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

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