基于Entrance分类器的IIoT鲁棒攻击检测方法
Robust Attack Detection Approach for IIoT Using Ensemble Classifier
摘要：
通常，由于对互联网的依赖和物联网设备的最小资源可用性，IIoT及其相关应用程序与恶意威胁相关的风险正在增加。因此，物联网网络基于异常的入侵检测模型至关重要。需要为IIoT网络开发独特的检测方法，因为威胁检测是利益相关者的重要期望。机器学习方法被认为是根据经验学习的进化技术，并且这样的方法已经在各种应用中产生了优异的性能，例如模式识别、离群值分析和语音识别。传统的技术和工具不足以保护工业物联网网络，因为工业系统中使用各种协议，而且扩展的可能性有限。本文的目标是开发一个两阶段异常检测模型，以提高IIoT网络的可靠性。在第一阶段，SVM和朴素贝叶斯集成使用集成混合技术。在使用不同的训练和测试比率训练数据的同时执行K折交叉验证，以获得优化的训练集和测试集。包围混合使用随机森林技术来预测类标签。人工神经网络（ANN）分类器也用于预测，该分类器使用Adam优化器来实现更好的准确性。在第二阶段，ANN和随机森林结果都被馈送到模型的分类单元，并且最高精度值被认为是最终结果。该模型在标准物联网攻击数据集上进行了测试，如WUSTL_IIOT-2018，N_BaIoT和Bot_IoT。最高精度达到99%。结果还表明，该模型优于传统技术，从而提高了IIoT网络的可靠性。
英文摘要：
Generally, the risks associated with malicious threats are increasing for the IIoT and its related applications due to dependency on the Internet and the minimal resource availability of IoT devices. Thus, anomaly-based intrusion detection models for IoT networks are vital. Distinct detection methodologies need to be developed for the IIoT network as threat detection is a significant expectation of stakeholders. Machine learning approaches are considered to be evolving techniques that learn with experience, and such approaches have resulted in superior performance in various applications, such as pattern recognition, outlier analysis, and speech recognition. Traditional techniques and tools are not adequate to secure IIoT networks due to the use of various protocols in industrial systems and restricted possibilities of upgradation. In this paper, the objective is to develop a two-phase anomaly detection model to enhance the reliability of an IIoT network. In the first phase, SVM and Naive Bayes are integrated using an ensemble blending technique. K-fold cross-validation is performed while training the data with different training and testing ratios to obtain optimized training and test sets. Ensemble blending uses a random forest technique to predict class labels. An Artificial Neural Network (ANN) classifier that uses the Adam optimizer to achieve better accuracy is also used for prediction. In the second phase, both the ANN and random forest results are fed to the model's classification unit, and the highest accuracy value is considered the final result. The proposed model is tested on standard IoT attack datasets, such as WUSTL_IIOT-2018, N_BaIoT, and Bot_IoT. The highest accuracy obtained is 99%. The results also demonstrate that the proposed model outperforms traditional techniques and thus improves the reliability of an IIoT network.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
