Future of generative adversarial networks (GAN) for anomaly detection in network security: A review

Lim, Willone; Yong, Kelvin Sheng Chek; Lau, Bee Theng; Tan, Colin Choon Lin

doi:10.1016/j.cose.2024.103733

Cited by 4 publications

(1 citation statement)

References 60 publications

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“…In addition, as the deep learning approaches have feature learning capabilities, we will investigate how to integrate feature generation into the ensemble models to further improve detection accuracy. Finally, considering the sampling techniques that can be used to improve the prediction performance for imbalanced data, more advanced techniques (e.g., generative adversarial networks [52][53][54][55]) will be investigated to generate synthetic data and handle imbalance problems.…”

Section: Future Workmentioning

confidence: 99%

Sampling-Based Machine Learning Models for Intrusion Detection in Imbalanced Dataset

Fan,

Sohail,

Sabrina

et al. 2024

Electronics

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Cybersecurity is one of the important considerations when adopting IoT devices in smart applications. Even though a huge volume of data is available, data related to attacks are generally in a significantly smaller proportion. Although machine learning models have been successfully applied for detecting security attacks on smart applications, their performance is affected by the problem of such data imbalance. In this case, the prediction model is preferable to the majority class, while the performance for predicting the minority class is poor. To address such problems, we apply two oversampling techniques and two undersampling techniques to balance the data in different categories. To verify their performance, five machine learning models, namely the decision tree, multi-layer perception, random forest, XGBoost, and CatBoost, are used in the experiments based on the grid search with 10-fold cross-validation for parameter tuning. The results show that both the oversampling and undersampling techniques can improve the performance of the prediction models used. Based on the results, the XGBoost model based on the SMOTE has the best performance in terms of accuracy at 75%, weighted average precision at 82%, weighted average recall at 75%, weighted average F1 score at 78%, and Matthews correlation coefficient at 72%. This indicates that this oversampling technique is effective for multi-attack prediction under a data imbalance scenario.

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Section: Future Workmentioning

confidence: 99%

Sampling-Based Machine Learning Models for Intrusion Detection in Imbalanced Dataset

Fan,

Sohail,

Sabrina

et al. 2024

Electronics

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Unveiling the 5G Frontier: Navigating Challenges, Applications, and Measurements in Channel Models and Implementations

Shoaib,

Husnain,

Sayed

et al. 2024

IEEE Access

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In the epoch of the fifth generation (5G) of wireless communication, this survey article illuminates the nuanced landscape of 5G Channel Measurements and Models. It meticulously navigates the challenges, methodologies, and practical implications that define the implementation of this groundbreaking technology. Embarking on the exploration, the article unravels the Challenges in 5G Implementation, addressing technological hurdles and standardized protocols pivotal for seamless integration. Unveiling the Existing Methods and Technologies, it highlights Massive MIMO, millimeter-wave communications, and the integration of artificial intelligence (AI) as transformative elements shaping high-capacity wireless communication. Beyond the technical intricacies, the article delves into the Diverse Applications and Use Cases of 5G, promising a paradigm shift from the Internet of Things (IoT) to mission-critical communications. Frequency Considerations and Channel Bands take center stage, dissecting the critical spectrum domain and exploring high-frequency millimeter-wave bands for enhanced data rates. Addressing concerns, the survey scrutinizes Health Concerns and Regulatory Considerations, presenting a balanced perspective on the impact of 5G networks on health and regulatory landscapes. The Benefits Over 4G and LTE drive the transition, with enhanced data rates, lower latency, and massive device connectivity distinguishing 5G. The environmental impact is considered in Energy Efficiency in 5G Networks, exploring strategies and innovations for making 5G networks more energy-efficient. Global Standards and Interoperability become imperative for 5G's global potential, examining ongoing efforts by standardization bodies for a unified framework. The exploration extends to the realm of 5G Channel Measurements and Models, dissecting Massive MIMO Channel Measurements, Millimeter-Wave Channel Measurements, Beamforming, Antenna Array Configurations, Time-Variant, and Time-Invariant Channel Measurements, Channel Modeling, and Measurement Tools and Techniques. Critically addressing Challenges in 5G Channel Measurements and Models, the article explores limitations and proposes avenues for future research. This survey article serves as a definitive compendium on 5G Channel Measurements and Models, unraveling the complexities, challenges, and opportunities inherent in the deployment of this transformative technology. Its holistic examination provides a valuable resource for researchers, practitioners, and industry stakeholders navigating the dynamic landscape of 5G wireless communication.

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An Unsupervised Generative Adversarial Network System to Detect DDoS Attacks in SDN

Brandão Lent,

da Silva Ruffo,

Carvalho

et al. 2024

IEEE Access

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Future of generative adversarial networks (GAN) for anomaly detection in network security: A review

Cited by 4 publications

References 60 publications

Sampling-Based Machine Learning Models for Intrusion Detection in Imbalanced Dataset

Sampling-Based Machine Learning Models for Intrusion Detection in Imbalanced Dataset

Unveiling the 5G Frontier: Navigating Challenges, Applications, and Measurements in Channel Models and Implementations

An Unsupervised Generative Adversarial Network System to Detect DDoS Attacks in SDN

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