Intrusion detection using artificial neural network / Gan Sze Kai

Gan , Sze Kai (2002) Intrusion detection using artificial neural network / Gan Sze Kai. Undergraduates thesis, University of Malaya.

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    According to this thesis title - Intrusion Detection Using Artificial Neural Network is whereby a neural network technology must be built inside the Intrusion Detection System (IDS). IDS is one of the security techniques, which is being used currently. It can be said that, Intrusion Detection System is a companion of the firewall, (of is beyond the firewall). The firewall is analog to the locker in (our houses to lock the windows and doors) to prevent intruders' break-in. Therefore, the IDS is a burglar alarm system to alert the user when an intruder successfully get through the firewall. In addition, it is also a vulnerable scanner to scan the network traffic to detect any existing intruder at that period of usage. In this project, neural network is used to analyze the network traffic, which will be implemented into the IDS to improve its ability to distinguish its authorized user and the intruder. Neural network is itself, a classification system that will enable the IDS to classify between its user and system activities. ('This project is only focus on analysis statistical network traffic). Hence, in my Chapter II - Literature Review, there will be a discussion providing details of the neural network and the IDS. Lastly, in Chapter ID, report on the system design of the entire system will be discussed too. System implementation and system testing is discuss in chapter IV and chapter V. A conclusion as well as presentation will then be made preceding the end of the above report.

    Item Type: Thesis ( Undergraduates)
    Additional Information: Academic Exercise (Bachelor’s Degree) – Faculty of Computer Science & Information Technology, University of Malaya, 2001/2002.
    Uncontrolled Keywords: Intrusion Detection Using Artificial Neural Network; classification system; Authorized user; Firewall; System activities
    Subjects: Q Science > QA Mathematics > QA76 Computer software
    Divisions: Faculty of Computer Science & Information Technology
    Depositing User: Mr Mohd Safri Tahir
    Date Deposited: 13 Jun 2019 08:48
    Last Modified: 13 Jun 2019 08:48

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