Deep Learning-Based Framework for the Detection of Cyberattack Using Feature Engineering

doi:10.1155/2021/6129210

IR > 计算机与通信学院

	Deep Learning-Based Framework for the Detection of Cyberattack Using Feature Engineering
	Akhtar, Muhammad Shoaib; Feng, Tao
	2021-12-24
发表期刊	SECURITY AND COMMUNICATION NETWORKS
ISSN	1939-0114
卷号	2021
摘要	Digital systems are changing to security systems in contemporary days. It is time for the digital system to have sufficient security to defend against threats and attacks. The intrusion detection system can identify an anomaly from an external or internal source in the network system. Many kinds of threats are present, that is, active and passive. These dangers could lead to anomalies in the system by which data can be attacked and taken by attackers from the beginning to the destination. Machine learning nowadays is a developing topic; its applications are wide. We can forecast the future through machine learning and classify the right class. In this paper, we employed the new binary and multiclass classification model of Convolutional Neural Networks (CNNs) to identify the anomaly of the network system. In this respect, we used the NSLKDD dataset. Our model uses a Convolutional Neural Network (CNN) to conduct binary and multiclass classification. In both datasets, we build a DL-based DoS detection model. We focus on the DoS category in the most extensively used IDS dataset, KDD. As the name implies, CNN is the most extensively used the DL model for image recognition. Adding a pooling layer to the convolution layer minimizes the size of the feature data extracted from the image while maintaining I/O and spatial information. The CNN model has shown the promising results of multiclass and binary classification in terms of validation loss of 0.0012 at 11th epochs and validation accuracy of 98% and 99%, respectively.
DOI	10.1155/2021/6129210
收录类别	SCIE
语种	英语
WOS研究方向	Computer Science ; Telecommunications
WOS类目	Computer Science, Information Systems ; Telecommunications
WOS记录号	WOS:000739221800003
出版者	WILEY-HINDAWI
来源库	WOS
引用统计	被引频次：8[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	https://ir.lut.edu.cn/handle/2XXMBERH/154930
专题	计算机与通信学院
通讯作者	Feng, Tao
作者单位	Lanzhou Univ Technol, Sch Comp & Commun, Lanzhou 730050, Peoples R China
第一作者单位	兰州理工大学
通讯作者单位	兰州理工大学
第一作者的第一单位	兰州理工大学
推荐引用方式 GB/T 7714	Akhtar, Muhammad Shoaib,Feng, Tao. Deep Learning-Based Framework for the Detection of Cyberattack Using Feature Engineering[J]. SECURITY AND COMMUNICATION NETWORKS,2021,2021.
APA	Akhtar, Muhammad Shoaib,&Feng, Tao.(2021).Deep Learning-Based Framework for the Detection of Cyberattack Using Feature Engineering.SECURITY AND COMMUNICATION NETWORKS,2021.
MLA	Akhtar, Muhammad Shoaib,et al."Deep Learning-Based Framework for the Detection of Cyberattack Using Feature Engineering".SECURITY AND COMMUNICATION NETWORKS 2021(2021).

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