Cyber Attack Detection by Using Neural Network Approaches: Shallow Neural Network, Deep Neural Network and AutoEncoder
| Autor: | M. Ali Aydin, Serpil Üstebay, Zeynep Turgut |
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| Rok vydání: | 2019 |
| Předmět: |
Complex data type
0209 industrial biotechnology Artificial neural network Computer science business.industry Computer Science::Neural and Evolutionary Computation Botnet Denial-of-service attack 02 engineering and technology Machine learning computer.software_genre Autoencoder 020901 industrial engineering & automation Brute force Server 0202 electrical engineering electronic engineering information engineering Cyber-attack 020201 artificial intelligence & image processing Artificial intelligence business computer Computer Science::Cryptography and Security |
| Zdroj: | Computer Networks ISBN: 9783030219512 CN |
| DOI: | 10.1007/978-3-030-21952-9_11 |
| Popis: | As the accuracy rate of artificial intelligence based applications increased, they have started to be used in different areas. Artifical Neural Networks (ANN) can be very successful for extracting meaningful data from features by processing complex data. Well-trained models can solve difficult problems with high a high accuracy rate. In this study, 2 different ANN models have been developed to detect malicious users who want to access high-security servers. These models are tested from simple to complex: Shallow Neural Network (SNN), Deep Neural Network (DNN), and Auto Encoder are used to reduce features. All models are trained with CICIDS2017 dataset. Server connection requests are classified as normal or malicious (Brute Force, Web Attack, In ltration, Botnet or DDoS) with 98.45% accuracy rate. |
| Databáze: | OpenAIRE |
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