4.6 Article

A New Data-Balancing Approach Based on Generative Adversarial Network for Network Intrusion Detection System

Related references

Note: Only part of the references are listed.
Article Chemistry, Multidisciplinary

AI-Assisted Security Alert Data Analysis with Imbalanced Learning Methods

Samuel Ndichu et al.

Summary: This paper presents a bidirectional approach to address severe class imbalance in security alert data analysis. The proposed method generates an augmented set of high-quality synthetic positive samples using three oversampling techniques and removes noisy negative samples using a data subsampling algorithm. Experimental results confirm that this approach improves recall and false positive rates, suggesting its potential for more effective and efficient AI-assisted security operations.

APPLIED SCIENCES-BASEL (2023)

Review Computer Science, Artificial Intelligence

GAN-based anomaly detection: A review

Xuan Xia et al.

Summary: This review explores the application of generative adversarial networks (GANs) in anomaly detection, discussing the concept of anomalies, criteria for anomaly detection tasks, and analyzing current challenges and future research directions.

NEUROCOMPUTING (2022)

Article Chemistry, Multidisciplinary

Network Intrusion Detection Model Based on CNN and GRU

Bo Cao et al.

Summary: In this study, a network intrusion detection model that combines a convolutional neural network and a gated recurrent unit is proposed to address the low accuracy and class imbalance problems in existing intrusion detection models. By using a hybrid sampling algorithm, feature selection, and attention mechanism, the proposed model achieves higher classification accuracy and effectively handles class imbalance. The experimental results demonstrate its superiority over existing models.

APPLIED SCIENCES-BASEL (2022)

Article Computer Science, Information Systems

GAN-based imbalanced data intrusion detection system

JooHwa Lee et al.

Summary: This study addresses the issue of data imbalance by utilizing Generative Adversarial Networks (GAN) and proposes a Random Forest model for detection performance. Experimental results demonstrate that the proposed model outperforms other models widely used for data imbalance problems.

PERSONAL AND UBIQUITOUS COMPUTING (2021)

Proceedings Paper Computer Science, Artificial Intelligence

A Comprehensive Survey for Machine Learning and Deep Learning Applications for Detecting Intrusion Detection

Ola M. Surakhi et al.

Summary: The paper discusses the increasing data and network attacks due to the rapid development of computer networks and the internet, leading to the development of new technologies and the use of machine learning and deep learning methods to enhance IDS performance. It highlights the importance of detection methods, benchmark datasets, and IDS environments in improving the performance of IDS systems, and provides a comprehensive overview of recent articles focusing on methodology and dataset advancements, strengths and weaknesses of each work. Future challenges and research directions for ML and DL-based IDS are also discussed.

2021 22ND INTERNATIONAL ARAB CONFERENCE ON INFORMATION TECHNOLOGY (ACIT) (2021)

Article Computer Science, Information Systems

An integrated rule based intrusion detection system: analysis on UNSW-NB15 data set and the real time online dataset

Vikash Kumar et al.

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS (2020)

Article Computer Science, Information Systems

Intrusion Detection in IoT Networks Using Deep Learning Algorithm

Bambang Susilo et al.

INFORMATION (2020)

Article Computer Science, Information Systems

GAN-Based Semi-Supervised Learning Approach for Clinical Decision Support in Health-IoT Platform

Yun Yang et al.

IEEE ACCESS (2019)

Article Computer Science, Hardware & Architecture

A hybrid intrusion detection system based on ABC-AFS algorithm for misuse and anomaly detection

Vajiheh Hajisalem et al.

COMPUTER NETWORKS (2018)

Article Computer Science, Information Systems

UGR'16: A new dataset for the evaluation of cyclostationarity-based network IDSs

Gabriel Macia-Fernandez et al.

COMPUTERS & SECURITY (2018)

Article Computer Science, Theory & Methods

A novel statistical technique for intrusion detection systems

Enamul Kabir et al.

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE (2018)

Proceedings Paper Computer Science, Information Systems

Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery

Thomas Schlegl et al.

INFORMATION PROCESSING IN MEDICAL IMAGING (IPMI 2017) (2017)

Proceedings Paper Computer Science, Artificial Intelligence

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

Christian Ledig et al.

30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017) (2017)