To prevent an exploit, the security analyst must implement a suitable countermeasure. In this paper, we consider cost-sensitive attack graphs (CAGs) for network vulnerability analysis. In these attack graphs, a weight is assigned to each countermeasure to represent the cost of its implementation. There may be multiple countermeasures with different weights for preventing a single exploit. Also,...

To prevent an exploit, the security analyst must implement a suitable countermeasure. In this paper, we consider cost-sensitive attack graphs (CAGs) for network vulnerability analysis. In these attack graphs, a weight is assigned to each countermeasure to represent the cost of its implementation. There may be multiple countermeasures with different weights for preventing a single exploit. Also,...

An attack graph represents all known sequences of actions that compromise a system in form of an and-or graph. We assume that each action in the attack graph has a specified cost and probability of success and propose an algorithm for computing an action selection policy minimizing the expected cost of performing an attack. We model the problem as a finite horizon MDP and use forward search wit...

Deep learning algorithms have seen a massive rise in popularity for remote sensing over the past few years. Recently, studies on applying deep techniques to graph data (e.g., public transport networks) been conducted. In node classification tasks, traditional neural network (GNN) models assume that different types of misclassifications an equal loss and thus seek maximize posterior probability ...

Intruders often combine exploits against multiple vulnerabilities in order to break into the system. Each attack scenario is a sequence of exploits launched by an intruder that leads to an undesirable state such as access to a database, service disruption, etc. The collection of possible attack scenarios in a computer network can be represented by a directed graph, called network attack gra...

Attack graph is a popular tool for modeling multi-staged, correlated attacks on computer networks. Attack graphs have been widely used for measuring network security risks. A major portion of these works, have used host based or state based attack graphs. These attack graph models are either too restrictive or too resource consuming. Also, a significant portion of these works have used ‘probabi...

Security analysis and attack-defense modeling are effective method to identify the vulnerabilities of information systems for proactive defense. The attack graph model reflects only attack actions and system state changes, without considering the perspective of the defenders. To assess the network information system and comprehensively show attack and defense strategies and theirs cost, a defen...

This paper introduces a novel cost sensitive weighted samples approach to a cascade of Graph Neural Networks for learning from imbalanced data in the graph structured input domain. This is shown to be very effective in addressing the effects of imbalanced data distribution on learning systems. The proposed idea is based on a weighting mechanism which forces the network to encode misclassified g...

Various tools exist to analyze enterprise network systems and to produce attack graphs detailing how attackers might penetrate into the system. These attack graphs, however, are often complex and difficult to comprehend fully, and a human user may find it problematic to reach appropriate configuration decisions. This paper presents methodologies that can 1) automatically identify portions of an...