中圖分類號： TP393
文獻標識碼： A
DOI： 10.19358/j.issn.2096-5133.2020.10.009
引用格式： 張金霜，梁樹杰，左敬龍. 基于GWO-SVM算法的物聯(lián)網(wǎng)入侵檢測研究[J].信息技術與網(wǎng)絡安全，2020，39(10)：44-48.

Research on Internet of Things intrusion detection by optimizing SVM using Grey Wolf Optimization algorithm

Abstract： The era of the Internet of Things is coming quietly. With the development of the Internet of Things technology, which brings convenience to people, security issues become increasingly prominent. To solve the problem of network intrusion security in Internet of Things, GWO-SVM algorithm was proposed to realize network intrusion detection. Grey Wolf Optimization algorithm(GWO) has the advantages of fast convergence speed and strong global search ability. Using GWO to optimize the parameter selection of Support Vector Machine(SVM) is helpful to improve the accuracy of classification model. Furthermore, by adjusting the fitness value function, overfitting of the classification model is avoided.In order to verify the effectiveness of the GWO-SVM algorithm, the experiment employs UNSW-NB15 data sets and compares with other parameter optimization methods such as SVM, PSO-SVM, GA-SVM. The experimental results show that GWO-SVM algorithm has higher classification accuracy and performance, which is suitable for network intrusion detection in the Internet of Things.

Key words : network intrusion detection；Grey Wolf Optimization(GWO)；Support Vector Machine(SVM)；Internet of Things security