Real-Time Evasion Attacks against Deep Learning-Based Anomaly Detection from Distributed System Logs

Abstract

Distributed system logs, which record states and events that occurred during the execution of a distributed system, provide valuable information for troubleshooting and diagnosis of its operational issues. Due to the complexity of such systems, there have been some recent research efforts on automating anomaly detection from distributed system logs using deep learning models. As these anomaly detection models can also be used to detect malicious activities inside distributed systems, it is important to understand their robustness against evasive manipulations in adversarial environments. Although there are various attacks against deep learning models in domains such as natural language processing and image classification, they cannot be applied directly to evade anomaly detection from distributed system logs. In this work, we explore the adversarial robustness of deep learning-based anomaly detection models on distributed system logs. We propose a real-time attack method called LAM (Log Anomaly Mask) to perturb streaming logs with minimal modifications in an online fashion so that the attacks can evade anomaly detection by even the state-of-the-art deep learning models. To overcome the search space complexity challenge, LAM models the perturber as a reinforcement learning agent that operates in a partially observable environment to predict the best perturbation action. We have evaluated the effectiveness of LAM on two log-based anomaly detection systems for distributed systems: DeepLog and an AutoEncoder-based anomaly detection system. Our experimental results show that LAM significantly reduces the true positive rate of these two models while achieving attack imperceptibility and real-time responsiveness.