Pandora: A Cyber Range Environment for the Safe Testing and Deployment of Autonomous Cyber Attack Tools
Cybersecurity tools are increasingly automated with artificial intelligent (AI) capabilities to match the exponential scale of attacks, compensate for the relatively slower rate of training new cybersecurity talents, and improve of the accuracy and performance of both tools and users. However, the safe and appropriate usage of autonomous cyber attack tools - especially at the development stages for these tools - is still largely an unaddressed gap. Our survey of current literature and tools showed that most of the existing cyber range designs are mostly using manual tools and have not considered augmenting automated tools or the potential security issues caused by the tools. In other words, there is still room for a novel cyber range design which allow security researchers to safely deploy autonomous tools and perform automated tool testing if needed. In this paper, we introduce Pandora, a safe testing environment which allows security researchers and cyber range users to perform experiments on automated cyber attack tools that may have strong potential of usage and at the same time, a strong potential for risks. Unlike existing testbeds and cyber ranges which have direct compatibility with enterprise computer systems and the potential for risk propagation across the enterprise network, our test system is intentionally designed to be incompatible with enterprise real-world computing systems to reduce the risk of attack propagation into actual infrastructure. Our design also provides a tool to convert in-development automated cyber attack tools into to executable test binaries for validation and usage realistic enterprise system environments if required. Our experiments tested automated attack tools on our proposed system to validate the usability of our proposed environment. Our experiments also proved the safety of our environment by compatibility testing using simple malicious code.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Exploring Flexible Scenario Generation in Godot Simulator
Cyber-physical systems (CPS) combine cyber and physical components engineered to make decisions and interact within dynamic environments. Ensuring the safety of CPS is of great importance, requiring extensive testing acr…
A Survey of Algorithms for Black-Box Safety Validation of Cyber-Physical Systems
Autonomous cyber-physical systems (CPS) can improve safety and efficiency for safety-critical applications, but require rigorous testing before deployment. The complexity of these systems often precludes the use of forma…
Autonomous VehiclesCollision AvoidanceProblem DecompositionSurveyTesting learning-enabled cyber-physical systems with Large-Language Models: A Formal Approach
The integration of machine learning (ML) into cyber-physical systems (CPS) offers significant benefits, including enhanced efficiency, predictive capabilities, real-time responsiveness, and the enabling of autonomous ope…
Autonomous VehiclesPandora's Regret: A Proper Scoring Rule for Evaluating Sequential Search
In sequential search, alternatives are tested until the true class is found. Standard proper scoring rules like log loss are local, ignoring the ranking of competitors and misaligning model evaluation with search utility…
Autonomous Penetration Testing using Reinforcement Learning
Penetration testing (pentesting) involves performing a controlled attack on a computer system in order to assess it's security. Although an effective method for testing security, pentesting requires highly skilled practi…
Q-Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)