支持向量机算法在船舶网络入侵检测中的应用

Application of support vector machine in ship network intrusion detection

入侵行为严重威胁船舶网络安全,对其入侵检测进行研究具有重要的意义,针对当前船舶网络入侵检测存在精度低、错误率高等不足,设计了一种支持向量机算法的船舶网络入侵检测模型。首先分析船舶网络入侵原理,并且提取船舶网络入侵检测特征,然后采用支持向量机算法根据入侵检测特征建立船舶网络入侵检测分类器,并引入和声搜索算法对船舶网络入侵检测分类器的参数进行优化,最后以某一个船舶网络入侵检测数据为例进行了验证性测试。支持向量机算法克服了当前船舶网络入侵检测模型的局限性,入侵检测精度超过90%,减少了入侵检测错误,检测效果要优于当前其他船舶网络入侵检测模型,是一种有效的船舶网络入侵检测模型。

The intrusion behavior is serious in ship network security,so it is of great significance to study its detection.In view of the low accuracy and high error rate of current ship network intrusion detection,a ship network intrusion detection model based on support vector machine algorithm is designed.Firstly,the principle of ship network intrusion is analyzed,and the intrusion detection features of ship network are extracted.Secondly,a classifier of ship network intrusion detection is established based on the intrusion detection features using support vector machine algorithm,and the parameters of ship network intrusion detection classifier are optimized by introducing harmony search algorithm.Finally,a ship network intrusion detection classifier is selected.The intrusion detection data is tested as an example.Support Vector Machine(SVM)overcomes the limitations of current ship network intrusion detection.The correct rate of ship network intrusion detection exceeds 90%,which reduces the error of ship network intrusion detection.The effect of ship network intrusion detection is better than other current ship network intrusion detection models.It is an effective and effective ship network intrusion detection model.