# Explainable Security

arXiv: https://arxiv.org/abs/1807.04178 · HTML: https://mlsanity.com/paper/1807.04178/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jul 2018.

## Built on by (most cited first, 15 of 15)

- [SoK: Explainable Machine Learning for Computer Security Applications](https://mlsanity.com/paper/2208.10605/) (2022)
- [When and How to Fool Explainable Models (and Humans) with Adversarial Examples](https://mlsanity.com/paper/2107.01943/) (2021)
- [A Survey on XAI for 5G and Beyond Security: Technical Aspects, Challenges and Research Directions](https://mlsanity.com/paper/2204.12822/) (2022)
- [On the Importance of Domain-specific Explanations in AI-based Cybersecurity Systems (Technical Report)](https://mlsanity.com/paper/2108.02006/) (2021)
- [SoK: Modeling Explainability in Security Analytics for Interpretability, Trustworthiness, and Usability](https://mlsanity.com/paper/2210.17376/) (2022)
- [An Explainable Regression Framework for Predicting Remaining Useful Life of Machines](https://mlsanity.com/paper/2204.13574/) (2022)
- [Applications of Explainable AI for 6G: Technical Aspects, Use Cases, and Research Challenges](https://mlsanity.com/paper/2112.04698/) (2021)
- [SoK: Access Control Policy Generation from High-level Natural Language Requirements](https://mlsanity.com/paper/2310.03292/) (2023)
- [RAGent: Retrieval-based Access Control Policy Generation](https://mlsanity.com/paper/2409.07489/) (2024)
- [Detection, Explanation and Filtering of Cyber Attacks Combining Symbolic and Sub-Symbolic Methods](https://mlsanity.com/paper/2212.13991/) (2022)
- [Explanation-Bound Tool Execution for AI Agents: Server-Verified Action Claims Without Trusting Model Rationales](https://mlsanity.com/paper/2607.25364/) (2026)
- [One-shot Policy Elicitation via Semantic Reward Manipulation](https://mlsanity.com/paper/2101.01860/) (2021)
- [Towards Explainable Meta-Learning for DDoS Detection](https://mlsanity.com/paper/2204.02255/) (2022)
- [Towards a Transparent and Interpretable AI Model for Medical Image Classifications](https://mlsanity.com/paper/2509.16685/) (2025)
- [AWARE-RG: Abstraction Aware Explanation of Resiliency Graphs for Cyber Physical Systems](https://mlsanity.com/paper/2610.19650/) (2026)

## Abstract (arXiv)

The Defense Advanced Research Projects Agency (DARPA) recently launched the Explainable Artificial Intelligence (XAI) program that aims to create a suite of new AI techniques that enable end users to understand, appropriately trust, and effectively manage the emerging generation of AI systems. In this paper, inspired by DARPA's XAI program, we propose a new paradigm in security research: Explainable Security (XSec). We discuss the ``Six Ws'' of XSec (Who? What? Where? When? Why? and How?) and argue that XSec has unique and complex characteristics: XSec involves several different stakeholders (i.e., the system's developers, analysts, users and attackers) and is multi-faceted by nature (as it requires reasoning about system model, threat model and properties of security, privacy and trust as well as about concrete attacks, vulnerabilities and countermeasures). We define a roadmap for XSec that identifies several possible research directions.