Necessary Information to Know to Solve Class Imbalance Problem: From a User’s Perspective

Rekha, Gillala; Tyagi, Amit Kumar

doi:10.1007/978-3-030-29407-6_46

Cited by 3 publications

(1 citation statement)

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“…Data imbalance is a common feature of realistic, complex network systems; there are plenty of data samples for majority-class attacks and routine traffic but few examples for minority-class attacks. Due to their incapacity to comprehend the characteristics of minority attack classes, traditional NIDS models have poor detection accuracy and even fail to identify attacks using minority-class sample attacks, jeopardizing network security [46].…”

Section: Related Workmentioning

confidence: 99%

A High-Performance Multimodal Deep Learning Model for Detecting Minority Class Sample Attacks

Yu,

Xu,

Jiang

2023

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A large amount of sensitive information is generated in today’s evolving network environment. Some hackers utilize low-frequency attacks to steal sensitive information from users. This generates minority attack samples in real network traffic. As a result, the data distribution in real network traffic is asymmetric, with a large number of normal traffic and a rare number of attack traffic. To address the data imbalance problem, intrusion detection systems mainly rely on machine-learning-based methods to detect minority attacks. Although this approach can detect minority attacks, the performance is not satisfactory. To solve the above-mentioned problems, this paper proposes a novel high-performance multimodal deep learning method. The method is based on deep learning. It captures the features of minority class attacks based on generative adversarial networks, which in turn generate high-quality minority class sample attacks. Meanwhile, it uses the designed multimodal deep learning model to learn the features of minority attacks. The integrated classifier then utilizes the learned features for multi-class classification. This approach achieves 99.55% and 99.95% F-measure, 99.56% and 99.96% accuracy on the CICIDS2017 dataset and the NSL-KDD dataset, respectively, with the highest false positives at only 3.4%. This exceeds the performance of current state-of-the-art methods.

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Section: Related Workmentioning

confidence: 99%