连接的设备或物联网(IoT)设备的数量已经迅速增加。根据最新的统计数据,到2023年,大约有172亿个连接的物联网设备;预计到2030年将达到254亿个IoT设备,并在可预见的未来逐年增长。IoT设备共享,收集,通过互联网交换数据,无线网络,或其他网络。物联网互连技术改善和便利了人们的生活,但是,同时,对他们的安全构成了真正的威胁。拒绝服务(DoS)和分布式拒绝服务(DDoS)攻击被认为是最常见的威胁物联网设备安全的攻击。这些被认为是一种增加的趋势,降低风险将是一个重大挑战,尤其是在未来。在这种情况下,本文提出了一种改进的框架(SDN-ML-IoT),该框架可用作入侵和防御检测系统(IDPS),可以帮助更高效地检测DDoS攻击并实时减轻它们。该SDN-ML-IoT在软件定义网络(SDN)环境中使用机器学习(ML)方法,以保护智能家居IoT设备免受DDoS攻击。我们采用了一种基于随机森林(RF)的ML方法,逻辑回归(LR),k-最近邻居(kNN),和朴素贝叶斯(NB)与一个对休息(OvR)策略,然后将我们的工作与其他相关工作进行比较。根据性能指标,如混淆矩阵,培训时间,预测时间,准确度,和接收器工作特性曲线下面积(AUC-ROC),已经确定SDN-ML-IoT,当应用于RF时,优于其他ML算法,以及与我们工作相关的类似方法。它有一个令人印象深刻的99.99%的准确率,它可以在不到3s的时间内缓解DDoS攻击。我们对相关工作中使用的各种模型和算法进行了比较分析。结果表明,我们提出的方法优于其他方法,展示其在检测和减轻SDN内DDoS攻击方面的有效性。基于这些有希望的结果,我们选择在SDN中部署SDN-ML-IoT。此实施可确保智能家居中的物联网设备免受网络流量中的DDoS攻击。
The number of connected devices or Internet of Things (IoT) devices has rapidly increased. According to the latest available statistics, in 2023, there were approximately 17.2 billion connected IoT devices; this is expected to reach 25.4 billion IoT devices by 2030 and grow year over year for the foreseeable future. IoT devices share, collect, and exchange data via the internet, wireless networks, or other networks with one another. IoT interconnection technology improves and facilitates people\'s lives but, at the same time, poses a real threat to their security. Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) attacks are considered the most common and threatening attacks that strike IoT devices\' security. These are considered to be an increasing trend, and it will be a major challenge to reduce risk, especially in the future. In this context, this paper presents an improved framework (SDN-ML-IoT) that works as an Intrusion and Prevention Detection System (IDPS) that could help to detect DDoS attacks with more efficiency and mitigate them in real time. This SDN-ML-IoT uses a Machine Learning (ML) method in a Software-Defined Networking (SDN) environment in order to protect smart home IoT devices from DDoS attacks. We employed an ML method based on Random Forest (RF), Logistic Regression (LR), k-Nearest Neighbors (kNN), and Naive Bayes (NB) with a One-versus-Rest (OvR) strategy and then compared our work to other related works. Based on the performance metrics, such as confusion matrix, training time, prediction time, accuracy, and Area Under the Receiver Operating Characteristic curve (AUC-ROC), it was established that SDN-ML-IoT, when applied to RF, outperforms other ML algorithms, as well as similar approaches related to our work. It had an impressive accuracy of 99.99%, and it could mitigate DDoS attacks in less than 3 s. We conducted a comparative analysis of various models and algorithms used in the related works. The results indicated that our proposed approach outperforms others, showcasing its effectiveness in both detecting and mitigating DDoS attacks within SDNs. Based on these promising results, we have opted to deploy SDN-ML-IoT within the SDN. This implementation ensures the safeguarding of IoT devices in smart homes against DDoS attacks within the network traffic.