A novel network intrusion detection and application identification approach based on matrix profile and gramian angular summation field

Network intrusions and attacks constitute a serious threat to internet structure. It is necessary to recognize attacks accurately to maintain the integrity of communication networks. Also, network traffic needs to be accurately classified for security and quality of service considerations. Machine learning applications have demonstrated superior performance in classification problems in several areas including network security. Uncovering hidden patterns is considered complex and therefore open to several novel methods. Matrix Profile (MP) and Gramian Angular Summation Field (GASF) are such mathematical algorithms that may successfully be utilized in machine learning models. This thesis presents a novel network intrusion detection and application classification methodology that incorporates MP and GASF with machine learning classifiers. Success of Random Forest model may be enhanced with MP and accuracy of Convolutional Neural Network (CNN) classifier may be improved with GASF. Features are converted to image using GASF to visualize patterns in time-series. Images are then fed to CNN model. Similarities in time-series are computed with MP algorithm as input to Random Forest classifier. Assessment of the proposed model is accomplished with publicly available datasets that contain several intrusions and network traffic application types. Validation results certainly prove that presented approach is successful and applicable. Major achievement of this study is to demonstrate that mathematical algorithms such as MP and GASF may be merged with machine learning models to yield accurate network intrusion and application classification.