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Optimal DBN-based distributed attack detection model for Internet of Things

机译：基于DBN的基于DBN的分布式攻击检测模型

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摘要

This paper introduces a new detection mechanism for defending the cyberspace with a new logic that aiding the concept of deep learning. The process involves two phases, namely, feature extraction and classification. The initial phase is the feature extraction, in which the features are extracted from the given input data by the renowned principal component analysis (PCA). Subsequently, the extracted features are subjected to the classification phase, where the deep belief network (DBN) model is used. The DBN model classifies the presence of attacks like denial of service (DoS), probe, R2L, and U2R. In order to make the performance more excellent, this paper diverts the strategy to a new concept termed "Optimization Concept." Here, the hidden neuron of DBN is optimally selected by a new algorithm termed novel mutation rate-based lion algorithm (NMR-LA), which is the modified model of lion algorithm (LA). The performance of proposed algorithm NMR-LA is compared over the conventional models in terms of both positive and negative measures like accuracy, sensitivity, specificity, precision, negative predictive value (NPV), F1 score and Mathews correlation coefficient (MCC), false-positive rate (FPR), false-negative rate (FNR), and false-discovery rate (FDR) and proves the betterments of proposed work.
机译：本文介绍了一种新的检测机制，用于捍卫网络空间，并具有帮助深入学习概念的新逻辑。该过程涉及两个阶段，即特征提取和分类。初始阶段是特征提取，其中通过着名的主成分分析（PCA）从给定的输入数据中提取特征。随后，对提取的特征进行分类阶段，其中使用深信念网络（DBN）模型。 DBN模型对拒绝服务（DOS），探测器，R2L和U2R等攻击的存在进行分类。为了使表现更加优秀，本文将该策略转移到一个新的概念被称为“优化概念”。这里，DBN的隐藏神经元通过新的突变率基狮子算法（NMR-LA）的新算法最佳地选择，这是狮子算法（LA）的修改模型。在常规模型中，在正常和负面测量等方面比较了算法NMR-LA的性能，如精度，灵敏度，特异性，精度，负预测值（NPV），F1得分和Mathews相关系数（MCC），假 - 阳性率（FPR），假负率（FNR）和虚假发现率（FDR）并证明提出的工作的提高。

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