面向物联网攻击检测的混合优化深度学习-深度信念网络

IF 1.7 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Journal of Experimental & Theoretical Artificial Intelligence Pub Date : 2021-06-02 DOI:10.1080/0952813X.2021.1924868
Subramonian Krishna Sarma
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引用次数: 1

摘要

物联网(IoT)是互联网的一场新革命。然而,物理设备和对象的物联网网络通常容易受到拒绝服务(DoS)和分布式拒绝服务(DDoS)等攻击。所提出的攻击检测系统将开发与运营(DevOps)联系起来,因为它建立了开发与it运营之间的关系。为此,提议的系统包括(i)提议的特征提取和(ii)分类。来自每个应用程序的数据在特征提取的初始阶段进行处理,其中将统计和高阶统计特征连接在一起。随后,将提取的特征进行分类处理,以确定是否存在攻击。对于分类过程,本文打算部署优化的深度信念网络(DBN),其中激活函数进行了优化调整。将萤火虫(FF)算法与灰狼优化算法(GWO)相结合,提出了一种新的混合算法——萤火虫阿尔法评估灰狼优化算法(FAE-GWO)。最后,根据某些性能指标,将所提出的系统模型的性能与其他常规工作进行比较。
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Hybrid optimised deep learning-deep belief network for attack detection in the internet of things
ABSTRACT Internet of Things (IoT) is a new revolution of the Internet. However, the IoT network of physical devices and objects is often vulnerable to attacks like Denial of Service (DoS) and Distributed Denial of Service (DDoS). The proposed attack detection system makes the interlinking of Development and Operations (DevOps) as it makes the relationship between development and IT operations. For this, the proposed system includes (i) Proposed Feature Extraction and (ii) Classification. The data from each application are processed under the initial stage of feature extraction, where the statistical and higher-order statistical features are concatenated. Subsequently, the extracted features are subjected to a classification process, where it determines the presence of attacks. For the classification process, this paper intends to deploy the optimised Deep Belief Network (DBN), in which the activation function is optimally tuned. A new hybrid algorithm termed Firefly Alpha Evaluated Grey Wolf Optimisation (FAE-GWO) algorithm is proposed, which is the combination of Firefly (FF) and Grey Wolf Optimisation (GWO). Finally, the performance of the proposed system model is compared over other conventional works in terms of certain performance measures.
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来源期刊
CiteScore
6.10
自引率
4.50%
发文量
89
审稿时长
>12 weeks
期刊介绍: Journal of Experimental & Theoretical Artificial Intelligence (JETAI) is a world leading journal dedicated to publishing high quality, rigorously reviewed, original papers in artificial intelligence (AI) research. The journal features work in all subfields of AI research and accepts both theoretical and applied research. Topics covered include, but are not limited to, the following: • cognitive science • games • learning • knowledge representation • memory and neural system modelling • perception • problem-solving
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