Authors: Seiba Alhassan, Gaddafi Abdul-Salaam, Yaw Missah
Affiliation: Kwame Nkrumah University of Science and Technology, Dr Hilla Limann Technical University


Keywords: Autoencoder, IDS, encoder, decoder, bottleneck
ABSTRACT. Sensitive data processed, stored, and transmitted on a computer requires a mechanism to protect it from unauthorized access. Several techniques have been proposed, including Intrusion Detection Systems (IDS), to protect computer networks from attacks. Autoencoders, a deep learning technique, have been explored by several researchers aiming to improve the performance of existing IDS. Despite the significant improvements seen with the use of autoencoders, the issues of low detection accuracy and high false alarm rates continue to be major problem. The architecture of a deep autoencoder, including the number of layers, neurons, and the bottleneck, affects its performance. This study is conducted to determine the optimal bottleneck size based on the architecture of a two-layer autoencoder. The study utilizes the benchmark dataset NSL-KDD to train, test, and validate the model. The experimental results from our proposed system reveal that the optimal bottleneck size for an autoencoder is obtained by setting it to 60% of the size of the previous hidden layer.


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