DOC-IDS: A Deep Learning-Based Method for Feature Extraction and Anomaly Detection in Network Traffic

• Published in: Sensors Vol. 22; no. 12; p. 4405
• Main Authors: Yoshimura, Naoto, Kuzuno, Hiroki, Shiraishi, Yoshiaki, Morii, Masakatu
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2022; MDPI
• Subjects: Algorithms; anomaly detection; autoencoder; Chemical technology; Classification; convolutional neural network; Datasets; Deep learning; deep learning; feature extraction; anomaly detection; convolutional neural network; autoencoder; intrusion detection; Experiments; feature extraction; Feature selection; intrusion detection; Machine Learning; Methods; Neural networks; Neural Networks, Computer; Support vector machines; TP1-1185; Value engineering

With the growing diversity of cyberattacks in recent years, anomaly-based intrusion detection systems that can detect unknown attacks have attracted significant attention. Furthermore, a wide range of studies on anomaly detection using machine learning and deep learning methods have been conducted....