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.

摘要

物联网（IoT）是互联网的一次新革命。但是，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使开发和运营（DevOps）相互联系，因为它建立了开发和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，其中统计特征和高阶统计特征被连接起来。随后，对提取的特征进行分类过程，确定攻击的存在。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。提出了一种新的混合算法，称为萤火虫阿尔法评估灰狼优化（FAE-GWO）算法，它是萤火虫（FF）和灰狼优化（GWO）的结合。最后，在某些性能指标方面，将所提出的系统模型的性能与其他常规工作进行了比较。