Masthan M, Pazhanikumar K, Chavan Meena, Mandala Jyothi, Prasad Kumar Sanjay Nakharu
Technical Project Manager, Arffy Technologies, Karnataka, India.
Dept of Computer Science, S.T.Hindu College, Nagercoil, India.
Network. 2023 Feb-Nov;34(4):343-373. doi: 10.1080/0954898X.2023.2261531. Epub 2023 Nov 9.
Security and privacy are regarded as the greatest priority in any real-world smart ecosystem built on the Internet of Things (IoT) paradigm. In this study, a SqueezeNet model for IoT threat detection is built using Sine Cosine Sea Lion Optimization (SCSLnO). The Base Station (BS) carries out intrusion detection. The Hausdorff distance is used to determine which features are important. Using the SqueezeNet model, attack detection is carried out, and the network classifier is trained using SCSLnO, which is developed by combining the Sine Cosine Algorithm (SCA) with Sea Lion Optimization (SLnO). BoT-IoT and NSL-KDD datasets are used for the analysis. In comparison to existing approaches, PSO-KNN/SVM, Voting Ensemble Classifier, Deep NN, and Deep learning, the accuracy value produced by devised method for the BoT-IoT dataset is 10.75%, 8.45%, 6.36%, and 3.51% higher when the training percentage is 90.
在基于物联网（IoT）范式构建的任何现实世界智能生态系统中，安全和隐私都被视为最优先事项。在本研究中，使用正弦余弦海狮优化算法（SCSLnO）构建了一个用于物联网威胁检测的SqueezeNet模型。基站（BS）进行入侵检测。豪斯多夫距离用于确定哪些特征是重要的。使用SqueezeNet模型进行攻击检测，并使用通过将正弦余弦算法（SCA）与海狮优化算法（SLnO）相结合而开发的SCSLnO来训练网络分类器。使用BoT-IoT和NSL-KDD数据集进行分析。与现有方法PSO-KNN/SVM、投票集成分类器、深度神经网络和深度学习相比，当训练百分比为90时，所设计方法对BoT-IoT数据集产生的准确率值分别高出10.75%、8.45%、6.36%和3.51%。
