...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Computer science review >Deep Learning Algorithms for Cybersecurity Applications: A Technological and Status Review
【24h】

Deep Learning Algorithms for Cybersecurity Applications: A Technological and Status Review

机译：网络安全应用的深度学习算法：技术和地位审查

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Cybersecurity mainly prevents the hardware, software, and data present in the system that has an active internet connection from external attacks. Organizations mainly deploy cybersecurity for their databases and systems to prevent it from unauthorized access. Different forms of attacks like phishing, spear-phishing, a drive-by attack, a password attack, denial of service, etc. are responsible for these security problems In this survey, we analyzed and reviewed the usage of deep learning algorithms for Cybersecurity applications. Deep learning which is also known as Deep Neural Networks includes machine learning techniques that enable the network to learn from unsupervised data and solve complex problems. Here, 80 papers from 2014 to 2019 have been used and successfully analyzed. Deep learning approaches such as Convolutional Neural Network (CNN), Auto Encoder (AE), Deep Belief Network (DBN), Recurrent Neural Network (RNN), Generative Adversal Network (GAN) and Deep Reinforcement Learning (DIL) are used to categorize the papers referred. Each specific technique is effectively discussed with its algorithms, platforms, dataset, and potential benefits. The paper related to deep learning with cybersecurity is mainly published in the year 2018 in a large number and 18% of published articles originate from the UK. In addition, the papers are selected from a variety of journals, and 30% of papers used are from the Elsevier journal. From the experimental analysis, it is clear that the deep learning model improved the accuracy, scalability, reliability, and performance of the cybersecurity applications when applied in realtime.
机译：网络安全主要是防止系统中存在有源Internet连接的系统中的硬件，软件和数据。组织主要部署网络安全的数据库和系统，以防止其未经授权的访问。不同形式的攻击形式，如网络钓鱼，矛网络钓鱼，驱动器攻击，密码攻击，拒绝服务等负责本调查中的这些安全问题，我们分析并审查了对网络安全应用的深度学习算法的用法。深度学习，也称为深度神经网络，包括机器学习技术，使网络能够从无监督的数据和解决复杂问题。在这里，2014年至2019年的80篇论文已被使用并成功分析。诸如卷积神经网络（CNN），自动编码器（AE），深度信仰网络（DBN），经常性神经网络（RNN），生成犯错网（GAN）和深增强学习（DIM）的深度学习方法用于分类提到论文。用其算法，平台，数据集和潜在福利有效地讨论了每个特定技术。与网络安全有关的论文主要发表于2018年，大量，18％的已发表的文章来自英国。此外，纸张选自各种期刊，其中30％的纸张来自elestvier杂志。从实验分析开始，很明显，深度学习模型在实时应用时改善了网络安全应用的准确性，可扩展性，可靠性和性能。

著录项

  • 来源
    《Computer science review》 |2021年第2期|100317.1-100317.15|共15页
  • 作者

    Priyanka Dixit; Sanjay Silakari;

  • 作者单位

    Department of Computer Science & Engg University Institute of Technology Rajiv Gandhi Proudyogiki Vishwavidyalaya Bhopal (M.P) India;

    Department of Computer Science & Engg University Institute of Technology Rajiv Gandhi Proudyogiki Vishwavidyalaya Bhopal (M.P) India;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Cybersecurity; Deep learning; Attack; Supervised and unsupervised;

    机译：网络安全;深度学习;攻击;监督和无人监督;

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号