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1Characterizing Honeypot-Captured Cyber Attacks: Statistical Framework and Case Study

by Zhenxin Zhan, Maochao Xu, Shouhuai Xu
"... Abstract—Rigorously characterizing the statistical properties of cyber attacks is an important problem. In this paper, we propose the first statistical framework for rigorously analyzing honeypot-captured cyber attack data. The framework is built on the novel concept of stochastic cyber attack proce ..."
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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

Intrusion Detection Framework for Cyber Crimes using Bayesian Network

by Chaminda Alocious, Nasser Abouzakhar, Hannan Xiao, Bruce Christianson
"... Abstract: Computer Network Security has become a critical and important issue due to ever increasing cyber-crimes. Cybercrimes are spanning from simple piracy crimes to information theft in international terrorism. Defence security agencies and other militarily related organizations are highly conce ..."
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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

Collaborative Recommendation: A Robustness Analysis

by Michael O'Mahony, Neil Hurley, Nicholas Kushmerick, Guénolé Silvestre - ACM Transactions on Internet Technology , 2003
"... this article is organised as follows. We begin with a discussion of collaborative recommendation and a formalisation of the notions of robustness and the perturbations with which we are concerned (Section 2). We then analyse robustness from both the accuracy and stability perspectives. Regarding acc ..."
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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

Mariana: Tencent Deep Learning Platform and its Applications

by Yongqiang Zou, Xing Jin, Yi Li, Zhimao Guo, Eryu Wang, Bin Xiao, Tencent Inc
"... 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 ..."
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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

Cyberspace Security Using Adversarial Learning and Conformal Prediction

by Harry Wechsler , 2015
"... This paper advances new directions for cyber security using adversarial learning and conformal prediction in order to enhance network and computing services defenses against adaptive, mali-cious, persistent, and tactical offensive threats. Conformal prediction is the principled and unified adaptive ..."
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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

On the Definitions of Cryptographic Security: Chosen-Ciphertext Attack Revisited

by unknown authors , 1999
"... ..."
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Abstract not found

Attacking Others Online: The Formation of Cyberbullying in Late Adolescence

by Christopher P Barlett , Douglas A Gentile
"... Cyberbullying frequency is related to a wide range of negative outcomes. Little research has attempted to delineate the long-term predictors and mechanisms to predict cyberbullying. Study 1 (N ϭ 493) used a correlational study that tested our long-term model of cyberbullying. This model predicted t ..."
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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

Analysis of Intrusion Detection and Attack Proliferation in Computer Networks

by Prahalad Rangana, Kevin H. Knutha
"... Abstract. One of the popular models to describe computer worm propagation is the Susceptible-Infected (SI) model [1]. This model of worm propagation has been implemented on the simulation toolkit Network Simulator v2 (ns-2) [2]. The ns-2 toolkit has the capability to simulate networks of different t ..."
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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

A Framework for Simulation of Intrusion Detection System using Support Vector Machine

by D. P. Gaikwad
"... An intrusion compromises the security and the value of a computer system in network. Legitimate users find it difficult to access network services due to the network attacks as they intentionally occupy or sabotage network resources and services. The intrusion detection system defends the critical c ..."
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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

Protecting Your Children from Inappropriate Content in Mobile Apps: An Automatic Maturity Rating Framework

by Bing Hu, Bin Liu, Neil Zhenqiang Gong, Deguang Kong, Hongxia Jin
"... Mobile applications (Apps) could expose children or adoles-cents to mature themes such as sexual content, violence and drug use, which harms their online safety. Therefore, mobile platforms provide rating policies to label the maturity levels of Apps and the reasons why an App has a given maturity l ..."
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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
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