Related papers: Penetration Testing == POMDP Solving?

Penetration testing (or pentesting) is one of the widely used and important methodologies to assess the security of computer systems and networks. Traditional pentesting relies on the domain expert knowledge and requires considerable human…

Penetration testing is a security exercise aimed at assessing the security of a system by simulating attacks against it. So far, penetration testing has been carried out mainly by trained human attackers and its success critically depended…

Penetration testing is a critical technique for identifying security vulnerabilities, traditionally performed manually by skilled security specialists. This complex process involves gathering information about the target system, identifying…

Security has become, nowadays, a major concern for the organizations as the majority of its applications are exposed to Internet, which increases the threats of security considerably. Thus, the solution is to improve tools and mechanisms to…

Partially Observable Markov Decision Processes (POMDPs) are the standard framework for decision-making under uncertainty. While sampling-based methods scale well, they lack formal correctness guarantees, making them unsuitable for…

We study planning problems where autonomous agents operate inside environments that are subject to uncertainties and not fully observable. Partially observable Markov decision processes (POMDPs) are a natural formal model to capture such…

Recent changes in standards and regulations, driven by the increasing importance of software systems in meeting societal needs, mandate increased security testing of software systems. Penetration testing has been shown to be a reliable…

Web application pentesting is a crucial component in the offensive cybersecurity area, whose aim is to safeguard web applications and web services as the majority of the web applications are mounted in publicly accessible web environments.…

Partially Observable Markov Decision Processes (POMDPs) provide a rich framework for sequential decision-making under uncertainty in stochastic domains. However, solving a POMDP is often intractable except for small problems due to their…

The main goal in task planning is to build a sequence of actions that takes an agent from an initial state to a goal state. In robotics, this is particularly difficult because actions usually have several possible results, and sensors are…

We formulate and analyze a simplest Markov decision process model for intrusion tolerance problems, assuming that (i) each attack proceeds through one or more steps before the system's security fails, (ii) defensive responses that target…

Model checking is a widespread automatic formal analysis that has been successful in discovering flaws in security protocols. However existing possibilities for state space explosion still hinder analyses of complex protocols and protocol…

Offensive security-tests are a common way to pro-actively discover potential vulnerabilities. They are performed by specialists, often called penetration-testers or white-hat hackers. The chronic lack of available white-hat hackers prevents…

We consider the problem of tracking an intruder using a network of wireless sensors. For tracking the intruder at each instant, the optimal number and the right configuration of sensors has to be powered. As powering the sensors consumes…

This tutorial paper presents a hands-on perspective on probabilistic model checking with the Storm model checker. Storm is a decade-old model checker that excels in performance and a rich Python-based ecosystem, which makes it easy to…

Penetration testing is an essential means of proactive defense in the face of escalating cybersecurity incidents. Traditional manual penetration testing methods are time-consuming, resource-intensive, and prone to human errors. Current…

Autonomous systems are often required to operate in partially observable environments. They must reliably execute a specified objective even with incomplete information about the state of the environment. We propose a methodology to…

Early detection of cyber-attacks is crucial for a safe and reliable operation of the smart grid. In the literature, outlier detection schemes making sample-by-sample decisions and online detection schemes requiring perfect attack models…

Partially-Observable Markov Decision Processes (POMDPs) are a well-known stochastic model for sequential decision making under limited information. We consider the EXPTIME-hard problem of synthesising policies that almost-surely reach some…

Partially observable Markov decision processes (POMDPs) provide a modeling framework for a variety of sequential decision making under uncertainty scenarios in artificial intelligence (AI). Since the states are not directly observable in a…