process, a new kind of mathematical objects for describing cyber attacks. To demonstrate use of the framework, we apply it to analyze a low-interaction honeypot dataset, while noting that the framework can be equally applied to analyze high-interaction honeypot data that contains richer information about

different Bayesian networks as classification models, where each of these classifier models are interconnected and communicated to predict on incoming network traffic data. Each designed Bayesian network model is capable of detecting a major category of attack such as denial of service (DoS). However, all

accuracy, in Section 3 we formalise robustness in machine learnings terms, and introduce a novel form of class noise that models an interesting suite of attacks. We develop two models that predict the change in accuracy as a function of the number of fake ratings that have been inserted into the customer

Deep learning gains lots of attentions in recent years and is more and more important for mining values in big data. However, to make deep learning practical for a wide range of applications in Tencent Inc., three requirements must be considered: 1) Lots of computational power are required to train

and learning framework used to design, develop, and deploy a multi-faceted self-man-aging defensive shield to detect, disrupt, and deny intrusive attacks, hostile and malicious beha-vior, and subterfuge. Conformal prediction leverages apparent relationships between immunity and intrusion detection using non

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longitudinal study to test what reinforced learning processes are related to the development of cyberbullying. We predicted that readily accessible attitudes, beliefs, and other knowledge structures likely predict cyberbullying behavior. In doing so, we attempted to elucidate a psychological model

an attack. The infection equation obtained from [1] enables us to derive a likelihood function for the infection reports. This prior information can be used in the Bayesian framework to obtain the posterior probabilities for network properties of interest, such as the rate at which nodes contact one another

computer system and networks from cyber-attacks. Various techniques of machine learning are applied to intrusion detection system. In this paper, a framework for simulation of intrusion detection system is described. The radial basis kernel based support vector machine is used to simulate the intrusion

and build a machine learning framework to automatically predict maturity levels for mobile Apps and the associated reasons with a high accuracy and a low cost. To this end, we take a multi-label classification approach to predict the mature contents in a given App and then label the maturity level according