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A web-based intrusion detection and prevention system using neural networks



Abstract In recent times, it has become a necessity to obtain a security measure for computer networks due to the high influx of perpetrators using the internet for malicious purposes. These perpetrators have caused the system and its users to lose confidential information for their own benefit. This work aims at providing a phenomenal solution to the problem of data intrusion. The research project is specified in the protection of web data intrusion i.e. the data that is stored on different websites or web applications. The intrusion detection and prevention system makes use of an Artificial Neural Network (ANN) which adopts the pattern matching algorithm that compares the current state of the system with the normal state. The Agile System Development Life Cycle (SDLC) was used in the development of the system. For the pattern matching, rules like back door penetration, brute force attack on password/username, SQL injection and XSS injection were embedded in the system. The developed system was tested on two different web applications and it successfully detected and prevented intrusions based on the rules that were defined by the Neural Network Intrusion Detection and Prevention System (NNIDPS).   Keywords: Intrusion Detection, Pattern matching, Artificial Neural Network.


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