Log-based anomaly detection (LAD) is imperative to ensure both the reliability and security of software systems. Although many deep learning approaches have been designed to capture complex and diverse anomaly patterns from log files, they heavily rely on large-scale annotated data. However, collecting sufficient labeled data is impractical when a software system has just been deployed. In this paper, we propose a cross-system few-shot learning log-based anomaly detection approach, namely FSLog, to solve the abnormal label scarcity problem, which is the main challenge of recent LAD research. Specifically, we leverage a pre-trained model from source system to enrich feature representations so that data instances from target system can also be effectively represented. To this end, we introduce a novel adversarial margin loss to enhance our feature distinguishability while preserving their generalizability. Further, we also develop a masked interactive temporal network for robust feature extraction of temporal relationships for log samples. We evaluate the proposed FSLog on three publicly available datasets based on a standard few-shot learning setup protocol. Experimental results demonstrate that our method achieves the best performance in detecting abnormal logs when compared to state-of-the-art methods.

Log anomaly detection (LAD) is of paramount importance to enhance the reliability and stability of software systems. Current state-of-the-art LAD suffers a significant performance degradation when dealing with consistently evolving log events caused by system updates. To build a reliable LAD model under the context of log data evolution, we propose an incremental learning-based method for LAD, namely ilLog, to avoid catastrophic forgetting of previously learned knowledge while continuously updating the model for better detection when processing the evolving log events. In particular, we design a novel entropy-driven sorting algorithm for real log sample replay, which enables the preservation of old knowledge via storing representative samples with discrete sequence features from previous tasks. Additionally, we introduce a Halton-based low discrepancy sequence to better approximate the sliced Cram´ er distance between the probability distributions of two models, thus enhancing the model learning capability. Based on a standard incremental learning protocol setting, we evaluate the newly proposed ilLog method on three publicly available datasets. Experimental results demonstrate that our approach achieves the best performance compared to SOTA LAD methods and models by applying existing IL-based methods in evolving software systems.

For intelligent unmanned aerial vehicles working in complex environments, it is necessary to have a certain autonomous flight control decision-making ability to adapt to complex and changeable environments. In order to realize the rapid path planning of intelligent unmanned aerial vehicle in complex flight environment and ensure its accurate positioning, we consider the constraints of error correction and turning radius and so on, and establish a multiobjective optimization model with the shortest path and the least correction times. In addition, a novel projection algorithm is proposed to solve this model. The evaluation of our proposed method is done from a dataset. We clearly show its effectiveness and its superiority compared to several state-of-the art approaches.