A Network Intrusion Detection Method Based on Domain Confusion

Article Computer Science, Artificial Intelligence

Generalizing to Unseen Domains: A Survey on Domain Generalization

Jindong Wang et al.

Summary: This paper provides the first review of recent advances in domain generalization, discussing the formal definition, related fields, theories, algorithms, datasets, applications, and potential research topics. It categorizes algorithms into data manipulation, representation learning, and learning strategy, and presents popular algorithms in each category. It also introduces a codebase for fair evaluation.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2023)

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Article Computer Science, Theory & Methods

Generative Adversarial Networks (GANs): Challenges, Solutions, and Future Directions

Divya Saxena et al.

Summary: This study provides a comprehensive survey of the advancements in design and optimization solutions for Generative Adversarial Networks (GANs). It proposes a new taxonomy to structure the solutions and discusses different GAN variants within each solution and their relationships. Promising research directions in this rapidly growing field are also presented.

ACM COMPUTING SURVEYS (2022)

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Review Computer Science, Information Systems

A systematic literature review of methods and datasets for anomaly-based network intrusion detection

Zhen Yang et al.

Summary: With the rapid evolution of network techniques, network attacks are becoming more sophisticated and threatening. Network intrusion detection is widely recognized as an effective method to address network threats. Anomaly-based network intrusion detection is an important research direction, but there is a lack of systematic literature reviews on recent techniques and datasets. In this study, we conducted a systematic literature review of 119 top-cited papers on anomaly-based intrusion detection, investigating the technical landscape of the field from various perspectives, and identifying unsolved research challenges and future research directions.

COMPUTERS & SECURITY (2022)

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Article Computer Science, Information Systems

CRND: An Unsupervised Learning Method to Detect Network Anomaly

YanZe Qu et al.

Summary: Network anomaly detection system is crucial for maintaining network system security, and models based on deep learning have advantages in this area. However, most research is based on supervised learning, which has difficulties in obtaining labeled data. This paper proposes an unsupervised learning method CRND, achieving high accuracy in network anomaly detection through contrastive learning.

SECURITY AND COMMUNICATION NETWORKS (2022)

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Article Computer Science, Artificial Intelligence