A Comparison of Feature Selection and Feature Extraction in Network Intrusion Detection Systems

Vuong, Tuan-Cuong; Tran, Hung; Trang, Mai Xuan; Ngo, Vu-Duc; Luong, Thien Van

doi:10.23919/apsipaasc55919.2022.9979923

Cited by 1 publication

(1 citation statement)

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“…Improving the machine learning model's efficiency, decreasing overfitting, and improving performance are the goals. Filter methods select features that are independent of one another and are heavily dependent on the output, while wrapper techniques strive to maximize some specified criteria with respect to the feature set as part of the selection process [39].…”

Section: Feature Selection Stagementioning

confidence: 99%

Using Feature Selection Enhancement to Evaluate Attack Detection in the Internet of Things Environment

Harahsheh,

Al-Naimat,

Chen

2024

Electronics

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The rapid evolution of technology has given rise to a connected world where billions of devices interact seamlessly, forming what is known as the Internet of Things (IoT). While the IoT offers incredible convenience and efficiency, it presents a significant challenge to cybersecurity and is characterized by various power, capacity, and computational process limitations. Machine learning techniques, particularly those encompassing supervised classification techniques, offer a systematic approach to training models using labeled datasets. These techniques enable intrusion detection systems (IDSs) to discern patterns indicative of potential attacks amidst the vast amounts of IoT data. Our investigation delves into various aspects of supervised classification, including feature selection, model training, and evaluation methodologies, to comprehensively evaluate their impact on attack detection effectiveness. The key features selected to improve IDS efficiency and reduce dataset size, thereby decreasing the time required for attack detection, are drawn from the extensive network dataset. This paper introduces an enhanced feature selection method designed to reduce the computational overhead on IoT resources while simultaneously strengthening intrusion detection capabilities within the IoT environment. The experimental results based on the InSDN dataset demonstrate that our proposed methodology achieves the highest accuracy with the fewest number of features and has a low computational cost. Specifically, we attain a 99.99% accuracy with 11 features and a computational time of 0.8599 s.

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Section: Feature Selection Stagementioning

confidence: 99%