![]() ![]() SABD integrates the feature extraction of stacked sparse contractive autoencoders with the classification ability of attention-based bidirectional LSTM. SABD integrates Stacked sparse contractive autoencoders, Attention-based Bidirectional long-term and short-term memory (LSTM), and Decision fusion. This work proposes a new hybrid classification method named SABD for network intrusion detection. Currently, classification methods with autoencoders for feature learning have been proved to be suitable for the network intrusion detection. ![]() Keywords: Application of Artificial Intelligence, Intelligent Internet Systems, Neural Networks and their ApplicationsĪbstract: Accurately identifying network intrusion cannot only help individuals and enterprises better deal with network security problems but also maintain the Internet environment. ![]()
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