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About Network Intrusion Detection Based On Deep Neural N
This document summarizes a research paper that proposes using deep neural networks to develop an intrusion detection system for SCADA systems. The researchers extracted 12 features from DNP3 packets to train different deep learning models, including RNNs, LSTMs, CNNs, and GRUs. They measured the accuracy and loss of models trained on both normal and anomalous packets, including denial of service, unsolicited message, and data injection attacks. The goal is to evaluate which deep learning algorithm can most effectively detect anomalies in SCADA network traffic while accurately reflecting the domain semantics.
Detailed Information
| Author: | ['Aditya Singh'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 6 |
| Language: | English |
| Format: | |
| Price: | FREE |
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