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Title |
Optimization of Cyber-Attack Detection Using the Deep Learning Network |
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Author |
Lai Van Duong |
| Citation |
Vol. 21 No. 7 pp. 159-168 |
|
Abstract |
Detecting cyber-attacks using machine learning or deep learning is being studied and applied widely in network intrusion detection systems. We noticed that the application of deep learning algorithms yielded many good results. However, because each deep learning model has different architecture and characteristics with certain advantages and disadvantages, so those deep learning models are only suitable for specific datasets or features. In this paper, in order to optimize the process of detecting cyber-attacks, we propose the idea of building a new deep learning network model based on the association and combination of individual deep learning models. In particular, based on the architecture of 2 deep learning models: Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), we combine them into a combined deep learning network for detecting cyber-attacks based on network traffic. The experimental results in Section IV.D have demonstrated that our proposal using the CNN-LSTM deep learning model for detecting cyber-attacks based on network traffic is completely correct because the results of this model are much better than some individual deep learning models on all measures. |
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Keywords |
cyber attack, combined deep learning; abnormal behaviors of cyber-attacks; detection attacks |
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URL |
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Title |
Optimization of Cyber-Attack Detection Using the Deep Learning Network |
|
Author |
Lai Van Duong |
| Citation |
Vol. 21 No. 7 pp. 159-168 |
|
Abstract |
Detecting cyber-attacks using machine learning or deep learning is being studied and applied widely in network intrusion detection systems. We noticed that the application of deep learning algorithms yielded many good results. However, because each deep learning model has different architecture and characteristics with certain advantages and disadvantages, so those deep learning models are only suitable for specific datasets or features. In this paper, in order to optimize the process of detecting cyber-attacks, we propose the idea of building a new deep learning network model based on the association and combination of individual deep learning models. In particular, based on the architecture of 2 deep learning models: Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), we combine them into a combined deep learning network for detecting cyber-attacks based on network traffic. The experimental results in Section IV.D have demonstrated that our proposal using the CNN-LSTM deep learning model for detecting cyber-attacks based on network traffic is completely correct because the results of this model are much better than some individual deep learning models on all measures. |
|
Keywords |
cyber attack, combined deep learning; abnormal behaviors of cyber-attacks; detection attacks |
|
URL |
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Title |
Optimization of Cyber-Attack Detection Using the Deep Learning Network |
|
Author |
Lai Van Duong |
| Citation |
Vol. 21 No. 7 pp. 159-168 |
|
Abstract |
Detecting cyber-attacks using machine learning or deep learning is being studied and applied widely in network intrusion detection systems. We noticed that the application of deep learning algorithms yielded many good results. However, because each deep learning model has different architecture and characteristics with certain advantages and disadvantages, so those deep learning models are only suitable for specific datasets or features. In this paper, in order to optimize the process of detecting cyber-attacks, we propose the idea of building a new deep learning network model based on the association and combination of individual deep learning models. In particular, based on the architecture of 2 deep learning models: Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), we combine them into a combined deep learning network for detecting cyber-attacks based on network traffic. The experimental results in Section IV.D have demonstrated that our proposal using the CNN-LSTM deep learning model for detecting cyber-attacks based on network traffic is completely correct because the results of this model are much better than some individual deep learning models on all measures. |
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Keywords |
cyber attack, combined deep learning; abnormal behaviors of cyber-attacks; detection attacks |
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URL |
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Title |
Optimization of Cyber-Attack Detection Using the Deep Learning Network |
|
Author |
Lai Van Duong |
| Citation |
Vol. 21 No. 7 pp. 159-168 |
|
Abstract |
Detecting cyber-attacks using machine learning or deep learning is being studied and applied widely in network intrusion detection systems. We noticed that the application of deep learning algorithms yielded many good results. However, because each deep learning model has different architecture and characteristics with certain advantages and disadvantages, so those deep learning models are only suitable for specific datasets or features. In this paper, in order to optimize the process of detecting cyber-attacks, we propose the idea of building a new deep learning network model based on the association and combination of individual deep learning models. In particular, based on the architecture of 2 deep learning models: Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), we combine them into a combined deep learning network for detecting cyber-attacks based on network traffic. The experimental results in Section IV.D have demonstrated that our proposal using the CNN-LSTM deep learning model for detecting cyber-attacks based on network traffic is completely correct because the results of this model are much better than some individual deep learning models on all measures. |
