Now, the Cyber security is facing unprecedented difficulties as a result of the proliferation of smart devices in the Internet of Things (IoT) environment. The rapid growth in the number of Internet users over the past two decades has increased the need for cyber security. Users have provided new opportunities for attackers to do harm. Limited security budgets leave IoT devices vulnerable and easily hacked to launch distributed denial-of-service (DDoS) attacks, with disastrous results. Unfortunately, due to the unique nature of the Internet of Things environment, most security solutions and intrusion detection systems (IDS) cannot be directly adapted to the IoT with acceptable security performance and are vulnerable to various attacks that do not benefit. In this paper we propose an optimal secure defense mechanism for DDoS in IoT network using feature optimization and intrusion detection system (OSD-IDS). In OSD-IDS mechanism, first we introduce an enhanced ResNet architecture for feature extraction which extracts more deep features from given traffic traces. An improved quantum query optimization (IQQO) algorithm for is used feature selection to selects optimal best among multiple features which reduces the data dimensionality issues. The selected features have given to the detection and classification module to classify the traffic traces are affected by intrusion or not. For this, we design a fast and accurate intrusion detection mechanism, named as hybrid deep learning technique which combines convolutional neural network (CNN) and diagonal XG boosting (CNN-DigXG) for the fast and accurate intrusion detection in IoT network. Finally, we validate the performance of proposed technique by using different benchmark datasets are BoNeSi-SlowHTTPtest and CIC-DDoS2019. The simulation results of proposed IDS mechanism are compared with the existing state-of-art IDS mechanism and analyze the performance with respects to different statistical measures. The results show that the DDoS detection accuracy of proposed OSD-IDS mechanism is high as 99.476% and 99.078% for BoNeSi-SlowHTTPtest, CICDDoS2019, respectively.

中文翻译：

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基于特征优化和入侵检测系统的物联网网络中DDoS攻击的最佳安全防御机制

现在，由于物联网（IoT）环境中智能设备的激增，网络安全面临着前所未有的困难。过去二十年互联网用户数量的快速增长增加了对网络安全的需求。用户为攻击者提供了新的伤害机会。有限的安全预算使物联网设备容易受到攻击，很容易被黑客发起分布式拒绝服务 (DDoS) 攻击，从而造成灾难性的后果。不幸的是，由于物联网环境的独特性，大多数安全解决方案和入侵检测系统（IDS）无法以可接受的安全性能直接适应物联网，并且容易受到各种不利于其的攻击。在本文中，我们利用特征优化和入侵检测系统（OSD-IDS）提出了一种针对物联网网络中 DDoS 的最佳安全防御机制。在 OSD-IDS 机制中，首先我们引入了一种用于特征提取的增强型 ResNet 架构，该架构可以从给定的流量轨迹中提取更深层的特征。改进的量子查询优化（IQQO）算法使用特征选择来在多个特征中选择最优的，从而减少数据维度问题。所选择的特征已提供给检测和分类模块以对流量痕迹是否受到入侵影响进行分类。为此，我们设计了一种快速准确的入侵检测机制，称为混合深度学习技术，该技术结合了卷积神经网络（CNN）和对角 XG 提升（CNN-DigXG），用于物联网网络中快速准确的入侵检测。最后，我们使用不同的基准数据集 BoNeSi-SlowHTTPtest 和 CIC-DDoS2019 验证了所提出技术的性能。将所提出的 IDS 机制的仿真结果与现有最先进的 IDS 机制进行比较，并分析不同统计指标的性能。结果表明，所提出的 OSD-IDS 机制对于 BoNeSi-SlowHTTPtest、CICDDoS2019 的 DDoS 检测准确率分别高达 99.476% 和 99.078%。