An Agile Approach to Identify Single and Hybrid Normalization for Enhancing Machine Learning-Based Network Intrusion Detection

Article Chemistry, Analytical

An IoT-Focused Intrusion Detection System Approach Based on Preprocessing Characterization for Cybersecurity Datasets

Xavier Larriva-Novo et al.

Summary: Security in IoT networks is crucial due to the high amount of data handled, and new intrusion detection techniques based on machine learning algorithms show promise in improving accuracy. This research evaluates multiple preprocessing techniques for a machine learning neural network algorithm using different benchmark datasets, ultimately showing a potential accuracy enhancement of up to 45% by applying traffic categorization and various preprocessing methods.

SENSORS (2021)

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Article Computer Science, Information Systems

CorrAUC: A Malicious Bot-IoT Traffic Detection Method in IoT Network Using Machine-Learning Techniques

Muhammad Shafiq et al.

Summary: Researchers have proposed a new feature selection method and algorithm to accurately detect malicious traffic in IoT networks. By integrating TOPSIS and Shannon entropy methods to validate the selected features for malicious traffic identification in IoT networks, the experimental results have shown that this method is efficient and can achieve over 96% accuracy on average.

IEEE INTERNET OF THINGS JOURNAL (2021)

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Article Computer Science, Artificial Intelligence

Transforming variables to central normality

Jakob Raymaekers et al.

Summary: This article proposes modifications to the Box-Cox and Yeo-Johnson transformations, using a robust estimator for the transformation parameter to make the transformed data approximately normal in the center while allowing for some outliers. It outperforms existing techniques in simulation studies and real data analysis.

MACHINE LEARNING (2021)

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Review Engineering, Electrical & Electronic

A Unifying Review of Deep and Shallow Anomaly Detection

Lukas Ruff et al.

Summary: Deep learning approaches have significantly enhanced anomaly detection performance on complex data sets, sparking a renewed interest in the field. Various new methods have been introduced, including those based on generative models, one-class classification, and reconstruction. It is crucial to bring these methods together and explore the underlying principles and connections between classic and novel approaches for future research and development in anomaly detection.

PROCEEDINGS OF THE IEEE (2021)

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Proceedings Paper Computer Science, Artificial Intelligence

An Adaptive Deep-Ensemble Anomaly-Based Intrusion Detection System for the Internet of Things

Khalid Albulayhi et al.

Summary: IoT technology is widely used but faces security concerns, with anomaly detection being one proposed solution. Current solutions lack adaptive capabilities and effective feature selection techniques.

2021 IEEE WORLD AI IOT CONGRESS (AIIOT) (2021)

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Article Multidisciplinary Sciences