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- W1574503756 abstract "The growing dependence of modern society on telecommunication and information networks has become inevitable. The increase in the number of interconnecte d networks to the Internet has led to an increase in security threats and cybercrimes such as Distributed Denial of Service (DDoS) attacks. Any Internet based attack typically is prefaced by a reconnaissance probe process, which might take just a few minutes, hours, days, or even months before the attack takes place. In order to detect distributed network attacks as early as possible, an under research and development probabilistic approach, which is known by Bayesian networks has been proposed. This paper shows how probabilistically Bayesian network detects communication network attacks, allowing for generalization of Network Intrusion Detection Systems (NIDSs). Learning Agents which deploy Bayesian network approach are considered to be a promising and useful tool in determining suspicious early events of Internet threats and conseque ntly relating them to the following occurring activities." @default.
- W1574503756 created "2016-06-24" @default.
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- W1574503756 date "2003-01-01" @default.
- W1574503756 modified "2023-09-27" @default.
- W1574503756 title "Bayesian Learning Networks Approach to Cybercrime Detection" @default.
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