LogBabylon: A Unified Framework for Cross-Log File Integration and Analysis

Logs are critical resources that record events, activities, or messages produced by software applications, operating systems, servers, and network devices. However, consolidating the heterogeneous logs and cross-referencing them is challenging and complicated. Manually analyzing the log data is time-consuming and prone to errors. LogBabylon is a centralized log data consolidating solution that leverages Large Language Models (LLMs) integrated with Retrieval-Augmented Generation (RAG) technology. LogBabylon interprets the log data in a human-readable way and adds insight analysis of the system performance and anomaly alerts. It provides a paramount view of the system landscape, enabling proactive management and rapid incident response. LogBabylon consolidates diverse log sources and enhances the extracted information's accuracy and relevancy. This facilitates a deeper understanding of log data, supporting more effective decision-making and operational efficiency. Furthermore, LogBabylon streamlines the log analysis process, significantly reducing the time and effort required to interpret complex datasets. Its capabilities extend to generating context-aware insights, offering an invaluable tool for continuous monitoring, performance optimization, and security assurance in dynamic computing environments.

RAF: Recursive Adversarial Attacks on Face Recognition Using Extremely Limited Queries

Recent successful adversarial attacks on face recognition show that, despite the remarkable progress of face recognition models, they are still far behind the human intelligence for perception and recognition. It reveals the vulnerability of deep convolutional neural networks (CNNs) as state-of-the-art building block for face recognition models against adversarial examples, which can cause certain consequences for secure systems. Gradient-based adversarial attacks are widely studied before and proved to be successful against face recognition models. However, finding the optimized perturbation per each face needs to submitting the significant number of queries to the target model. In this paper, we propose recursive adversarial attack on face recognition using automatic face warping which needs extremely limited number of queries to fool the target model. Instead of a random face warping procedure, the warping functions are applied on specific detected regions of face like eyebrows, nose, lips, etc. We evaluate the robustness of proposed method in the decision-based black-box attack setting, where the attackers have no access to the model parameters and gradients, but hard-label predictions and confidence scores are provided by the target model.