DeepAID: Interpreting and Improving Deep Learning-based Anomaly Detection in Security Applications

TL;DR

DeepAID is a framework that interprets and enhances unsupervised deep learning anomaly detection models in security, addressing interpretability challenges and aiding security operators in understanding and improving system performance.

Contribution

DeepAID introduces a novel interpretation method for unsupervised DNNs tailored for security, along with tools to improve security systems based on these interpretations.

Findings

High-quality interpretations for unsupervised DL models in security

DeepAID helps understand model decisions and diagnose mistakes

Reduces false positives in security anomaly detection

Abstract

Unsupervised Deep Learning (DL) techniques have been widely used in various security-related anomaly detection applications, owing to the great promise of being able to detect unforeseen threats and superior performance provided by Deep Neural Networks (DNN). However, the lack of interpretability creates key barriers to the adoption of DL models in practice. Unfortunately, existing interpretation approaches are proposed for supervised learning models and/or non-security domains, which are unadaptable for unsupervised DL models and fail to satisfy special requirements in security domains. In this paper, we propose DeepAID, a general framework aiming to (1) interpret DL-based anomaly detection systems in security domains, and (2) improve the practicality of these systems based on the interpretations. We first propose a novel interpretation method for unsupervised DNNs by formulating and solving well-designed optimization problems with special constraints for security domains. Then, we provide several applications based on our Interpreter as well as a model-based extension Distiller to improve security systems by solving domain-specific problems. We apply DeepAID over three types of security-related anomaly detection systems and extensively evaluate our Interpreter with representative prior works. Experimental results show that DeepAID can provide high-quality interpretations for unsupervised DL models while meeting the special requirements of security domains. We also provide several use cases to show that DeepAID can help security operators to understand model decisions, diagnose system mistakes, give feedback to models, and reduce false positives.