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Double Layered Priority based Gray Wolf Algorithm (PrGWO-SK) for safety management in IoT network through anomaly detection
参考中译:基于双层优先级的Gray Wolf算法(PrGWO-SK)用于物联网网络异常检测的安全管理


          

刊名：Maintenance and Reliability
作者：Akhileshwar Prasad Agrawal（Guru Gobind Singh Indraprastha University, Dept of Computer Science and Engg., Ambedkar Institute of Advanced Communication Technologies and Research (now NSUT-E)）
Nanhay Singh（Guru Gobind Singh Indraprastha University, Dept of Computer Science and Engg., Ambedkar Institute of Advanced Communication Technologies and Research (now NSUT-E)）
刊号：780LH011
ISSN：1507-2711
出版年：2022
年卷期：2022, vol.24, no.4
页码：641-654
总页数：14
分类号：TB114.3
关键词：Gray wolf optimizer；Anomaly detection；Feature selection；Predictive maintenance
参考中译：灰狼优化器；异常检测；特征选择；预测性维护
语种：eng
文摘：For mitigating and managing risk failures due to Internet of Things (IoT) attacks, many Machine Learning (ML) and Deep Learning (DL) solutions have been used to detect attacks but mostly suffer from the problem of high dimensionality. The problem is even more acute for resource starved IoT nodes to work with high dimension data. Motivated by this problem, in the present work a priority based Gray Wolf Optimizer is proposed for effectively reducing the input feature vector of the dataset. At each iteration all the wolves leverage the relative importance of their leader wolves' position vector for updating their own positions. Also, a new inclusive fitness function is hereby proposed which incorporates all the important quality metrics along with the accuracy measure. In a first, SVM is used to initialize the proposed PrGWO population and kNN is used as the fitness wrapper technique. The proposed approach is tested on NSL-KDD, DS2OS and BoTIoT datasets and the best accuracies are found to be 99.60%, 99.71% and 99.97% with number of features as 12,6 and 9 respectively which are better than most of the existing algorithms.
参考中译：为了缓解和管理物联网(IoT)攻击导致的风险故障，许多机器学习(ML)和深度学习(DL)解决方案已被用于检测攻击，但大多存在高维问题。对于资源匮乏的物联网节点来说，处理高维数据的问题更加严重。针对这一问题，本文提出了一种基于优先级的Gray Wolf优化器，以有效地减少数据集的输入特征向量。在每一次迭代中，所有狼都利用它们的领头狼的位置向量的相对重要性来更新它们自己的位置。此外，本文还提出了一种新的包含适应度函数，它结合了所有重要的质量度量和准确度度量。首先，使用支持向量机对提出的PrGWO种群进行初始化，并使用KNN作为适应度包装器技术。该方法在NSL-KDD、DS2OS和BoTIoT数据集上进行了测试，准确率分别为99.60%、99.71%和99.97%，特征数分别为12、6和9，优于现有的大多数算法。



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