A deep learning method with wrapper based feature extraction for wireless intrusion detection system

Kasongo, Sydney Mambwe; Sun, Yanxia

doi:10.1016/j.cose.2020.101752

Cited by 255 publications

(126 citation statements)

References 22 publications

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“…Alipourfard et al [87] worked on the hyperspectral images high dimensionality and proposed a system to reduce it. The proposed system was a combination of CNN and the subspace feature extraction method.…”

Section: Sellami and Farahmentioning

confidence: 99%

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A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction

Zebari¹,

Abdulazeez²,

Zeebaree³

et al. 2020

JASTT

607

210

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Due to sharp increases in data dimensions, working on every data mining or machine learning (ML) task requires more efficient techniques to get the desired results. Therefore, in recent years, researchers have proposed and developed many methods and techniques to reduce the high dimensions of data and to attain the required accuracy. To ameliorate the accuracy of learning features as well as to decrease the training time dimensionality reduction is used as a pre-processing step, which can eliminate irrelevant data, noise, and redundant features. Dimensionality reduction (DR) has been performed based on two main methods, which are feature selection (FS) and feature extraction (FE). FS is considered an important method because data is generated continuously at an ever-increasing rate; some serious dimensionality problems can be reduced with this method, such as decreasing redundancy effectively, eliminating irrelevant data, and ameliorating result comprehensibility. Moreover, FE transacts with the problem of finding the most distinctive, informative, and decreased set of features to ameliorate the efficiency of both the processing and storage of data. This paper offers a comprehensive approach to FS and FE in the scope of DR. Moreover, the details of each paper, such as used algorithms/approaches, datasets, classifiers, and achieved results are comprehensively analyzed and summarized. Besides, a systematic discussion of all of the reviewed methods to highlight authors' trends, determining the method(s) has been done, which significantly reduced computational time, and selecting the most accurate classifiers. As a result, the different types of both methods have been discussed and analyzed the findings.

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Section: Sellami and Farahmentioning

confidence: 99%

“…On the other hand, 21 feature extraction methods have been reviewed in this study and summarized in Table IV. Among them 7 methods ( [92], [77], [82], [84], [87], [88], and [91]) depended on deep learning (CNN and DNN). CNN and LBP were used in research [92] and achieved high-efficiency accuracy.…”

Section: Sellami and Farahmentioning

confidence: 99%

A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction

Zebari¹,

Abdulazeez²,

Zeebaree³

et al. 2020

JASTT

607

210

View full text Add to dashboard Cite

show abstract

“…After pre-training process, the fine-tuning procedure is used to adjust the parameters such as the weights and biases. Take the hth hidden layer as an example, and define the learning rate as  , the weight matrix and the bias vector of it can be updated as follows: is the activation probability of the jth element of the ( 1)  h th hidden layer, and…”

Section: The Fine-tuning Process Of Cdbnmentioning

confidence: 99%

“…With the extensive popularization of Wireless Local Area Networks (WLAN) technology used in hardware devices, the IEEE 802.11 protocol based short-distance transmission wireless network is facing great security challenges [1]. Any illegal users may have access to the local area network through the wireless access point [2,3].…”

Section: Introductionmentioning

confidence: 99%

“…Kim applied Long Short-Term Memory (LSTM) architecture, the experimental results show this deep learning approach was effective [14]. Kasongo proposed a Feed-Forward Deep Neural Network (FFDNN) based wireless intrusion detection method, and the experimental results show this method can achieve high detection accuracy [15]. At the same time, Al-Abassi proposed an ensemble method using Deep Neural Network (DNN) and Decision Tree (DT), and this method shows it can achieve high detection accuracy with low false-positive [16].…”

Section: Introductionmentioning

confidence: 99%

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Real-Time Intrusion Detection in Wireless Network: A Deep Learning-Based Intelligent Mechanism

Yang

Yin

et al. 2020

IEEE Access

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With the development of the wireless network techniques, the number of cyber-attack increases significantly, which has seriously threat the security of Wireless Local Area Network (WLAN). The traditional intrusion detection technology is a prevalent area of study for numerous years, but it may not have a good detection performance in a real-time way. Therefore, it is urgent to design a detection mechanism to detect the attacks timely. In this paper, we exploit a CDBN (Conditional Deep Belief Network)-based intrusion detection mechanism to recognize the attack features and perform the wireless network intrusion detection in real time. To avoid the impact of the imbalanced dataset and the data redundancy on the detection accuracy, a windowbased instance selection algorithm-SamSelect‖ is adopted to undersample the majority class data samples, and a Stacked Contractive Auto-Encoder (SCAE) algorithm is proposed to reduce the dimension of the data samples. By doing so, our proposed mechanism can effectively detect the potential attack and achieve high accuracy. The experiment results show that CDBN can be effectively combined with-SamSelect‖ and SCAE, and the proposed mechanism has a high detection speed and accuracy, with the average detection time 1.14 ms and the detection accuracy 0.974.

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A Wrapper based feature extraction framework based on AlexNet deep convolutional neural network parameters optimized using gradient‐based optimizer for mammogram images

Sakthivel¹,

Subasree

Malik

et al. 2022

Concurrency and Computation

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In this manuscript, a Wrapper based feature extraction framework based on AlexNet deep convolutional neural network (ADCNN) with gradient‐based optimizer (GBO) is proposed for early detection of breast cancer. In this input images are occupied as mini‐mammography image analysis society (MIAS) database. Then the images are pre‐processed to eliminate that noises using Markov random field (MRF) method. Image features are extracted by the process of Wrapper based feature extraction framework with ADCNN. Then, the weight parameters of ADCNN are optimizing through the aid of GBO. Then the mammogram images are characterized as normal or abnormal (malignant and benign) with SVM classifier. The simulation process is implemented on MATLAB platform. The proposed ADCNN‐SVM‐GBO attains higher accuracy 34.64%, 28.86%, 19.86%, 24.64%, 32.86%, higher Precision 28.07%, 18.96%, 16.86%, 25.86%, 26.86%, higher recall 27.86%, 32.54%, 27.86%, 23.95%, 19.97%, and the efficiency of the proposed method FE‐ADCNN‐GBO‐SVM is likened with the existing processes. Classification of mammograms depends features removal processes with support vector machine (FE‐LBP‐GLCM‐SVM), breast cancer classification with global discriminate features on mammographic images (FE‐GLCM‐ANN), enhancing breast cancer classification with (SMOTE) technique and pectoral muscle removal on mammographic images (FE‐SMOTE‐RF), application of artificial intelligence depends deep learning on breast cancer screening and imaging diagnosis (FE‐CNN‐CDCNN) respectively.

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A deep learning method with wrapper based feature extraction for wireless intrusion detection system

Cited by 255 publications

References 22 publications

A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction

A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction

Real-Time Intrusion Detection in Wireless Network: A Deep Learning-Based Intelligent Mechanism

A Wrapper based feature extraction framework based on AlexNet deep convolutional neural network parameters optimized using gradient‐based optimizer for mammogram images

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