A New Deep Learning Approach for Anomaly Base IDS using Memetic Classifier

Mohammadi, Shahriar; Namadchian, Amin

doi:10.15837/ijccc.2017.5.2972

Cited by 25 publications

(7 citation statements)

References 9 publications

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“…A classifier combination tactic is generally preferred to substitute a single classifier. Mohammadi and Namadchian [23] proposed a new integrated construction method, which used the weights generated by the particle swarm optimization (PSO) to create a classifier set for higher intrusion detection accuracy. Gu et al [24] applied a logarithmic marginal density ratio transformation on the original features in order to obtain the newest and better-quality transformed training data and then to use SVM integration to establish an intrusion detection framework.…”

Section: Related Workmentioning

confidence: 99%

An Intrusion Detection Method Based on Decision Tree-Recursive Feature Elimination in Ensemble Learning

Lian

Nie

Jia

et al. 2020

Mathematical Problems in Engineering

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With the rapid development of the Internet, various forms of network attack have emerged, so how to detect abnormal behavior effectively and to recognize their attack categories accurately have become an important research subject in the field of cyberspace security. Recently, many hot machine learning-based approaches are applied in the Intrusion Detection System (IDS) to construct a data-driven model. The methods are beneficial to reduce the time and cost of manual detection. However, the real-time network data contain an ocean of redundant terms and noises, and some existing intrusion detection technologies have lower accuracy and inadequate ability of feature extraction. In order to solve the above problems, this paper proposes an intrusion detection method based on the Decision Tree-Recursive Feature Elimination (DT-RFE) feature in ensemble learning. We firstly propose a data processing method by the Decision Tree-Based Recursive Elimination Algorithm to select features and to reduce the feature dimension. This method eliminates the redundant and uncorrelated data from the dataset to achieve better resource utilization and to reduce time complexity. In this paper, we use the Stacking ensemble learning algorithm by combining Decision Tree (DT) with Recursive Feature Elimination (RFE) methods. Finally, a series of comparison experiments by cross-validation on the KDD CUP 99 and NSL-KDD datasets indicate that the DT-RFE and Stacking-based approach can better improve the performance of the IDS, and the accuracy for all kinds of features is higher than 99%, except in the case of U2R accuracy, which is 98%.

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Section: Related Workmentioning

confidence: 99%

An Intrusion Detection Method Based on Decision Tree-Recursive Feature Elimination in Ensemble Learning

Lian

Nie

Jia

et al. 2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…Accordingly, deep learning technology has been applied to the traditional NN architecture of deep neural networks (DNN). Mohammadi et al [ 18 ] proposed an IDS model based on AE and Memetic algorithms. CNN is also a popular DNN with a hierarchical structure similar to digital images.…”

Section: Related Workmentioning

confidence: 99%

A Diffusion Model Based on Network Intrusion Detection Method for Industrial Cyber-Physical Systems

Tang

et al. 2023

Sensors

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Industrial Cyber-Physical Systems (ICPS) connect intelligent manufacturing equipment equipped with sensors, wireless and RFID communication technologies through data interaction, which makes the interior of the factory, even between factories, become a whole. However, intelligent factories will suffer information leakage and equipment damage when being attacked by ICPS intrusion. Therefore, the network security of ICPS cannot be ignored, and researchers have conducted in-depth research on network intrusion detection for ICPS. Though machine learning and deep learning methods are often used for network intrusion detection, the problem of data imbalance can cause the model to pay attention to the misclassification cost of the prevalent class, but ignore that of the rare class, which seriously affects the classification performance of network intrusion detection models. Considering the powerful generative power of the diffusion model, we propose an ICPS Intrusion Detection system based on the Diffusion model (IDD). Firstly, data corresponding to the rare class is generated by the diffusion model, which makes the training dataset of different classes balanced. Then, the improved BiLSTM classification network is trained on the balanced training set. Extensive experiments are conducted to show that the IDD method outperforms the existing baseline method on several available datasets.

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“…Musafer et al [20] designed a sparse autoencoder for intrusion detection system on a reliable and updated network attacks dataset CICIDS2017. The authors proposed a deep learning model namely a memetic algorithm for abnormal traffic detection and tested it on two well-known datasets that are NSLKDD and KDD-CUP 99 [21]. Feature augmentation has been applied along with SVM to provide an effective intrusion detection framework and achieved robust results in terms of training speed and faulty alarm rate [22].…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning-Based Smart Framework for Cyber-Physical and Satellite System Security Threats Detection

et al. 2022

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An intrusion detection system serves as the backbone for providing high-level network security. Different forms of network attacks have been discovered and they continue to become gradually more sophisticated and complicated. With the wide use of internet-based applications, cyber security has become an important research area. Despite the availability of many existing intrusion detection systems, intuitive cybersecurity systems are needed due to alarmingly increasing intrusion attacks. Furthermore, with new intrusion attacks, the efficacy of existing systems depletes unless they evolve. The lack of real datasets adds further difficulties to properly investigating this problem. This study proposes an intrusion detection approach for the modern network environment by considering the data from satellite and terrestrial networks. Incorporating machine learning models, the study proposes an ensemble model RFMLP that integrates random forest (RF) and multilayer perceptron (MLP) for increasing intrusion detection performance. For analyzing the efficiency of the proposed framework, three different datasets are used for experiments and validation, namely KDD-CUP 99, NSL-KDD, and STIN. In addition, performance comparison with state-of-the-art models is performed which suggests that the RFMLP can detect intrusion attacks with high accuracy than the existing approaches.

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A New Deep Learning Approach for Anomaly Base IDS using Memetic Classifier

Cited by 25 publications

References 9 publications

An Intrusion Detection Method Based on Decision Tree-Recursive Feature Elimination in Ensemble Learning

An Intrusion Detection Method Based on Decision Tree-Recursive Feature Elimination in Ensemble Learning

A Diffusion Model Based on Network Intrusion Detection Method for Industrial Cyber-Physical Systems

A Deep Learning-Based Smart Framework for Cyber-Physical and Satellite System Security Threats Detection

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