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Utilizing fuzzy logic and neural networks for effective, preventative intrusion detection in a wireless environment

The importance of properly securing an organization's information and computing resources has become paramount in modern business. Intrusion detection systems in particular have an increasingly valuable role to play: as networks grow and more information becomes available, administrators need better ways to monitor their systems. Most current intrusion detection systems lack the means to accurately monitor and report on wireless segments within the corporate network. This paper will propose an extension to the NeGPAIM model that will allow for the accurate detection of attacks originating on wireless network segments. This will be done by the use of fuzzy logic and neural networks utilized in the detection of intrusion attacks. The model is based on the assumption that each user has and leaves a unique footprint on a network when using it. This model is able to proactively detect intrusion attacks in both wired and wireless environments.