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Keywords |
cyber attack, combined deep learning; abnormal behaviors of cyber-attacks; detection attacks |
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URL |
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Title |
Optimization of Cyber-Attack Detection Using the Deep Learning Network |
|
Author |
Lai Van Duong |
| Citation |
Vol. 21 No. 7 pp. 159-168 |
|
Abstract |
Detecting cyber-attacks using machine learning or deep learning is being studied and applied widely in network intrusion detection systems. We noticed that the application of deep learning algorithms yielded many good results. However, because each deep learning model has different architecture and characteristics with certain advantages and disadvantages, so those deep learning models are only suitable for specific datasets or features. In this paper, in order to optimize the process of detecting cyber-attacks, we propose the idea of building a new deep learning network model based on the association and combination of individual deep learning models. In particular, based on the architecture of 2 deep learning models: Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), we combine them into a combined deep learning network for detecting cyber-attacks based on network traffic. The experimental results in Section IV.D have demonstrated that our proposal using the CNN-LSTM deep learning model for detecting cyber-attacks based on network traffic is completely correct because the results of this model are much better than some individual deep learning models on all measures. |
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Keywords |
cyber attack, combined deep learning; abnormal behaviors of cyber-attacks; detection attacks |
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URL |
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Title |
Optimization of Cyber-Attack Detection Using the Deep Learning Network |
|
Author |
Lai Van Duong |
| Citation |
Vol. 21 No. 7 pp. 159-168 |
|
Abstract |
Detecting cyber-attacks using machine learning or deep learning is being studied and applied widely in network intrusion detection systems. We noticed that the application of deep learning algorithms yielded many good results. However, because each deep learning model has different architecture and characteristics with certain advantages and disadvantages, so those deep learning models are only suitable for specific datasets or features. In this paper, in order to optimize the process of detecting cyber-attacks, we propose the idea of building a new deep learning network model based on the association and combination of individual deep learning models. In particular, based on the architecture of 2 deep learning models: Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), we combine them into a combined deep learning network for detecting cyber-attacks based on network traffic. The experimental results in Section IV.D have demonstrated that our proposal using the CNN-LSTM deep learning model for detecting cyber-attacks based on network traffic is completely correct because the results of this model are much better than some individual deep learning models on all measures. |
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Keywords |
cyber attack, combined deep learning; abnormal behaviors of cyber-attacks; detection attacks |
|
URL |
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Title |
Optimization of Cyber-Attack Detection Using the Deep Learning Network |
|
Author |
Lai Van Duong |
| Citation |
Vol. 21 No. 7 pp. 159-168 |
|
Abstract |
Detecting cyber-attacks using machine learning or deep learning is being studied and applied widely in network intrusion detection systems. We noticed that the application of deep learning algorithms yielded many good results. However, because each deep learning model has different architecture and characteristics with certain advantages and disadvantages, so those deep learning models are only suitable for specific datasets or features. In this paper, in order to optimize the process of detecting cyber-attacks, we propose the idea of building a new deep learning network model based on the association and combination of individual deep learning models. In particular, based on the architecture of 2 deep learning models: Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), we combine them into a combined deep learning network for detecting cyber-attacks based on network traffic. The experimental results in Section IV.D have demonstrated that our proposal using the CNN-LSTM deep learning model for detecting cyber-attacks based on network traffic is completely correct because the results of this model are much better than some individual deep learning models on all measures. |
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Keywords |
cyber attack, combined deep learning; abnormal behaviors of cyber-attacks; detection attacks |
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URL |
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Title |
Optimization of Cyber-Attack Detection Using the Deep Learning Network |
|
Author |
Lai Van Duong |
| Citation |
Vol. 21 No. 7 pp. 159-168 |
|
Abstract |
Detecting cyber-attacks using machine learning or deep learning is being studied and applied widely in network intrusion detection systems. We noticed that the application of deep learning algorithms yielded many good results. However, because each deep learning model has different architecture and characteristics with certain advantages and disadvantages, so those deep learning models are only suitable for specific datasets or features. In this paper, in order to optimize the process of detecting cyber-attacks, we propose the idea of building a new deep learning network model based on the association and combination of individual deep learning models. In particular, based on the architecture of 2 deep learning models: Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), we combine them into a combined deep learning network for detecting cyber-attacks based on network traffic. The experimental results in Section IV.D have demonstrated that our proposal using the CNN-LSTM deep learning model for detecting cyber-attacks based on network traffic is completely correct because the results of this model are much better than some individual deep learning models on all measures. |
