In this paper, we position the correct way of using graphical models for enhancing cyber security analysis in enterprise networks. Graphical models can be powerful in representation, analysis and visualization. We describe the need of introducing “intelligence” in security analysis, followed by a critical review of state-of-the-art attack graph approaches. Such review leads to the lessons learn...

Cloud-based systems and services are seeing exponential growth in the last few years. Many companies digital actively migrating their storage computational needs to cloud. With such an expansion of virtual services, security threats also significantly increasing. Utilizing Attack Representation Methods (ARMs) Graph (AG) enables administrator understand cloud network’s current situation. However...

Attack graphs are valuable tools in the assessment of network security, revealing potential attack paths an adversary could use to gain control of network assets. Creating an effective visualization for attack graphs is essential to their utility, but many previous efforts produce complex displays that are difficult to relate to the underlying networks. This thesis presents GARNET (Graphical At...

In today’s large complex enterprise network, security is a challenging task for most of the administrators. The typical means by which an attacker breaks into a network is through a series of exploits, where each exploit in the series satisfies the precondition for subsequent exploits and makes a causal relationship among them. Such a series of exploits constitutes an attack path and the set of...

Listeners flexibly recalibrate the perceptual categorization of sounds in response to speakers’ unusual pronunciation variants. Recent studies have shown that generalization of this recalibration can inform us about the nature of prelexical units used for speech perception. The present study tested whether this generalization is sensitive to phonetic or phonological properties of speech. Using ...

We evaluate a version of the recently-proposed Optimized Dissimilarity Space Embedding (ODSE) classification system that operates in the input space of sequences of generic objects. The ODSE system has been originally presented as a labeled graph classification system. However, since it is founded on the dissimilarity space representation of the input data, the classifier can be easily adapted ...

Distributed graph processing systems increasingly require many compute nodes to cope with the requirements imposed by contemporary graph-based Big Data applications. However, increasing the number of compute nodes increases the chance of node failures. Therefore, provisioning an efficient failure recovery strategy is critical for distributed graph processing systems. This paper proposes a novel...