SCSLnO SqueezeNet:See Cosine Sea Lion Optimization使SqueeziNet能够在物联网中进行入侵检测。

IF 1.1 3区计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Network-Computation in Neural Systems Pub Date : 2023-02-01 Epub Date: 2023-11-09 DOI:10.1080/0954898X.2023.2261531

M Masthan, K Pazhanikumar, Meena Chavan, Jyothi Mandala, Sanjay Nakharu Prasad Kumar

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引用次数: 0

摘要

在任何基于物联网（IoT）范式的现实世界智能生态系统中，安全和隐私都被视为最优先考虑的问题。在本研究中，使用正弦-余弦海狮优化（SCSLnO）建立了物联网威胁检测的SqueezeNet模型。基站（BS）执行入侵检测。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，并使用将正弦余弦算法（SCA）与海狮优化算法（SLnO）相结合开发的SCSLnO训练网络分类器。BoT-IoT和NSL-KDD数据集用于分析。与现有方法PSO-KNN/SVM、Voting Ensemble Classifier、Deep NN和深度学习相比，当训练百分比为90时，所设计的方法对BoT IoT数据集的准确率分别高出10.75%、8.45%、6.36%和3.51%。

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SCSLnO-SqueezeNet: Sine Cosine-Sea Lion Optimization enabled SqueezeNet for intrusion detection in IoT.

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.

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来源期刊

Network-Computation in Neural Systems 工程技术-工程：电子与电气

CiteScore

3.70

自引率

1.30%

发文量

审稿时长

>12 weeks

期刊介绍： Network: Computation in Neural Systems welcomes submissions of research papers that integrate theoretical neuroscience with experimental data, emphasizing the utilization of cutting-edge technologies. We invite authors and researchers to contribute their work in the following areas: Theoretical Neuroscience: This section encompasses neural network modeling approaches that elucidate brain function. Neural Networks in Data Analysis and Pattern Recognition: We encourage submissions exploring the use of neural networks for data analysis and pattern recognition, including but not limited to image analysis and speech processing applications. Neural Networks in Control Systems: This category encompasses the utilization of neural networks in control systems, including robotics, state estimation, fault detection, and diagnosis. Analysis of Neurophysiological Data: We invite submissions focusing on the analysis of neurophysiology data obtained from experimental studies involving animals. Analysis of Experimental Data on the Human Brain: This section includes papers analyzing experimental data from studies on the human brain, utilizing imaging techniques such as MRI, fMRI, EEG, and PET. Neurobiological Foundations of Consciousness: We encourage submissions exploring the neural bases of consciousness in the brain and its simulation in machines.