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Keywords |
cyber attack, combined deep learning; abnormal behaviors of cyber-attacks; detection attacks |
|
URL |
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Title |
Optimization of Cyber-Attack Detection Using the Deep Learning Network |
|
Author |
Lai Van Duong |
| Citation |
Vol. 21 No. 7 pp. 159-168 |
|
Abstract |
Detecting cyber-attacks using machine learning or deep learning is being studied and applied widely in network intrusion detection systems. We noticed that the application of deep learning algorithms yielded many good results. However, because each deep learning model has different architecture and characteristics with certain advantages and disadvantages, so those deep learning models are only suitable for specific datasets or features. In this paper, in order to optimize the process of detecting cyber-attacks, we propose the idea of building a new deep learning network model based on the association and combination of individual deep learning models. In particular, based on the architecture of 2 deep learning models: Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), we combine them into a combined deep learning network for detecting cyber-attacks based on network traffic. The experimental results in Section IV.D have demonstrated that our proposal using the CNN-LSTM deep learning model for detecting cyber-attacks based on network traffic is completely correct because the results of this model are much better than some individual deep learning models on all measures. |
|
Keywords |
cyber attack, combined deep learning; abnormal behaviors of cyber-attacks; detection attacks |
|
URL |
|
|
Title |
Optimization of Cyber-Attack Detection Using the Deep Learning Network |
|
Author |
Lai Van Duong |
| Citation |
Vol. 21 No. 7 pp. 159-168 |
|
Abstract |
Detecting cyber-attacks using machine learning or deep learning is being studied and applied widely in network intrusion detection systems. We noticed that the application of deep learning algorithms yielded many good results. However, because each deep learning model has different architecture and characteristics with certain advantages and disadvantages, so those deep learning models are only suitable for specific datasets or features. In this paper, in order to optimize the process of detecting cyber-attacks, we propose the idea of building a new deep learning network model based on the association and combination of individual deep learning models. In particular, based on the architecture of 2 deep learning models: Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), we combine them into a combined deep learning network for detecting cyber-attacks based on network traffic. The experimental results in Section IV.D have demonstrated that our proposal using the CNN-LSTM deep learning model for detecting cyber-attacks based on network traffic is completely correct because the results of this model are much better than some individual deep learning models on all measures. |
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Keywords |
cyber attack, combined deep learning; abnormal behaviors of cyber-attacks; detection attacks |
|
URL |
|
|
Title |
Optimization of Cyber-Attack Detection Using the Deep Learning Network |
|
Author |
Lai Van Duong |
| Citation |
Vol. 21 No. 7 pp. 159-168 |
|
Abstract |
Detecting cyber-attacks using machine learning or deep learning is being studied and applied widely in network intrusion detection systems. We noticed that the application of deep learning algorithms yielded many good results. However, because each deep learning model has different architecture and characteristics with certain advantages and disadvantages, so those deep learning models are only suitable for specific datasets or features. In this paper, in order to optimize the process of detecting cyber-attacks, we propose the idea of building a new deep learning network model based on the association and combination of individual deep learning models. In particular, based on the architecture of 2 deep learning models: Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), we combine them into a combined deep learning network for detecting cyber-attacks based on network traffic. The experimental results in Section IV.D have demonstrated that our proposal using the CNN-LSTM deep learning model for detecting cyber-attacks based on network traffic is completely correct because the results of this model are much better than some individual deep learning models on all measures. |
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Keywords |
cyber attack, combined deep learning; abnormal behaviors of cyber-attacks; detection attacks |
|
URL |
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|
Title |
Optimization of Cyber-Attack Detection Using the Deep Learning Network |
|
Author |
Lai Van Duong |
| Citation |
Vol. 21 No. 7 pp. 159-168 |
|
Abstract |
Detecting cyber-attacks using machine learning or deep learning is being studied and applied widely in network intrusion detection systems. We noticed that the application of deep learning algorithms yielded many good results. However, because each deep learning model has different architecture and characteristics with certain advantages and disadvantages, so those deep learning models are only suitable for specific datasets or features. In this paper, in order to optimize the process of detecting cyber-attacks, we propose the idea of building a new deep learning network model based on the association and combination of individual deep learning models. In particular, based on the architecture of 2 deep learning models: Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), we combine them into a combined deep learning network for detecting cyber-attacks based on network traffic. The experimental results in Section IV.D have demonstrated that our proposal using the CNN-LSTM deep learning model for detecting cyber-attacks based on network traffic is completely correct because the results of this model are much better than some individual deep learning models on all measures. |
