Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research

Abstract

This survey presents a comprehensive review of current literature on Explainable Artificial Intelligence (XAI) methods for cyber security applications. Due to the rapid development of Internet-connected systems and Artificial Intelligence in recent years, Artificial Intelligence including Machine Learning (ML) and Deep Learning (DL) has been widely utilized in the fields of cyber security including intrusion detection, malware detection, and spam filtering. However, although Artificial Intelligence-based approaches for the detection and defense of cyber attacks and threats are more advanced and efficient compared to the conventional signature-based and rule-based cyber security strategies, most ML-based techniques and DL-based techniques are deployed in the black-box manner, meaning that security experts and customers are unable to explain how such procedures reach particular conclusions. The deficiencies of transparency and interpretability of existing Artificial Intelligence techniques would decrease human users' confidence in the models utilized for the defense against cyber attacks, especially in current situations where cyber attacks become increasingly diverse and complicated. Therefore, it is essential to apply XAI in the establishment of cyber security models to create more explainable models while maintaining high accuracy and allowing human users to comprehend, trust, and manage the next generation of cyber defense mechanisms. Although there are papers reviewing Artificial Intelligence applications in cyber security areas and the vast literature on applying XAI in many fields including healthcare, financial services, and criminal justice, the surprising fact is that there are currently no survey research articles that concentrate on XAI applications in cyber security.

arXiv commentAccepted by IEEE Access

References in corpus (39)

• Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
• European Union regulations on algorithmic decision-making and a "right to explanation"
• N-BaIoT: Network-based Detection of IoT Botnet Attacks Using Deep Autoencoders
• An open access repository of images on plant health to enable the development of mobile disease diagnostics
• GNNExplainer: Generating Explanations for Graph Neural Networks
• Survey of Machine Learning Techniques for Malware Analysis
• Technical Report on the CleverHans v2.1.0 Adversarial Examples Library
• One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques
• What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use
• A study of the effect of JPG compression on adversarial images
• A Survey of the State of Explainable AI for Natural Language Processing
• CoAID: COVID-19 Healthcare Misinformation Dataset
• Ground Truth Evaluation of Neural Network Explanations with CLEVR-XAI
• Utilizing XAI technique to improve autoencoder based model for computer network anomaly detection with shapley additive explanation(SHAP)
• DeepCloak: Masking Deep Neural Network Models for Robustness Against Adversarial Samples
• Explainable AI meets Healthcare: A Study on Heart Disease Dataset
• Modeling Users' Behavior Sequences with Hierarchical Explainable Network for Cross-domain Fraud Detection
• Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling
• Evaluating Explainable AI on a Multi-Modal Medical Imaging Task: Can Existing Algorithms Fulfill Clinical Requirements?
• Explaining Network Intrusion Detection System Using Explainable AI Framework
• XAI for Cybersecurity: State of the Art, Challenges, Open Issues and Future Directions
• Why Fairness Cannot Be Automated: Bridging the Gap Between EU Non-Discrimination Law and AI
• xFraud: Explainable Fraud Transaction Detection
• Explainable Artificial Intelligence (XAI): An Engineering Perspective
• Analyzing the Real-World Applicability of DGA Classifiers
• Towards Explainable NLP: A Generative Explanation Framework for Text Classification
• Global Explanations of Neural Networks: Mapping the Landscape of Predictions
• When Explainability Meets Adversarial Learning: Detecting Adversarial Examples using SHAP Signatures
• SoK: Applying Machine Learning in Security - A Survey
• First Step Towards EXPLAINable DGA Multiclass Classification
• Applications of Artificial Intelligence to Network Security
• Towards Explainable Artificial Intelligence in Banking and Financial Services
• Explainable Artificial Intelligence (XAI) for Internet of Things: A Survey
• Explainable Deep Behavioral Sequence Clustering for Transaction Fraud Detection
• The Plant Pathology 2020 challenge dataset to classify foliar disease of apples
• ECQ${}^{\text{x}}$: Explainability-Driven Quantization for Low-Bit and Sparse DNNs
• Generating Plausible Counterfactual Explanations for Deep Transformers in Financial Text Classification
• Edge Computing in Transportation: Security Issues and Challenges
• Gradient-based Analysis of NLP Models is Manipulable

Cited by in corpus (6)

• A Survey on Explainable Artificial Intelligence for Cybersecurity
• A Review of Digital Twins and their Application in Cybersecurity based on Artificial Intelligence
• Machine Learning for Healthcare-IoT Security: A Review and Risk Mitigation
• Enabling AutoML for Zero-Touch Network Security: Use-Case Driven Analysis
• Explainable Data Poison Attacks on Human Emotion Evaluation Systems based on EEG Signals
• An Explainable Multi-Task Similarity Measure: Integrating Accumulated Local Effects and Weighted Fréchet Distance