Stacked Autoencoder-Based Intrusion Detection System to Combat Financial Fraudulent

With the rapid progress of wireless communication technologies along with their digital revolutions, the quantity of the Internet of Things (IoT) has been increased by manifolds, resulting in a huge increase in data volume and network traffic. It became easier for an intruder to pretend as a valid service provider, and generate different types of network attacks. This becomes even more severe when the service involves digital financial transactions for possible urbanization. This article proposes an intrusion detection system (IDS) based on a stacked autoencoder (AE) and a deep neural network (DNN). The stacked AE learns the features of the input network record in an unsupervised manner to decrease the feature width. Then, the DNN is trained in a supervised manner to extract deep-learned features for the classifier. In the proposed system, the stacked AE has two latent layers and the DNN has two or three layers, where each layer has a fully connected layer, a batch normalization, and a dropout. The system was evaluated on three publicly available data sets: 1) KDDCup99; 2) NSL-KDD; and 3) aegean Wi-Fi intrusion data sets. Experimental results exhibited that the proposed IDS achieved 94.2%, 99.7%, and 99.9% accuracy, respectively, for multiclass classification.

Automated summary

With the rapid growth of the Internet of Things (IoT) and the increase in data volume and network traffic, it has become easier for intruders to launch network attacks. This article proposes an intrusion detection system (IDS) based on stacked autoencoders (AE) and deep neural networks (DNN) to address this issue. The system achieved high accuracy rates of 94.2%, 99.7%, and 99.9% for multiclass classification on different datasets.