|
Keywords |
cyber attack, combined deep learning; abnormal behaviors of cyber-attacks; detection attacks |
|
URL |
|
|
Title |
Optimization of Cyber-Attack Detection Using the Deep Learning Network |
|
Author |
Lai Van Duong |
| Citation |
Vol. 21 No. 7 pp. 159-168 |
|
Abstract |
Detecting cyber-attacks using machine learning or deep learning is being studied and applied widely in network intrusion detection systems. We noticed that the application of deep learning algorithms yielded many good results. However, because each deep learning model has different architecture and characteristics with certain advantages and disadvantages, so those deep learning models are only suitable for specific datasets or features. In this paper, in order to optimize the process of detecting cyber-attacks, we propose the idea of building a new deep learning network model based on the association and combination of individual deep learning models. In particular, based on the architecture of 2 deep learning models: Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), we combine them into a combined deep learning network for detecting cyber-attacks based on network traffic. The experimental results in Section IV.D have demonstrated that our proposal using the CNN-LSTM deep learning model for detecting cyber-attacks based on network traffic is completely correct because the results of this model are much better than some individual deep learning models on all measures. |
|
Keywords |
cyber attack, combined deep learning; abnormal behaviors of cyber-attacks; detection attacks |
|
URL |
|
|
Title |
Optimization of Cyber-Attack Detection Using the Deep Learning Network |
|
Author |
Lai Van Duong |
| Citation |
Vol. 21 No. 7 pp. 159-168 |
|
Abstract |
Detecting cyber-attacks using machine learning or deep learning is being studied and applied widely in network intrusion detection systems. We noticed that the application of deep learning algorithms yielded many good results. However, because each deep learning model has different architecture and characteristics with certain advantages and disadvantages, so those deep learning models are only suitable for specific datasets or features. In this paper, in order to optimize the process of detecting cyber-attacks, we propose the idea of building a new deep learning network model based on the association and combination of individual deep learning models. In particular, based on the architecture of 2 deep learning models: Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), we combine them into a combined deep learning network for detecting cyber-attacks based on network traffic. The experimental results in Section IV.D have demonstrated that our proposal using the CNN-LSTM deep learning model for detecting cyber-attacks based on network traffic is completely correct because the results of this model are much better than some individual deep learning models on all measures. |
|
Keywords |
cyber attack, combined deep learning; abnormal behaviors of cyber-attacks; detection attacks |
|
URL |
|
|
Title |
Optimization of Cyber-Attack Detection Using the Deep Learning Network |
|
Author |
Lai Van Duong |
| Citation |
Vol. 21 No. 7 pp. 159-168 |
|
Abstract |
Detecting cyber-attacks using machine learning or deep learning is being studied and applied widely in network intrusion detection systems. We noticed that the application of deep learning algorithms yielded many good results. However, because each deep learning model has different architecture and characteristics with certain advantages and disadvantages, so those deep learning models are only suitable for specific datasets or features. In this paper, in order to optimize the process of detecting cyber-attacks, we propose the idea of building a new deep learning network model based on the association and combination of individual deep learning models. In particular, based on the architecture of 2 deep learning models: Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), we combine them into a combined deep learning network for detecting cyber-attacks based on network traffic. The experimental results in Section IV.D have demonstrated that our proposal using the CNN-LSTM deep learning model for detecting cyber-attacks based on network traffic is completely correct because the results of this model are much better than some individual deep learning models on all measures. |
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Keywords |
cyber attack, combined deep learning; abnormal behaviors of cyber-attacks; detection attacks |
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URL |
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Title |
Latest Transformations of XP Process Model: A Systematic Literature Review |
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Author |
Sadia Khan, Muhammad Abuzar Fahiem, Birra Bakhtawar, Shabib Aftab, Munir Ahmad, Nauman Aziz, Abdullah Almotilag, Nouh Sabri Elmitwally |
| Citation |
Vol. 21 No. 7 pp. 143-150 |
|
Abstract |
Process model is an integral part of software industry. Different process models are used now a days in the industry for different software projects. Process models need to be tailored to address some specific project needs. Agile models are considered as the most widely used process models nowadays. They have distinctive features and the ability to address the dynamic needs of today’s software development. Extreme programming (XP) is one of the extensively used agile process model especially for small projects. Many researchers have tried to mold XP to overcome its shortcomings and for better working in specific scenarios. Therefore, many customized versions of XP process model are available today. In this paper, we are going to analyze the latest customizations of XP. For this purpose, a systematic literature review is conducted on studies published from 2012 till 2018 in renowned online search libraries. This comprehensive review highlights the purpose of customizations, along with the areas in which customizations are made, and phases & practices which are being customized. This work will serve the researchers to discover the modern versions of XP process model as well as will provide a baseline for future directions for customizations. |
