当前位置: X-MOL 学术Wireless Pers. Commun. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Optimally Configured Deep Convolutional Neural Network for Attack Detection in Internet of Things: Impact of Algorithm of the Innovative Gunner
Wireless Personal Communications ( IF 1.9 ) Pub Date : 2021-01-03 , DOI: 10.1007/s11277-020-08011-9
Subramonian Krishna Sarma

Nowadays, the internet of things (IoT) has gained significant research attention. It is becoming critically imperative to protect IoT devices against cyberattacks with the phenomenal intensification. The malicious users or attackers might take control of the devices and serious things will be at stake apart from privacy violation. Therefore, it is important to identify and prevent novel attacks in the IoT context. This paper proposes a novel attack detection system by interlinking the development and operations framework. This proposed detection model includes two stages such as proposed feature extraction and classification. The preliminary phase is feature extraction, the data from every application are processed by integrating the statistical and higher-order statistical features together with the extant features. Based on these extracted features the classification process is evolved for this, an optimized deep convolutional neural network (DCNN) model is utilized. Besides, the count of filters and filter size in the convolution layer, as well as the activation function, are optimized using a new modified algorithm of the innovative gunner (MAIG), which is the enhanced version of the AIG algorithm. Finally, the proposed work is compared and proved over other traditional works concerning positive and negative measures as well. The experimental outcomes show that the proposed MAIG algorithm for application 1 under the GAF-GYT attack achieves higher accuracy of 64.52, 2.38 and 3.76% when compared over the methods like DCNN, AIG and FAE-GWO-DBN, respectively.



中文翻译:

优化配置的深度卷积神经网络,用于物联网攻击检测:创新炮手算法的影响

如今,物联网(IoT)已引起了广泛的研究关注。随着现象的加剧,保护物联网设备免受网络攻击变得至关重要。恶意用户或攻击者可能会控制设备,除了侵犯隐私之外,其他事情也将受到威胁。因此,在物联网环境中识别和防止新型攻击非常重要。通过将开发和运营框架相互联系,提出了一种新颖的攻击检测系统。该提议的检测模型包括两个阶段,例如提议的特征提取和分类。初步阶段是特征提取,通过将统计和高阶统计特征与现有特征集成在一起来处理来自每个应用程序的数据。基于这些提取的特征,为此发展了分类过程,并利用了优化的深度卷积神经网络(DCNN)模型。此外,使用创新的机枪手(MAIG)的新改进算法对卷积层中的滤波器数量和滤波器大小以及激活函数进行了优化,这是AIG算法的增强版本。最后,将拟议工作与涉及积极和消极措施的其他传统工作进行比较和证明。实验结果表明,与DCNN,AIG和FAE-GWO-DBN等方法相比,在GAF-GYT攻击下针对应用程序1提出的MAIG算法分别达到了64.52、2.38和3.76%的更高精度。利用了优化的深度卷积神经网络(DCNN)模型。此外,使用创新的机枪手(MAIG)的新改进算法对卷积层中的滤波器数量和滤波器大小以及激活函数进行了优化,这是AIG算法的增强版本。最后,将拟议工作与涉及积极和消极措施的其他传统工作进行比较和证明。实验结果表明,与DCNN,AIG和FAE-GWO-DBN等方法相比,在GAF-GYT攻击下针对应用程序1提出的MAIG算法分别达到了64.52、2.38和3.76%的更高精度。利用了优化的深度卷积神经网络(DCNN)模型。此外,使用创新的机枪手(MAIG)的新改进算法对卷积层中的滤波器数量和滤波器大小以及激活函数进行了优化,这是AIG算法的增强版本。最后,将拟议工作与涉及积极和消极措施的其他传统工作进行比较和证明。实验结果表明,与DCNN,AIG和FAE-GWO-DBN等方法相比,在GAF-GYT攻击下针对应用程序1提出的MAIG算法分别达到了64.52、2.38和3.76%的更高精度。使用创新型机枪手(MAIG)的新改良算法优化AIG,这是AIG算法的增强版本。最后,将拟议工作与涉及积极和消极措施的其他传统工作进行比较和证明。实验结果表明,与DCNN,AIG和FAE-GWO-DBN等方法相比,在GAF-GYT攻击下针对应用程序1提出的MAIG算法分别达到了64.52、2.38和3.76%的更高精度。使用创新型机枪手(MAIG)的新改良算法优化AIG,这是AIG算法的增强版本。最后,将拟议工作与涉及积极和消极措施的其他传统工作进行比较和证明。实验结果表明,与DCNN,AIG和FAE-GWO-DBN等方法相比,在GAF-GYT攻击下针对应用程序1提出的MAIG算法分别达到了64.52、2.38和3.76%的更高精度。

更新日期:2021-01-03
down
wechat
bug