Recurrent Neural Network Attention Mechanisms for Interpretable System Log Anomaly Detection (1803.04967v1)

Abstract: Deep learning has recently demonstrated state-of-the art performance on key tasks related to the maintenance of computer systems, such as intrusion detection, denial of service attack detection, hardware and software system failures, and malware detection. In these contexts, model interpretability is vital for administrator and analyst to trust and act on the automated analysis of machine learning models. Deep learning methods have been criticized as black box oracles which allow limited insight into decision factors. In this work we seek to "bridge the gap" between the impressive performance of deep learning models and the need for interpretable model introspection. To this end we present recurrent neural network (RNN) LLMs augmented with attention for anomaly detection in system logs. Our methods are generally applicable to any computer system and logging source. By incorporating attention variants into our RNN LLMs we create opportunities for model introspection and analysis without sacrificing state-of-the art performance. We demonstrate model performance and illustrate model interpretability on an intrusion detection task using the Los Alamos National Laboratory (LANL) cyber security dataset, reporting upward of 0.99 area under the receiver operator characteristic curve despite being trained only on a single day's worth of data.

• The paper introduces attention mechanisms in RNN models to enhance interpretability in system log anomaly detection.
• It demonstrates competitive performance with over 0.99 AUC on the LANL dataset using minimal training data.
• The model insights enable actionable diagnostics by revealing log field contributions to anomaly decisions.

Interpretable System Log Anomaly Detection Using Attention Mechanisms in Recurrent Neural Networks

The paper "Recurrent Neural Network Attention Mechanisms for Interpretable System Log Anomaly Detection" presents a paper aimed at improving the interpretability of anomaly detection methods in system logs by augmenting recurrent neural networks (RNNs) with attention mechanisms. The proposed solutions address concerns that deep learning models, despite their high performance, are often criticized as black box solutions due to their lack of transparency. In the field of computer system maintenance, where decisions can have significant organizational consequences, understanding the model's decision-making process is crucial.

Overview

The authors focus on anomaly detection in system logs, a challenge compounded by large volumes of data, unbalanced class distribution, and complexity of relationships within logging data sources. To address these challenges, recurrent neural networks were selected for their ability to model sequences of log data. The paper introduces attention variants that enhance these RNN models, aiming to bridge the gap between performance and interpretability.

Core Contributions

1. Attention-Augmented RNN Models: The paper explores several attention mechanisms which, when combined with RNN LLMs, provide insights into model operations without sacrificing their efficacy. These mechanisms include fixed attention, syntax attention, semantic attention variations, and tiered attention models.
2. Performance Evaluation: Experiments are conducted using the Los Alamos National Laboratory (LANL) dataset. The results demonstrate competitive performance with an impressive area under the receiver operator characteristic curve (AUC) above 0.99, indicating strong detection capabilities even with minimal training data (a single day).
3. Interpretable Model Behavior: Attention mechanisms allow practitioners to inspect which parts of the input sequence the model focuses on, providing insights into how each log field contributes to anomaly detection decisions.

Implications and Future Directions

The paper's findings have significant implications for both the practical deployment and theoretical understanding of anomaly detection systems in computer networks. Practically, system administrators can leverage attention-augmented models to derive actionable insights and make informed decisions. Theoretically, understanding attention mechanisms in sequence modeling paves the way for developing models that are not only accurate but also comprehensible to users.

The paper opens avenues for further research in several directions. Exploring other sequence modeling tasks like hardware failure detection could validate the generalizability of these attention mechanisms across different domains. The attention mechanisms could be refined, potentially by integrating bidirectional attention models or combining attention between tiers for systems with hierarchical data dependencies. Additionally, the impact of using specialized vocabularies for different log fields suggests optimization potential in feature preprocessing.

In summary, this work underscores the necessity of interpretability in high-performance deep learning models and advances the understanding of attention-equipped RNNs in system log anomaly detection tasks. As the demand for transparent AI solutions grows, incorporating interpretability into model architecture as demonstrated here will become increasingly essential in the deployment of machine learning systems in critical applications.