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Keywords |
Agile, Extreme Programming, XP, Modified XP, Customized XP, Systematic Literature Review, SLR. |
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URL |
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Title |
Latest Transformations of XP Process Model: A Systematic Literature Review |
|
Author |
Sadia Khan, Muhammad Abuzar Fahiem, Birra Bakhtawar, Shabib Aftab, Munir Ahmad, Nauman Aziz, Abdullah Almotilag, Nouh Sabri Elmitwally |
| Citation |
Vol. 21 No. 7 pp. 143-150 |
|
Abstract |
Process model is an integral part of software industry. Different process models are used now a days in the industry for different software projects. Process models need to be tailored to address some specific project needs. Agile models are considered as the most widely used process models nowadays. They have distinctive features and the ability to address the dynamic needs of today’s software development. Extreme programming (XP) is one of the extensively used agile process model especially for small projects. Many researchers have tried to mold XP to overcome its shortcomings and for better working in specific scenarios. Therefore, many customized versions of XP process model are available today. In this paper, we are going to analyze the latest customizations of XP. For this purpose, a systematic literature review is conducted on studies published from 2012 till 2018 in renowned online search libraries. This comprehensive review highlights the purpose of customizations, along with the areas in which customizations are made, and phases & practices which are being customized. This work will serve the researchers to discover the modern versions of XP process model as well as will provide a baseline for future directions for customizations. |
|
Keywords |
Agile, Extreme Programming, XP, Modified XP, Customized XP, Systematic Literature Review, SLR. |
|
URL |
|
|
Title |
Latest Transformations of XP Process Model: A Systematic Literature Review |
|
Author |
Sadia Khan, Muhammad Abuzar Fahiem, Birra Bakhtawar, Shabib Aftab, Munir Ahmad, Nauman Aziz, Abdullah Almotilag, Nouh Sabri Elmitwally |
| Citation |
Vol. 21 No. 7 pp. 143-150 |
|
Abstract |
Process model is an integral part of software industry. Different process models are used now a days in the industry for different software projects. Process models need to be tailored to address some specific project needs. Agile models are considered as the most widely used process models nowadays. They have distinctive features and the ability to address the dynamic needs of today’s software development. Extreme programming (XP) is one of the extensively used agile process model especially for small projects. Many researchers have tried to mold XP to overcome its shortcomings and for better working in specific scenarios. Therefore, many customized versions of XP process model are available today. In this paper, we are going to analyze the latest customizations of XP. For this purpose, a systematic literature review is conducted on studies published from 2012 till 2018 in renowned online search libraries. This comprehensive review highlights the purpose of customizations, along with the areas in which customizations are made, and phases & practices which are being customized. This work will serve the researchers to discover the modern versions of XP process model as well as will provide a baseline for future directions for customizations. |
|
Keywords |
Agile, Extreme Programming, XP, Modified XP, Customized XP, Systematic Literature Review, SLR. |
|
URL |
|
|
Title |
Optimization of Cyber-Attack Detection Using the Deep Learning Network |
|
Author |
Lai Van Duong |
| Citation |
Vol. 21 No. 7 pp. 159-168 |
|
Abstract |
Detecting cyber-attacks using machine learning or deep learning is being studied and applied widely in network intrusion detection systems. We noticed that the application of deep learning algorithms yielded many good results. However, because each deep learning model has different architecture and characteristics with certain advantages and disadvantages, so those deep learning models are only suitable for specific datasets or features. In this paper, in order to optimize the process of detecting cyber-attacks, we propose the idea of building a new deep learning network model based on the association and combination of individual deep learning models. In particular, based on the architecture of 2 deep learning models: Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), we combine them into a combined deep learning network for detecting cyber-attacks based on network traffic. The experimental results in Section IV.D have demonstrated that our proposal using the CNN-LSTM deep learning model for detecting cyber-attacks based on network traffic is completely correct because the results of this model are much better than some individual deep learning models on all measures. |
|
Keywords |
cyber attack, combined deep learning; abnormal behaviors of cyber-attacks; detection attacks |
|
URL |
|
|
Title |
Optimization of Cyber-Attack Detection Using the Deep Learning Network |
|
Author |
Lai Van Duong |
| Citation |
Vol. 21 No. 7 pp. 159-168 |
|
Abstract |
Detecting cyber-attacks using machine learning or deep learning is being studied and applied widely in network intrusion detection systems. We noticed that the application of deep learning algorithms yielded many good results. However, because each deep learning model has different architecture and characteristics with certain advantages and disadvantages, so those deep learning models are only suitable for specific datasets or features. In this paper, in order to optimize the process of detecting cyber-attacks, we propose the idea of building a new deep learning network model based on the association and combination of individual deep learning models. In particular, based on the architecture of 2 deep learning models: Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), we combine them into a combined deep learning network for detecting cyber-attacks based on network traffic. The experimental results in Section IV.D have demonstrated that our proposal using the CNN-LSTM deep learning model for detecting cyber-attacks based on network traffic is completely correct because the results of this model are much better than some individual deep learning models on all measures. |
