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A backpropagation neural network called NNID (Neural Network Intrusion Detector) was trained in the identification task and tested experimentally on a system of 10 users. The system was 96% accurate in detecting unusual activity, with 7% false alarm rate.
This paper proposes a new way of applying neural networks to detect intrusions. We believe that a user leaves a 'print' when using the system; a neural network ...
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Network Intrusion Detection System Using Neural Networks - IEEE Xplore
ieeexplore.ieee.org › document
This paper presents a neural network-based intrusion detection method for the internet-based attacks on a computer network.
[PDF] Detection Of Intrusion Attacks Using Neural Networks - CEUR-WS
ceur-ws.org › Vol-3200 › paper16
Various neural network structures are considered to detect the optimal neural network by the number of input neurons and the number of hidden layers.
This paper presents a novel approach to detection of malicious network traffic using artificial neural networks suitable for use in deep packet inspection ...
Mar 25, 2024 · IDS-SNNDT is a new intrusion detection system that is based on spike neural networks and decision trees. To reduce latency and minimize device ...
Ideally, an IDS has the capacity to detect in real-time all ( attempted ) intrusions, and to execute work to stop the attack ( for example, modifying firewall ...
This paper concerns intrusion detection. We describe approaches to intrusion detection using neural networks and support vector machines.
This paper proposes the use of deep learning architectures to develop an adaptive and resilient network intrusion detection system (IDS) to detect and classify ...
Intrusion Detection System Using Deep Neural Network for In-Vehicle ...
pubmed.ncbi.nlm.nih.gov › ...
A novel intrusion detection system (IDS) using a deep neural network (DNN) is proposed to enhance the security of in-vehicular network.