Cost-sensitive specialization is a generic technique for misclassification cost sensitive induction. This technique involves specializing aspects of a classifier associated with high misclassification costs and generalizing those associated with low misclassification costs. It is widely applicable and simple to implement. It could be used to augment the effect of standard cost-sensitive inducti...

While POMDPs provide a general platform for conditional planning under a wide range of quality metrics they have limited scalability. On the other hand, uniform probability conditional planners scale very well, but many lack the ability to optimize plan quality metrics. We present an innovation to planning graph based heuristics that helps uniform probability conditional planners both scale and...

Attack graphs offer a powerful framework for security risk assessment. They provide a compact representation of the attack paths that an attacker can follow to compromise network resources from the analysis of the network topology and vulnerabilities. The uncertainty about the attacker’s behaviour makes Bayesian networks suitable to model attack graphs to perform static and dynamic security ris...

The evaluation of classifier performance in a cost-sensitive setting is straightforward if the operating conditions (misclassification costs and class distributions) are fixed and known. When this is not the case, evaluation requires a method of visualizing classifier performance across the full range of possible operating conditions. This talk outlines the most important requirements for cost-...

‘GrADAR’ is an intuitive approach to create and maintain a model of a computer network and of the availability of its resources from the observations of deployed monitoring systems [JTM07, JTM08]. The graph-based model is able to express both the effects of DoS attacks and characterise the result of available response measures prior to their application in the real-world network. Thus, the appr...

We investigate families of infinite automata for context-sensitive languages. An infinite automaton is an infinite labeled graph with two sets of initial and final vertices. Its language is the set of all words labelling a path from an initial vertex to a final vertex. In 2001, Morvan and Stirling proved that rational graphs accept the context-sensitive languages between rational sets of initia...

Attack graph is important in defending against well-orchestrated network intrusions. However, the current analysis of attack graphs requires an algorithm to be developed and implemented, causing a delay in the availability of analysis. Such a delay is usually unacceptable because the needs for analyzing attack graphs may change rapidly in defending against network intrusions. An administrator m...

This paper presents a cost-sensitive Question-Answering (QA) framework for learning a nine-layer And-Or graph (AoG) from web images, which explicitly represents object categories, poses, parts, and detailed structures within the parts in a compositional hierarchy. The QA framework is designed to minimize an overall risk, which trades off the loss and query costs. The loss is defined for nodes i...

With the rapid growth of computer networks, more and more users access the remote server’s service in a distributed computing environment. Due to the fast development of the Internet and wireless communications, many activities like online-shopping, online banking, online voting are conducted over it. Authentication is one of the essential security features of network communication. The authent...

The current network risk assessment model often ignores the impact of attack cost and intrusion intention on security. In order to better solve problem information security defense strategy selection accurately assess target risk, this paper proposes an path prediction method based game model.The atomic probability is calculated by vulnerability value, benefit. static established combined with ...