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Keywords |
cyber attack, combined deep learning; abnormal behaviors of cyber-attacks; detection attacks |
|
URL |
|
|
Title |
Optimization of Cyber-Attack Detection Using the Deep Learning Network |
|
Author |
Lai Van Duong |
| Citation |
Vol. 21 No. 7 pp. 159-168 |
|
Abstract |
Detecting cyber-attacks using machine learning or deep learning is being studied and applied widely in network intrusion detection systems. We noticed that the application of deep learning algorithms yielded many good results. However, because each deep learning model has different architecture and characteristics with certain advantages and disadvantages, so those deep learning models are only suitable for specific datasets or features. In this paper, in order to optimize the process of detecting cyber-attacks, we propose the idea of building a new deep learning network model based on the association and combination of individual deep learning models. In particular, based on the architecture of 2 deep learning models: Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), we combine them into a combined deep learning network for detecting cyber-attacks based on network traffic. The experimental results in Section IV.D have demonstrated that our proposal using the CNN-LSTM deep learning model for detecting cyber-attacks based on network traffic is completely correct because the results of this model are much better than some individual deep learning models on all measures. |
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Keywords |
cyber attack, combined deep learning; abnormal behaviors of cyber-attacks; detection attacks |
|
URL |
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Title |
Optimization of Cyber-Attack Detection Using the Deep Learning Network |
|
Author |
Lai Van Duong |
| Citation |
Vol. 21 No. 7 pp. 159-168 |
|
Abstract |
Detecting cyber-attacks using machine learning or deep learning is being studied and applied widely in network intrusion detection systems. We noticed that the application of deep learning algorithms yielded many good results. However, because each deep learning model has different architecture and characteristics with certain advantages and disadvantages, so those deep learning models are only suitable for specific datasets or features. In this paper, in order to optimize the process of detecting cyber-attacks, we propose the idea of building a new deep learning network model based on the association and combination of individual deep learning models. In particular, based on the architecture of 2 deep learning models: Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), we combine them into a combined deep learning network for detecting cyber-attacks based on network traffic. The experimental results in Section IV.D have demonstrated that our proposal using the CNN-LSTM deep learning model for detecting cyber-attacks based on network traffic is completely correct because the results of this model are much better than some individual deep learning models on all measures. |
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Keywords |
cyber attack, combined deep learning; abnormal behaviors of cyber-attacks; detection attacks |
|
URL |
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Title |
Optimization of Cyber-Attack Detection Using the Deep Learning Network |
|
Author |
Lai Van Duong |
| Citation |
Vol. 21 No. 7 pp. 159-168 |
|
Abstract |
Detecting cyber-attacks using machine learning or deep learning is being studied and applied widely in network intrusion detection systems. We noticed that the application of deep learning algorithms yielded many good results. However, because each deep learning model has different architecture and characteristics with certain advantages and disadvantages, so those deep learning models are only suitable for specific datasets or features. In this paper, in order to optimize the process of detecting cyber-attacks, we propose the idea of building a new deep learning network model based on the association and combination of individual deep learning models. In particular, based on the architecture of 2 deep learning models: Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), we combine them into a combined deep learning network for detecting cyber-attacks based on network traffic. The experimental results in Section IV.D have demonstrated that our proposal using the CNN-LSTM deep learning model for detecting cyber-attacks based on network traffic is completely correct because the results of this model are much better than some individual deep learning models on all measures. |
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Keywords |
cyber attack, combined deep learning; abnormal behaviors of cyber-attacks; detection attacks |
|
URL |
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Title |
Optimization of Cyber-Attack Detection Using the Deep Learning Network |
|
Author |
Lai Van Duong |
| Citation |
Vol. 21 No. 7 pp. 159-168 |
|
Abstract |
Detecting cyber-attacks using machine learning or deep learning is being studied and applied widely in network intrusion detection systems. We noticed that the application of deep learning algorithms yielded many good results. However, because each deep learning model has different architecture and characteristics with certain advantages and disadvantages, so those deep learning models are only suitable for specific datasets or features. In this paper, in order to optimize the process of detecting cyber-attacks, we propose the idea of building a new deep learning network model based on the association and combination of individual deep learning models. In particular, based on the architecture of 2 deep learning models: Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), we combine them into a combined deep learning network for detecting cyber-attacks based on network traffic. The experimental results in Section IV.D have demonstrated that our proposal using the CNN-LSTM deep learning model for detecting cyber-attacks based on network traffic is completely correct because the results of this model are much better than some individual deep learning models on all measures. |
