| Autor: |
Mihai-Lica Pura, Andrei-Marius Avram, Stefan-Adrian Toma |
| Rok vydání: |
2019 |
| Předmět: |
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| Zdroj: |
2019 11th International Conference on Electronics, Computers and Artificial Intelligence (ECAI). |
| DOI: |
10.1109/ecai46879.2019.9042128 |
| Popis: |
Network Intrusion Detection Systems (NIDS) are a critical component in any security system and their role is to monitor traffic on a network, looking for suspicious activity (e.g. attacks, unauthorized activities). Neural networks have become an increasingly popular solution for implementing network intrusion detection. Their ability to identify complex patterns and behaviours have made them a suitable solution for differentiating between normal and malicious traffic. One of the major problems encountered in implementing such neural networks is that they are computational and memory expensive and they can cause massive overhead in small embedded devices. This paper proposes a deep autoencoded dense neural network for detecting network attacks, trained and evaluated on NSL-KDD dataset that can satisfy the memory and real time constraints of such systems. The hyperparameters of the model are tuned, and three variants with different sizes and accuracies are proposed in order to produce flexibility in choosing the correct architecture for the desired platform. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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