An Intrusion Detection System (IDS) using Dimensional Reduction Based on Statistical and SDAE to Enhance SVM in Classification Task
Muh Hanafi · 2022 5th International Conference on Information and Communications Technology (ICOIACT) · 2022
The intrusion detection system was deactivated. An Intrusion Detection System (IDS) is a hardware or software mechanism that monitors the Internet for malicious attacks. It is capable of screening an internetwork for potentially dangerous behavior or security threats. IDS is responsible for maintaining network activity in accordance with the Network-Based Intrusion Detection System (NIDS) or Host-Based Intrusion Detection System (HIDS). IDS works by comparing known normal network activity signatures with attack activity signatures. In this research, a preprocessing using statistical approach, a dimensional reduction and feature selection mechanism called Stack Denoising Auto Encoder (SDAE) succeeded in increasing the effectiveness of SVM work in IDS detection. In this research, we tried our model in two experiment scenarios include binary class and multi class classification experiment. We evaluated the performance of our model using evaluation metrics with a confusion matrix, accuracy, recall, and F1-score. Compared with the results of previous works in the IDS field, our model increased the effectiveness up to more than 2% in NSL-KDD Dataset. In the future, it is possible to integrate SDAE with a deep learning model to enhance the effectiveness of IDS detection.