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Keywords |
cyber attack, combined deep learning; abnormal behaviors of cyber-attacks; detection attacks |
|
URL |
|
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Title |
Optimization of Cyber-Attack Detection Using the Deep Learning Network |
|
Author |
Lai Van Duong |
| Citation |
Vol. 21 No. 7 pp. 159-168 |
|
Abstract |
Detecting cyber-attacks using machine learning or deep learning is being studied and applied widely in network intrusion detection systems. We noticed that the application of deep learning algorithms yielded many good results. However, because each deep learning model has different architecture and characteristics with certain advantages and disadvantages, so those deep learning models are only suitable for specific datasets or features. In this paper, in order to optimize the process of detecting cyber-attacks, we propose the idea of building a new deep learning network model based on the association and combination of individual deep learning models. In particular, based on the architecture of 2 deep learning models: Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), we combine them into a combined deep learning network for detecting cyber-attacks based on network traffic. The experimental results in Section IV.D have demonstrated that our proposal using the CNN-LSTM deep learning model for detecting cyber-attacks based on network traffic is completely correct because the results of this model are much better than some individual deep learning models on all measures. |
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Keywords |
cyber attack, combined deep learning; abnormal behaviors of cyber-attacks; detection attacks |
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URL |
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Title |
Optimization of Cyber-Attack Detection Using the Deep Learning Network |
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Author |
Lai Van Duong |
| Citation |
Vol. 21 No. 7 pp. 159-168 |
|
Abstract |
Detecting cyber-attacks using machine learning or deep learning is being studied and applied widely in network intrusion detection systems. We noticed that the application of deep learning algorithms yielded many good results. However, because each deep learning model has different architecture and characteristics with certain advantages and disadvantages, so those deep learning models are only suitable for specific datasets or features. In this paper, in order to optimize the process of detecting cyber-attacks, we propose the idea of building a new deep learning network model based on the association and combination of individual deep learning models. In particular, based on the architecture of 2 deep learning models: Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), we combine them into a combined deep learning network for detecting cyber-attacks based on network traffic. The experimental results in Section IV.D have demonstrated that our proposal using the CNN-LSTM deep learning model for detecting cyber-attacks based on network traffic is completely correct because the results of this model are much better than some individual deep learning models on all measures. |
|
Keywords |
cyber attack, combined deep learning; abnormal behaviors of cyber-attacks; detection attacks |
|
URL |
|
|
Title |
Optimization of Cyber-Attack Detection Using the Deep Learning Network |
|
Author |
Lai Van Duong |
| Citation |
Vol. 21 No. 7 pp. 159-168 |
|
Abstract |
Detecting cyber-attacks using machine learning or deep learning is being studied and applied widely in network intrusion detection systems. We noticed that the application of deep learning algorithms yielded many good results. However, because each deep learning model has different architecture and characteristics with certain advantages and disadvantages, so those deep learning models are only suitable for specific datasets or features. In this paper, in order to optimize the process of detecting cyber-attacks, we propose the idea of building a new deep learning network model based on the association and combination of individual deep learning models. In particular, based on the architecture of 2 deep learning models: Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), we combine them into a combined deep learning network for detecting cyber-attacks based on network traffic. The experimental results in Section IV.D have demonstrated that our proposal using the CNN-LSTM deep learning model for detecting cyber-attacks based on network traffic is completely correct because the results of this model are much better than some individual deep learning models on all measures. |
|
Keywords |
cyber attack, combined deep learning; abnormal behaviors of cyber-attacks; detection attacks |
|
URL |
|
|
Title |
Optimization of Cyber-Attack Detection Using the Deep Learning Network |
|
Author |
Lai Van Duong |
| Citation |
Vol. 21 No. 7 pp. 159-168 |
|
Abstract |
Detecting cyber-attacks using machine learning or deep learning is being studied and applied widely in network intrusion detection systems. We noticed that the application of deep learning algorithms yielded many good results. However, because each deep learning model has different architecture and characteristics with certain advantages and disadvantages, so those deep learning models are only suitable for specific datasets or features. In this paper, in order to optimize the process of detecting cyber-attacks, we propose the idea of building a new deep learning network model based on the association and combination of individual deep learning models. In particular, based on the architecture of 2 deep learning models: Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), we combine them into a combined deep learning network for detecting cyber-attacks based on network traffic. The experimental results in Section IV.D have demonstrated that our proposal using the CNN-LSTM deep learning model for detecting cyber-attacks based on network traffic is completely correct because the results of this model are much better than some individual deep learning models on all measures. |
