A Hybrid Anomaly Detection System Using CNN and LS-SVM Methods

As the number of social media users increases, network security becomes more important. The Intrusion Detection System (IDS) is one of the key elements of network security to detect threats and attacks. Since the network data is voluminous and has many features, the feature extraction method alongside classification methods is a common solution to this issue. Convolutional Neural Network (CNN) is a proper feature extraction method to manage the network data complexity. Support-Vector Machine (SVM) is an outstanding method to classify network data moving high-dimensional data to high-dimensional feature space. In this paper, we propose a hybrid anomaly detection system using CNN for feature extraction and Least-Squares SVM (LS-SVM) for classification. The KDDCUP’۹۹ and UNSW-NB۱۵ datasets are considered in our research to simulate real network traffic and cover new attack types. The experimental results show that our hybrid approach improves the F۱-score and AUC metrics compared to its counterparts.

کلیدواژه ها:

Network Anomaly Detection ، Deep Learning ، Convolutional Neural Network ، Support Vector Machine ، Intrusion Detection ، Network Security