Intrusion Detection using Spiking Neural Networks

Gaurav Budjade
Department of Computer Science, Rochester Institute of Technology, Rochester, NY
Rochester Institute of Technology, 2014


   title={Intrusion Detection using Spiking Neural Networks},

   author={Rochester, NY},


   school={Rochester Institute of Technology}


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Nowadays, the advancements in internet technology are increasing by leaps and bounds. This has lead to the increase in threats by attackers, consequently compromising system security. Intrusion detection systems (IDS) provide an intelligent way to provide capable system security. Traditionally, IDS’s have been designed using several statistical based methods such as classification algorithms or artificial neural networks. Artificial neural networks are capable of learning and differentiating between normal system activity and anomalous system behavior. Spiking neural networks (SNN) are third generation neural networks where the neurons propagate signals only when a threshold membrane potential is reached. This project aims to explore the applicability of SNN’s in real world applications, specifically intrusion detection. The project will use a SNN, simulated by a NEST simulator and primarily programmed in Python. The IDS designed using this SNN will be trained and tested using the KDD dataset which consists of network traffic instances. The IDS created will be evaluated using parameters such as accuracy, time and CPU utilization. It will also be compared with systems that have previously incorporated statistical based models or well known neural network paradigms such as Multilayer Perceptron.
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