all contributors may find it difficult to assimilate into the OSS community even they are enthusiastic to make contributions. We thus suggest that partner recommendation across different roles may benefit both the users and developers, i.e., once we are able to make successful recommendation for those in need, it may dramatically contribute to the productivity of developers and the enthusiasm of users, thus further boosting OSS projects’ development. Motivated by this potential, we model the partner recommendation as link prediction task from email data via network embedding methods. In this article, we introduce time-series snapshot network (TSSN) that is a mixture network to model the interactions among users and developers. Based on the established TSSN, we perform temporal biased walk (TBW) to automatically capture both temporal and structural information of the email network, i.e., the behavioral similarity between individuals in the OSS email network. Experiments on ten Apache data sets demonstrate that the proposed TBW significantly outperforms a number of advanced random walk-based embedding methods, leading to the state-of-the-art recommendation performance.

In social networks, by removing some target-sensitive links, privacy protection might be achieved. However, some hidden links can still be re-observed by link prediction methods on observable networks. In this paper, the conventional link prediction method named Resource Allocation Index (RA) is adopted for privacy attacks. Several defense methods are proposed, including heuristic and evolutionary approaches, to protect targeted links from RA attack. In particular, incremental computation is proposed for accelerating the calculation of fitness in evolutionary approaches. This is the first time to study privacy protection for targeted links against similarity based link prediction attacks. Some links are randomly selected from original network as targeted links for experimentation. The experimental results on nine real-world networks demonstrate the superiority of the evolutionary perturbations, especially EDA, for defending against RA attack. Moreover, experimental results show that the proposed perturbation generated by EDA is transferable and can even defend against other link prediction attacks which are based on high order similarity between pairwise nodes, although it is designed to prevent RA attack.