Classification of Malicious Intrusion through ANN-CNN Sequential Classifier

Malicious intrusion is an activity that disturbs the normal working of computer systems like stealing the important data, destroy the data, slow functioning in file system. In recent years the malicious intrusion becomes worst because it get more advance day by day and difficult to classify the intrusion. It is now important to prevent the future threats from the malicious intrusion. This paper aims to propose an neural network model named as ANN-CNN sequential classifier for classify the malicious intrusion. The proposed model is highly tuned by its parameter to take best performance from combination of both neural networks model Artificial Neural Network and Convolutional Neural Network. The proposed approach applies on three multiclass datasets names as WSN Dataset in 5 class, Microsoft Malware Dataset in 9 class, Virus Malware Digits in 10 class after an experiment 99.6%,99.6%,98.1% results comes. The proposed model achieve up to 99% accuracy on multiclass datasets.