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

doi:10.1155/2021/6129210

	Deep Learning-Based Framework for the Detection of Cyberattack Using Feature Engineering
	Akhtar, Muhammad Shoaib; Feng, Tao
刊名	SECURITY AND COMMUNICATION NETWORKS
	2021-12-24
卷号	2021
ISSN号	1939-0114
DOI	10.1155/2021/6129210
英文摘要	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.
WOS研究方向	Computer Science ; Telecommunications
语种	英语
出版者	WILEY-HINDAWI
WOS记录号	WOS:000739221800003
内容类型	期刊论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/154930]
专题	计算机与通信学院
作者单位	Lanzhou Univ Technol, Sch Comp & Commun, Lanzhou 730050, Peoples R China
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).