skip to main content
10.1145/3206129.3239430acmotherconferencesArticle/Chapter ViewAbstractPublication PagesicemtConference Proceedingsconference-collections
research-article

Trend of Malware Detection Using Deep Learning

Authors Info & Claims
Published:02 July 2018Publication History

ABSTRACT

According to recent security trends, there are more variations of existing malware than new type malware. These malwares are causing a lot of damage, such as encrypting users ' data to leak personal information, delete data, and make financial demands. Although many studies are being conducted to analyze malwares in order to respond to the rapidly increasing malware with such malicious purpose, current malware analysis methods are for obfuscation, virtual environment bypass, etc. To overcome these difficulties, Deep Learning method that analyzes and utilizes harmful codes is receiving spotlight recently. Therefore, this thesis introduces trends on how to analyze malware based on Deep Learning.

References

  1. Kaspersky lab korea. report 26 % of the fastest evolving security threat, ransomware, is targeted at companies http://news.kaspersky.co.kr/news2017/12n/171201.htmGoogle ScholarGoogle Scholar
  2. Michael Sikorski, Andres Honig. 2012. Practical Malware analysis.Google ScholarGoogle Scholar
  3. Kyo-Il Chung, Han-na Park, Boo-Geum Jung, Jong-Soo Jang, Myung-Ae Chung. 2012. Big Data and Information Security. Korea Institute of Information Technology Magazine, 10(3), 17--22.Google ScholarGoogle Scholar
  4. Deep Learning from Scratch. 2016. O'Reilly JapanGoogle ScholarGoogle Scholar
  5. Daniel Gibert. Convolutional Neural Networks for Malware Classification. 2016. Master Thesis, Universitat de BarcelonaGoogle ScholarGoogle Scholar
  6. Jae Woon Park. 2016. Deep Learning Based Malware Detection Using API Features. Department of IT Convergence Information Security Graduate School of KonKuk Universi http://konkuk.dcollection.net/public_resource/pdf/000002316768_20180522134444.pdfGoogle ScholarGoogle Scholar
  7. Jun-ho Hwang, Tae-jin Lee. 2017. Android Malware Analysis Technology Research Based on Naive Bayes. Journal of the Korea Institute of Information Security & Cryptology, 27(5), 1087--1097.Google ScholarGoogle Scholar
  8. Hae Jung Kim, Eun Jun Yoon. 2017. Image-based Artificial Intelligence Deep Learning to Protect the Big Data from Malware. Journal of the Institute of Electronics and Information Engineers, 54(2), 76--82."Google ScholarGoogle Scholar
  9. Seonhee Seok, Howon Kim. 2016. Visualized Malware Classification Based-on Convolutional Neural Network. Journal of the Korea Institute of Information Security & Cryptology, 26(1), 197--208.Google ScholarGoogle ScholarCross RefCross Ref
  10. Sungtaek OH, Woong Go, Junhyung Park. 2017. A Study on the Machine Learning Algorithm for Mobile Malware Detection." Korea Information Science Society 1111--1113.Google ScholarGoogle Scholar
  11. Jin Hyun Yu. 2017. Study on DNN Based Android Malware Detection Method for Mobile Environment. Department of Information Security Graduate School of Information Security Korea UniversityGoogle ScholarGoogle Scholar

Index Terms

  1. Trend of Malware Detection Using Deep Learning

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Other conferences
      ICEMT '18: Proceedings of the 2nd International Conference on Education and Multimedia Technology
      July 2018
      127 pages
      ISBN:9781450365253
      DOI:10.1145/3206129

      Copyright © 2018 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 2 July 2018

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article
      • Research
      • Refereed limited

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader