Improved intrusion detection method for communication networks using association rule mining and artificial neural networks

Safara, Fatemeh; Souri, Alireza; Serrizadeh, Masoud

doi:10.1049/iet-com.2019.0502

Cited by 33 publications

(15 citation statements)

References 28 publications

(32 reference statements)

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“…At this time, the malicious data center of gravity in wireless personal communication is the largest, but because of the fluctuation of its peripheral data, the data center of gravity cannot always be kept as the maximum value [14], and the absolute difference between aðρÞ and a + 1ðρÞ therefore needs to be calculated to obtain…”

Section: Construction Of Malicious Intrusion Datamentioning

confidence: 99%

[Retracted] Malicious Intrusion Data Mining Algorithm of Wireless Personal Communication Network Supported by Legal Big Data

Ji¹

2021

Wireless Communications and Mobile Computing

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Wireless personal communication network is easily affected by intrusion data in the communication process, resulting in the inability to ensure the security of personal information in wireless communication. Therefore, this paper proposes a malicious intrusion data mining algorithm based on legitimate big data in wireless personal communication networks. The clustering algorithm is used to iteratively obtain the central point of malicious intrusion data and determine its expected membership. The noise in malicious intrusion data is denoised by objective function, and the membership degree of communication data is calculated. The change factor of the neighborhood center of gravity of malicious intrusion data in wireless personal communication network is determined, the similarity between the characteristics of malicious intrusion data by using the Markov distance was determined, and the malicious intrusion data mining of wireless personal communication network supported by legal big data was completed. The experimental results show that the accuracy of mining malicious data is high and the mining time is short.

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Section: Construction Of Malicious Intrusion Datamentioning

confidence: 99%

[Retracted] Malicious Intrusion Data Mining Algorithm of Wireless Personal Communication Network Supported by Legal Big Data

Ji¹

2021

Wireless Communications and Mobile Computing

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“…The datasets used for the experiment were CIC-IDS 2017 [31] and ISCX-IDS 2012 [32]. In most ML methods, transformation is applied before classification is performed [7,33,34]. In our proposed method, we initially start by performing basic data cleaning and then pre-processing on the raw dataset.…”

Section: Methodology and Methodsmentioning

confidence: 99%

Optimizing Filter-Based Feature Selection Method Flow for Intrusion Detection System

Siddiqi

Pak

2020

Electronics

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In recent times, with the advancement in technology and revolution in digital information, networks generate massive amounts of data. Due to the massive and rapid transmission of data, keeping up with security requirements is becoming more challenging. Machine learning (ML)-based intrusion detection systems (IDSs) are considered as one of the most suitable solutions for big data security. Despite the progress in ML, unrelated features can drastically influence the performance of an IDS. Feature selection plays a significant role in improving ML-based IDSs. However, the recent growth of dimensionality in data poses quite a challenge for current feature selection and extraction methods. Due to high data dimensionality, feature selection methods suffer in terms of efficiency and effectiveness. In this paper, we are introducing a new process flow for filter-based feature selection with the help of a transformation technique. Generally, normalization or transformation is implemented before classification. In our proposed model, we implemented and evaluated the effects of normalization before feature selection. To present a clear analysis on the effects of power transformation, five different transformations were implemented and evaluated. Furthermore, we implemented and compared different feature selection methods with the proposed process flow. Results show that compared with existing process flow and feature selection methods, our proposed process flow for feature selection can locate a more relevant set of features with high efficiency and accuracy.

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“…Commonly used network security systems that discover suspicious attacks involve firewalls, intrusion detection systems (IDSs), and intrusion prevention systems (IPSs) [1]. Among them, the task of IDSs is to collect and identify abnormal behaviors in the network [2]. By analyzing captured data packets, IDSs can check legitimate network behaviors, detect the attacks, and report the attacks for further containment.…”

Section: Introductionmentioning

confidence: 99%

A Residual Learning-Based Network Intrusion Detection System

Man

Sun

2021

Security and Communication Networks

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Neural networks have been proved to perform well in network intrusion detection. In order to acquire better features of network traffic, more learning layers are necessarily required. However, according to the results of the previous research, adding layers to the neural networks might fail to improve the classification results. In fact, after the number of layers has reached a certain threshold, performance of the model tends to degrade. In this paper, we propose a network intrusion detection model based on residual learning. After transforming the UNSW-NB15 data set into images, deeper convolutional neural networks with residual blocks are built to learn more critical features. Instead of the cross-entropy loss function, the modified focal loss is calculated to address the class imbalance problem in the training set and identify minor attacks in the testing set. Batch normalization and global average pooling are used to avoid overfitting and enhance the model. Experimental results show that the proposed model can improve attack detection accuracy compared with existing models.

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Improved intrusion detection method for communication networks using association rule mining and artificial neural networks

Cited by 33 publications

References 28 publications

[Retracted] Malicious Intrusion Data Mining Algorithm of Wireless Personal Communication Network Supported by Legal Big Data

[Retracted] Malicious Intrusion Data Mining Algorithm of Wireless Personal Communication Network Supported by Legal Big Data

Optimizing Filter-Based Feature Selection Method Flow for Intrusion Detection System

A Residual Learning-Based Network Intrusion Detection System

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