Genetic fuzzy system for intrusion detection: Analysis of improving of multiclass classification accuracy using KDDCup-99 imbalance dataset

Gaffer, Salma M.; Yahia, Moawia Elfaki; Ragab, Khaled

doi:10.1109/his.2012.6421354

Cited by 8 publications

(4 citation statements)

References 8 publications

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“…Specifically, this method is applied in the case study of intrusion detection systems [98,99], following two different schemes. In the first case [100], the alterations to the method follow two aspects: the first one is the use of the SMOTE algorithm to preprocess the data for the subsequent operations; whereas the second one is related to the changes introduced in the genetic tuning of the knowledge base phase that is performed in the FARC-HD method. This genetic procedure changes its evaluation function to the GM performance measure.…”

Section: Efs and Algorithm-level Approachesmentioning

confidence: 99%

Evolutionary Fuzzy Systems: A Case Study in Imbalanced Classification

Fernández

Herrera

2016

Fuzzy Logic and Information Fusion

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The use of evolutionary algorithms for designing fuzzy systems provides them with learning and adaptation capabilities, resulting on what is known as Evolutionary Fuzzy Systems. These types of systems have been successfully applied in several areas of Data Mining, including standard classification, regression problems and frequent pattern mining. This is due to their ability to adapt their working procedure independently of the context we are addressing. Specifically, Evolutionary Fuzzy Systems have been lately applied to a new classification problem showing good and accurate results. We are referring to the problem of classification with imbalanced datasets, which is basically defined by an uneven distribution between the instances of the classes. In this work, we will first introduce some basic concepts on linguistic fuzzy rule based systems. Then, we will present a complete taxonomy for Evolutionary Fuzzy Systems. Then, we will review several significant proposals made in this research area that have been developed for addressing classification with imbalanced datasets. Finally, we will show a case study from which we will highlight the good behavior of Evolutionary Fuzzy Systems in this particular context.

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Section: Efs and Algorithm-level Approachesmentioning

confidence: 99%

Evolutionary Fuzzy Systems: A Case Study in Imbalanced Classification

Fernández

Herrera

2016

Fuzzy Logic and Information Fusion

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“…Khudhu and Samsudin (2022) explored the potential of an autoencoder in feature modeling and implemented a random forest classifier for intrusion detection. A joint operation on multiple schemes is done in the work of (Gaffer et al, 2012), where smote was integrated with a genetic algorithm and fuzzy logic to determine optimal parameters to balance the dataset. Hence, considerable works are on controlling the impact of dataset imbalance or class imbalance on the network IDS.…”

Section: Contribution Of the Proposed Workmentioning

confidence: 99%

Deep Regularization Mechanism for Combating Class Imbalance Problem in Intrusion Detection System for Defending DDoS Attack in SDN

Narender¹,

Yuvaraju²

2023

Journal of Computer Science

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Integration of deep learning into Intrusion Detection Systems (IDS) for Software Defined Networking (SDN) is an emerging field of research. Most of the datasets used to build IDS are highly imbalanced, especially in the case of DDoS attacks, which account for a larger percentage of malicious samples than normal traffic. As a result of a class imbalance, the classification result is distorted since deep learning is limited in its ability to generalize and is misled into favoring the majority class. This study aims to confront the problem of class imbalance by introducing a new deep regularization mechanism that allows the learning model to unlearn biased information. Unlike the existing system, the proposed mechanism integrates two multi-layer neural networks to extract shared information and biased distribution. The first learning model generalizes the data distribution to classify network traffic as normal or attacks class. On the other hand, the second model is integrated with the embedding layers feature of the first model, which learns the bias distribution and then it regressively instructs the first network not to learn this biased information. The proposed regularization scheme is evaluated on the most recent and highly imbalanced network dataset CIC-DDoS2019. The proposed scheme is compared with different supervised learning classifiers executed on the same dataset in the experiment, balanced with the smote technique. The proposed model outperforms other learning techniques and reached an overall precision, recall, and F1-score of 96.71, 97.14, and 96.92%, respectively.

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“…For the class imbalance problem, widely-used approaches include under-sampling, overlap-sampling, ensemble learning and cost sensitive learning [14], [15]. In general, under-sampling is based on category.…”

Section: Related Workmentioning

confidence: 99%

MSML: A Novel Multilevel Semi-Supervised Machine Learning Framework for Intrusion Detection System

Yao

Zhang

et al. 2019

IEEE Internet Things J.

104

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Intrusion detection technology has received increasing attention in recent years. Many researchers have proposed various intrusion detection systems using machine learning methods. However, there are two noteworthy factors affecting the robustness of the model. One is the severe imbalance of network traffic in different categories, and the other is the non-identical distribution between training set and test set in feature space. This paper presents a multi-level intrusion detection model framework named MSML to address these issues. The MSML framework includes four modules: pure cluster extraction, pattern discovery, fine-grained classification and model updating. In the pure cluster module, we introduce an concept of "pure cluster" and propose a hierarchical semi-supervised k-means algorithm (HSK-means) with an aim to find out all the pure clusters. In the pattern discovery module, we define the "unknown pattern and apply cluster based method aiming to find those unknown patterns. Then a test sample is sentenced to labeled known pattern or unlabeled unknown pattern. The fine-grained classification module can achieves fine-grained classification for those unknown pattern samples. The model updating module provides a mechanism for retraining. KDDCUP99 dataset is applied to evaluate MSML. Experimental results show that MSML is superior to other existing intrusion detection models in terms of overall accuracy, F1 score and unknown pattern recognition capability.

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Genetic fuzzy system for intrusion detection: Analysis of improving of multiclass classification accuracy using KDDCup-99 imbalance dataset

Cited by 8 publications

References 8 publications

Evolutionary Fuzzy Systems: A Case Study in Imbalanced Classification

Evolutionary Fuzzy Systems: A Case Study in Imbalanced Classification

Deep Regularization Mechanism for Combating Class Imbalance Problem in Intrusion Detection System for Defending DDoS Attack in SDN

MSML: A Novel Multilevel Semi-Supervised Machine Learning Framework for Intrusion Detection System

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