DeepAID: Interpreting and Improving Deep Learning-based Anomaly Detection in Security Applications
Dongqi Han, Zhiliang Wang, Wenqi Chen, Ying Zhong, Su Wang, Han Zhang, Jiahai Yang, Xingang Shi, Xia Yin
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.
Topics & Concepts
InterpretabilityComputer scienceDeep learningAnomaly detectionArtificial intelligenceUnsupervised learningMachine learningKey (lock)Deep neural networksComputer securityAnomaly Detection Techniques and ApplicationsNetwork Security and Intrusion DetectionAdversarial Robustness in Machine Learning