A novel classification and clustering algorithms for intrusion detection system on convolutional neural network

Mathiyalagan Ramasamy, Pamela Vinitha Eric


At present data transmission widely uses wireless network framework for transmitting large volume of data. It generates numerous security problems and privacy issues which laid a way for developing IDS. IDS act as preventive technique in securing computer networks. Previously there are numerous metaheuristic and deep learning algorithms used in IDS for detecting threats. Some are affected by dynamic growth of feature spaces and others are degraded in performance during detection of threats. One fine-grained model for intrusion detection can be developed by selecting accurate features and testing them with the intelligent algorithms. Based on these explorations, in this research IDS is implemented with intelligence from preprocessing to feature classification. At first stage, data preprocessing is done using binning concept to reduce noise. Secondly feature selection is done dynamically using dynamic tree growth algorithm with fire fly optimization techniques. Finally, these features are processed using DTB-FFNN for detecting anomalies perfectly. This DTB-FFNN is evaluated with popular KDD dataset. Our proposed model cable news network (CNN)-classification is compared with existing intelligent techniques: feed forward deep neural network, support vectors machines, decision tree, and CNN-clustering is compared with k-means, density-based spatial clustering of applications with noise (DBSCAN). The experimental outcome proves that dynamic tree based FFNN and CNN-clustering produce higher accuracy than the existing models.


Computer networks; Deep learning; Evolutionary techniques; Feature selection; Feed forward neural network; Intrusions detection system; Network security

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DOI: https://doi.org/10.11591/eei.v11i5.4145


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