|
Keywords |
cyber attack, combined deep learning; abnormal behaviors of cyber-attacks; detection attacks |
|
URL |
|
|
Title |
Optimization of Cyber-Attack Detection Using the Deep Learning Network |
|
Author |
Lai Van Duong |
| Citation |
Vol. 21 No. 7 pp. 159-168 |
|
Abstract |
Detecting cyber-attacks using machine learning or deep learning is being studied and applied widely in network intrusion detection systems. We noticed that the application of deep learning algorithms yielded many good results. However, because each deep learning model has different architecture and characteristics with certain advantages and disadvantages, so those deep learning models are only suitable for specific datasets or features. In this paper, in order to optimize the process of detecting cyber-attacks, we propose the idea of building a new deep learning network model based on the association and combination of individual deep learning models. In particular, based on the architecture of 2 deep learning models: Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), we combine them into a combined deep learning network for detecting cyber-attacks based on network traffic. The experimental results in Section IV.D have demonstrated that our proposal using the CNN-LSTM deep learning model for detecting cyber-attacks based on network traffic is completely correct because the results of this model are much better than some individual deep learning models on all measures. |
|
Keywords |
cyber attack, combined deep learning; abnormal behaviors of cyber-attacks; detection attacks |
|
URL |
|
|
Title |
Optimization of Cyber-Attack Detection Using the Deep Learning Network |
|
Author |
Lai Van Duong |
| Citation |
Vol. 21 No. 7 pp. 159-168 |
|
Abstract |
Detecting cyber-attacks using machine learning or deep learning is being studied and applied widely in network intrusion detection systems. We noticed that the application of deep learning algorithms yielded many good results. However, because each deep learning model has different architecture and characteristics with certain advantages and disadvantages, so those deep learning models are only suitable for specific datasets or features. In this paper, in order to optimize the process of detecting cyber-attacks, we propose the idea of building a new deep learning network model based on the association and combination of individual deep learning models. In particular, based on the architecture of 2 deep learning models: Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), we combine them into a combined deep learning network for detecting cyber-attacks based on network traffic. The experimental results in Section IV.D have demonstrated that our proposal using the CNN-LSTM deep learning model for detecting cyber-attacks based on network traffic is completely correct because the results of this model are much better than some individual deep learning models on all measures. |
|
Keywords |
cyber attack, combined deep learning; abnormal behaviors of cyber-attacks; detection attacks |
|
URL |
|
|
Title |
Optimization of Cyber-Attack Detection Using the Deep Learning Network |
|
Author |
Lai Van Duong |
| Citation |
Vol. 21 No. 7 pp. 159-168 |
|
Abstract |
Detecting cyber-attacks using machine learning or deep learning is being studied and applied widely in network intrusion detection systems. We noticed that the application of deep learning algorithms yielded many good results. However, because each deep learning model has different architecture and characteristics with certain advantages and disadvantages, so those deep learning models are only suitable for specific datasets or features. In this paper, in order to optimize the process of detecting cyber-attacks, we propose the idea of building a new deep learning network model based on the association and combination of individual deep learning models. In particular, based on the architecture of 2 deep learning models: Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), we combine them into a combined deep learning network for detecting cyber-attacks based on network traffic. The experimental results in Section IV.D have demonstrated that our proposal using the CNN-LSTM deep learning model for detecting cyber-attacks based on network traffic is completely correct because the results of this model are much better than some individual deep learning models on all measures. |
|
Keywords |
cyber attack, combined deep learning; abnormal behaviors of cyber-attacks; detection attacks |
|
URL |
|
|
Title |
Optimization of Cyber-Attack Detection Using the Deep Learning Network |
|
Author |
Lai Van Duong |
| Citation |
Vol. 21 No. 7 pp. 159-168 |
|
Abstract |
Detecting cyber-attacks using machine learning or deep learning is being studied and applied widely in network intrusion detection systems. We noticed that the application of deep learning algorithms yielded many good results. However, because each deep learning model has different architecture and characteristics with certain advantages and disadvantages, so those deep learning models are only suitable for specific datasets or features. In this paper, in order to optimize the process of detecting cyber-attacks, we propose the idea of building a new deep learning network model based on the association and combination of individual deep learning models. In particular, based on the architecture of 2 deep learning models: Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), we combine them into a combined deep learning network for detecting cyber-attacks based on network traffic. The experimental results in Section IV.D have demonstrated that our proposal using the CNN-LSTM deep learning model for detecting cyber-attacks based on network traffic is completely correct because the results of this model are much better than some individual deep learning models on all measures. |
|
Keywords |
cyber attack, combined deep learning; abnormal behaviors of cyber-attacks; detection attacks |
|
URL |
