Explainable Security
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Explainable artificial intelligenceExplainable Artificial Intelligence (XAI)Similar Papers 제목 키워드 기반
Explainable Artificial Intelligence and Cybersecurity: A Systematic Literature Review
Cybersecurity vendors consistently apply AI (Artificial Intelligence) to their solutions and many cybersecurity domains can benefit from AI technology. However, black-box AI techniques present some difficulties in compre…
Explainable artificial intelligenceExplainable Artificial Intelligence (XAI)Systematic Literature ReviewA Survey on Explainable Artificial Intelligence for Cybersecurity
The black-box nature of artificial intelligence (AI) models has been the source of many concerns in their use for critical applications. Explainable Artificial Intelligence (XAI) is a rapidly growing research field that …
Explainable artificial intelligenceExplainable Artificial Intelligence (XAI)SurveyLearning-to-Explain through 20Q Gaming: An Explainable Recommender for Cybersecurity Education
The growing sophistication of contemporary cyber threats necessitates a more effective and adaptive approach to cybersecurity training. Intuitive and adaptive approaches to learning, which are often required, are not pro…
Reinforcement LearningCognitive Threat Intelligence and Explainable Federated Security Analytics for distributed Infrastructure Systems
The increasing adoption of distributed infrastructure systems, cloud computing, Internet of Things (IoT) technologies, and edge-based architectures has significantly expanded the cybersecurity attack surface and introduc…
Intrusion DetectionFederated LearningCybersecurity threat detection based on a UEBA framework using Deep Autoencoders
User and Entity Behaviour Analytics (UEBA) is a broad branch of data analytics that attempts to build a normal behavioural profile in order to detect anomalous events. Among the techniques used to detect anomalies, Deep …
Anomaly Detection