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變頻矢量控制系統入侵檢測技術

Intrusion detection techniques of variable-frequency vector control system

  • 摘要: 針對采用以太網控制自動化技術(EtherCAT)工業總線的感應電機交-直-交變頻矢量控制系統的入侵檢測技術進行了研究. 首先通過對EtherCAT總線協議進行深度解析, 結合目前為止已經發現的EtherCAT工業總線常見協議漏洞, 提取協議數據包的關鍵特征并構建EtherCAT總線協議入侵檢測規則庫, 采用三維指針鏈表樹作為針對EtherCAT總線協議規則庫的檢索數據結構; 其次, 根據感應電機交-直-交變頻矢量控制系統的物理模型, 進行模型參數仿真計算, 并根據仿真計算值, 構建矢量控制模型入侵特征的最小二乘支持向量機(least square support vector machine, LSSVM)分類器, 使用混沌粒子群優化(choatics particle swarm optimization, CPSO)算法對分類器的參數進行優化, 二者共同構成了CPSO-LSSVM入侵檢測分類算法. 異常數據包在被分類后, 會被傳遞給Suricata入侵檢測引擎進行精確規則匹配; 最后為該入侵檢測系統搭建物理實驗環境, 經過測試, 本文中的交-直-交變頻矢量控制模型仿真結果動態性能良好, 與實際矢量控制系統參數的波形變化趨勢相近. 通過抽取KDD Cup99測試數據集中的一部分對該入侵檢測系統實施DOS攻擊、R2L、U2R以及PROBING攻擊行為, 驗證該入侵檢測系統的有效性.

     

    Abstract: As induction motors are the control core in variable-frequency speed-regulating systems, their efficient operation in industrial production processes needs to be ensured. To realize this, the accuracy and security of control commands and equipment parameters have been the priorities for industrial security protection research. This study aims to investigate the intrusion detection techniques of the AC-DC-AC variable-frequency vector control system for induction motors under EtherCAT industrial bus. First, the EtherCAT bus protocol is deeply analyzed, and combined with the EtherCAT industrial bus common protocol vulnerabilities that have been discovered so far, the key characteristics of the protocol data packets are extracted, and the EtherCAT bus protocol intrusion detection rule base is constructed. A three-dimensional pointer linked list tree is used as the retrieval data structure for the EtherCAT bus protocol rule base. Second, model parameters are simulated and calculated based on the physical model of the AC-DC-AC inverter vector control system of the induction motor. Then a least-squares support vector machine (LSSVM) with the characteristics of vector control model intrusion is constructed on the basis of the simulation results, and the parameters of LSSVM classifier are optimized using the chaotic particle swarm optimization (CPSO) algorithm, both of which constitute the CPSO-LSSVM intrusion detection classification algorithm. After the anomaly data packets are classified, they will be transferred to the Suricata intrusion detection engine for precise rule matching. Finally, a physical experiment environment is built for the intrusion detection system. The simulation results of the AC-DC-AC variable-frequency vector control model in this paper show good dynamic performance, which is similar to the trend of waveform change on actual vector control system parameters. The effectiveness of the intrusion detection system is verified by extracting part of the KDD Cup99 test dataset to implement the behaviors of attacks, such as the denial of service (DOS), remote-to-local (R2L), user-to-root (U2R), and Probing attacks on the intrusion detection system.

     

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