基于数据挖掘的船舶通信网络恶意攻击检测研究

Research on Malicious Attack Detection of Ship Communication Network Based on Data Mining

网络恶意攻击检测主要采用属性指标检测,但检测精准度和效率较低,适应性差,为此提出基于数据挖掘船舶通信网络恶意攻击检测。将需要检测的全部数据预处理,创建网络节点受攻击数据流检测模型,运用时频分析法得出恶意攻击节点时延尺度,对攻击滤波做出对应干扰,利用滤波后网络传输信号进行谱分析,获取出谱密度特征,并根据分布差异性实现对攻击节点的特征提取,完成船舶通信网络恶意攻击检测。仿真实验结果表明,在船舶通信环境不稳定下所提出算法依旧拥有较强检测精度和效率,具有极高适用性以及可靠性。

Network malicious attack detection mainly uses attribute index detection, but the detection accuracy and efficiency is low, and the adaptability is poor. Therefore, this paper proposes the detection of malicious attacks in ship communication network based on data mining. After preprocessing all the data that need to be detected, the network node attacked data flow detection model is created. The time-frequency analysis method is used to obtain the malicious attack node delay scale, and the corresponding interference is made to the attack filter. The spectrum density characteristics are obtained by spectrum analysis of the network transmission signal after filtering, and the characteristics of the attack node are extracted according to the distribution difference, and the ship communication is completed network malicious attack detection. Simulation results show that the proposed algorithm still has strong detection accuracy and efficiency, and has high applicability and reliability under the unstable ship communication environment.