A Survey of Deep Learning Methods for Cyber Security

被引：252
|
作者：
Berman, Daniel S. [1 ]
Buczak, Anna L. [1 ]
Chavis, Jeffrey S. [1 ]
Corbett, Cherita L. [1 ]
机构：
[1] JHU, Appl Phys Lab, APL1, Laurel, MD 20910 USA
来源：
INFORMATION | 2019年 / 10卷 / 04期
关键词：
cyber analytics; deep learning; deep neural networks; deep autoencoders; deep belief networks; restricted Boltzmann machines; convolutional neural networks; ANDROID MALWARE CHARACTERIZATION; NETWORK INTRUSION DETECTION; ATTACK DETECTION; BELIEF NETWORKS; NEURAL-NETWORKS; REPRESENTATIONS; CLASSIFICATION; INTERNET;
D O I：
10.3390/info10040122
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This survey paper describes a literature review of deep learning (DL) methods for cyber security applications. A short tutorial-style description of each DL method is provided, including deep autoencoders, restricted Boltzmann machines, recurrent neural networks, generative adversarial networks, and several others. Then we discuss how each of the DL methods is used for security applications. We cover a broad array of attack types including malware, spam, insider threats, network intrusions, false data injection, and malicious domain names used by botnets.
引用
收藏
页数：35
相关论文
共 50 条
  • [1] Deep learning algorithms for cyber security applications: A survey
    Li, Guangjun
    Sharma, Preetpal
    Pan, Lei
    Rajasegarar, Sutharshan
    Karmakar, Chandan
    Patterson, Nicholas
    [J]. JOURNAL OF COMPUTER SECURITY, 2021, 29 (05) : 447 - 471
  • [2] Survey on applications of deep learning and machine learning techniques for cyber security
    Alghamdi M.I.
    [J]. Alghamdi, Mohammed I. (mialmushilah@bu.edu.sa), 2020, International Association of Online Engineering (14): : 210 - 224
  • [3] Generalization of Deep Learning for Cyber-Physical System Security: A Survey
    Wickramasinghe, Chathurika S.
    Marino, Daniel L.
    Amarasinghe, Kasun
    Manic, Milos
    [J]. IECON 2018 - 44TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY, 2018, : 745 - 751
  • [4] A Survey of Security Protection Methods for Deep Learning Model
    Peng H.
    Bao S.
    Li L.
    [J]. IEEE Transactions on Artificial Intelligence, 2024, 5 (04): : 1533 - 1553
  • [5] Deep Reinforcement Learning for Cyber Security
    Thanh Thi Nguyen
    Reddi, Vijay Janapa
    [J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2023, 34 (08) : 3779 - 3795
  • [6] A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection
    Buczak, Anna L.
    Guven, Erhan
    [J]. IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, 2016, 18 (02): : 1153 - 1176
  • [7] A Survey of Game Theoretic Methods for Cyber Security
    Wang, Yuan
    Wang, Yongjun
    Liu, Jing
    Huang, Zhijian
    Xie, Peidai
    [J]. 2016 IEEE FIRST INTERNATIONAL CONFERENCE ON DATA SCIENCE IN CYBERSPACE (DSC 2016), 2016, : 631 - 636
  • [8] Machine Learning and Deep Learning in Cyber Security for IoT
    Velliangiri, S.
    Kasaraneni, Kenanya Kumar
    [J]. Lecture Notes in Electrical Engineering, 2020, 601 : 975 - 981
  • [9] On the Effectiveness of Machine and Deep Learning for Cyber Security
    Apruzzese, Giovanni
    Colajanni, Michele
    Ferretti, Luca
    Guido, Alessandro
    Marchetti, Mirco
    [J]. 2018 10TH INTERNATIONAL CONFERENCE ON CYBER CONFLICT (CYCON X): MAXIMISING EFFECTS, 2018, : 371 - 389
  • [10] Data Mining for the Security of Cyber Physical Systems Using Deep-Learning Methods
    Nath, Bhagawan
    Hamaleinen, Timo
    Ezekiel, Soundararajan
    [J]. PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON CYBER WARFARE AND SECURITY (ICCWS 2022), 2022, : 591 - 598
12345