Please use this identifier to cite or link to this item: http://localhost:8080/xmlui/handle/123456789/3741
Title: INTEGRATED FRAMEWORK FOR INTRUSION DETECTION THROUGH ADVERSARIAL SAMPLING AND ENHANCED DEEP CORRELATED HIERARCHICAL NETWORK
Authors: Deepa, Venkatraman
Radha, Narayanan
Keywords: intrusion detection
convolution neural network
deep learning
deep hierarchical network
adversarial sampling
Issue Date: 19-Aug-2022
Publisher: International Information and Engineering Technology and Association
Abstract: Intrusion Detection Systems (IDSs) play a critical role in detecting malicious assaults and threats in the network system. This research work proposed a network intrusion detection technique, which combines an Adversarial Sampling and Enhanced Deep Correlated Hierarchical Network for IDS. Initially, the proposed Enhanced Generative Adversarial Networks (EGAN) method is used to raise the minority sample. A balanced dataset can be created in this way, allowing the model to completely learn the properties of minority samples while also drastically minimizing the model training time. Then, create an Enhanced Deep Correlated Hierarchical Network model by using a Bi-Directional Long Short-Term Memory (BiLSTM) to collect temporal characteristics and Cross-correlated Convolution Neural Network (CCNN) to retrieve spatial characteristics. The softmax classifier at the end of BiLSTM is used to classify intrusion data. The traditional NSL-KDD dataset is utilized for the experimentation of the proposed model.
URI: https://www.iieta.org/journals/ria/paper/10.18280/ria.360412
Appears in Collections:c) 2022-Scopus Open Access (PDF)



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