A Classification of Attacks in an IDS Using Sparse Convolutional Autoencoder and DNN

The network security plays an important role in this modern world. After emerging modern technologies like cloud computing, big data, Internet of Things (IOT), Blockchain and so forth, network security set more complex task to firewalls and cyber security department. Network intrusion detection is a software system or a device which helps in monitoring unauthorized access and vulnerabilities in the complex networks. We propose a hybrid model using Sparse Convolution Autoencoder (SCA) along with Deep Neural Network for intrusion detection in the communication network. We applied our model on KDDCup’99 dataset and achieved an accuracy of 99.7%.