Bayesian Learning Networks Approach to Cybercrime Detection

Nasser Abouzakhar, Abdullah Gani, Gordon Manson, Mustafa Abuitbel, D. King

    Research output: Contribution to conferencePaperpeer-review

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    The growing dependence of modern society on telecommunication and information networks has become inevitable. The increase in the number of interconnected 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 consequently relating them to the following occurring activities.
    Original languageEnglish
    Number of pages6
    Publication statusPublished - 2003
    EventPGNet - Liverpool John Moores University, Liverpool, United Kingdom
    Duration: 16 Jun 200317 Jun 2003


    Country/TerritoryUnited Kingdom


    • Networks Intrusion Detection, Bayesian Networks, and Bayesian Learning


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