利用深度学习方法识别物联网内部的网络数据安全

Apurb Kumar, M.Jogendra Kumar, N. Sai, T. R. Kumar
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引用次数: 0

摘要

随着与网络相关的组织数量不断增加,包括循环计算系统和物联网(IoT),由于数据和步骤关联流量的巨大维度,对网络攻击的反应变得更加考验。最近,专家们提出了深度学习(DL)估计,通过规划测试数据和调整敌意异常实例来描绘中断的特征。然而,由于数据的巨大和不平等性质,目前的DL分类器在感知当前关联的惊讶和正常排列关系方面并不完全实用。然后,为干扰发现结构(IDS)设计一个自适应模型来处理可识别的攻击。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identification of Network Data security inside the IOT by using Deep learning approach
With an expanding number of organizations related with the web, including circulated figuring systems and the Internet of Things (IoT), the reaction to cyberattacks has become more testing because of the huge dimensionality of data and steps association traffic. As of late, experts have proposed profound learning (DL) estimations to portray the features of interruption by planning test data and adjusting instances of animosity abnormalities. Notwithstanding, because of the huge things and unequal nature of the data, current DL classifiers are not completely practical to perceive surprising and normal arrangement relationship for the present associations. Then, plan a self-adaptable model for a disturbance discovery structure (IDS) to deal with distinguishing attacks.
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