基于深度自编码网络的安全态势要素获取机制
发布时间：2018-07-27 19:32
【摘要】：针对大规模网络态势要素获取时间复杂度较高和攻击样本不平衡导致小类样本分类精度不高的问题,提出一种基于深度自编码网络的态势要素获取机制。在该机制下,利用优化后的深度自编码网络作为基分类器,识别数据类型。一方面,在自编码网络的逐层训练中,提出一种结合交叉熵(CE)函数和反向传播(BP)算法的训练规则,克服传统的方差代价函数更新权值过慢的缺陷;另一方面,在深度网络的微调和分类阶段,提出一种主动在线采样(AOS)算法应用于分类器中,通过在线选择用于更新网络权值的攻击样本,达到总样本的去冗余和平衡各类攻击样本数量的目的,从而提高小类攻击样本的分类精度。经对实例数据的仿真分析,该方案有较好的态势要素获取精度,并能有效减少数据传输时的通信开销。
[Abstract]:Aiming at the problem of high time complexity of acquisition of situation elements in large-scale networks and unbalance of attack samples resulting in low classification accuracy of small class samples, a novel approach based on deep self-coding network is proposed. In this mechanism, the optimized depth self-coding network is used as the base classifier to identify the data types. On the one hand, a new training rule combining cross-entropy (CE) function and back-propagation (BP) algorithm is proposed to overcome the disadvantage of the traditional variance cost function updating the weight too slowly in the self-coding network layer by layer training; on the other hand, In the stage of fine-tuning and classification of depth network, an active on-line sampling (AOS) algorithm is proposed to be applied to classifier, and attack samples are selected online to update network weights. The goal of eliminating redundancy of total samples and balancing the number of attack samples is achieved so as to improve the classification accuracy of small attack samples. The simulation results show that the scheme has better precision of acquisition of situation elements and can effectively reduce the communication cost of data transmission.
【作者单位】： 重庆市移动通信重点实验室(重庆邮电大学);
【基金】：国家自然科学基金资助项目(61271260,61301122) 重庆市科委自然科学基金资助项目(cstc2015jcyjA40050)~~
【分类号】：TP393.08
，
本文编号：2148917
[Abstract]:Aiming at the problem of high time complexity of acquisition of situation elements in large-scale networks and unbalance of attack samples resulting in low classification accuracy of small class samples, a novel approach based on deep self-coding network is proposed. In this mechanism, the optimized depth self-coding network is used as the base classifier to identify the data types. On the one hand, a new training rule combining cross-entropy (CE) function and back-propagation (BP) algorithm is proposed to overcome the disadvantage of the traditional variance cost function updating the weight too slowly in the self-coding network layer by layer training; on the other hand, In the stage of fine-tuning and classification of depth network, an active on-line sampling (AOS) algorithm is proposed to be applied to classifier, and attack samples are selected online to update network weights. The goal of eliminating redundancy of total samples and balancing the number of attack samples is achieved so as to improve the classification accuracy of small attack samples. The simulation results show that the scheme has better precision of acquisition of situation elements and can effectively reduce the communication cost of data transmission.
【作者单位】： 重庆市移动通信重点实验室(重庆邮电大学);
【基金】：国家自然科学基金资助项目(61271260,61301122) 重庆市科委自然科学基金资助项目(cstc2015jcyjA40050)~~
【分类号】：TP393.08
，
本文编号：2148917
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