research-article

An Efficient Malicious Code Detection System Based on Convolutional Neural Networks

Authors:

Fei Xue,

Yongli YangAuthors Info & Claims

CSAI '18: Proceedings of the 2018 2nd International Conference on Computer Science and Artificial Intelligence

Pages 86 - 89

https://doi.org/10.1145/3297156.3297246

Published: 08 December 2018 Publication History

Get Access

Abstract

With the rapid development of the Internet, people have variety in their life style. While the Internet is developing rapidly, some malware has also been generated, and the number of malicious codes grows exponentially, which seriously affects the security of the Internet. In the prevention of malicious code, the accurate classification of malicious code is the most critical components. At present, researchers mainly focus on static detection and dynamic detection to study malicious code variant detection schemes. In the study of malicious code classification, this paper first analyzes the limitations of static detection and dynamic detection, then employs convolution neural networks to classify and identify malicious code, and finally implements an efficient malicious code detection system based on convolutional neural networks. Our malware detection model realizes the idea of automatic detection of malicious code variants. Experimental results demonstrated that the proposed malicious code detection system is efficient.

References

[1]

Zhihua Cui, Fei Xue, Xingjuan Cai, Yang Cao, Gai Ge Wang, and Jinjun Chen. 2018. Detection of Malicious Code Variants Based on Deep Learning. IEEE Transactions on Industrial Informatics 14, 7 (2018), 3187--3196.

Crossref

Google Scholar

[2]

Jianwen Fu, Jingfeng Xue, Yong Wang, Zhenyan Liu, and Chun Shan. 2018. Malware Visualization for Fine-Grained Classification. IEEE Access 6 (2018), 14510--14523.

Crossref

Google Scholar

[3]

Li Li, Daoyuan Li, Tegawendé F Bissyandé, Jacques Klein, Yves Le Traon, David Lo, and Lorenzo Cavallaro. 2017. Understanding android app piggybacking: A systematic study of malicious code grafting. IEEE Transactions on Information Forensics and Security 12, 6 (2017), 1269--1284.

Digital Library

Google Scholar

[4]

Annamalai Narayanan, Mahinthan Chandramohan, Lihui Chen, and Yang Liu. 2017. A multi-view context-aware approach to Android malware detection and malicious code localization. Empirical Software Engineering (2017), 1--53.

Google Scholar

[5]

Yong Qiao, Yuexiang Yang, Jie He, Chuan Tang, and Zhixue Liu. 2014. CBM: Free, Automatic Malware Analysis Framework Using API Call Sequences. Springer Berlin Heidelberg. 225--236.

Google Scholar

[6]

Sathyanarayan, V Sai, Kohli, Pankaj and Bruhadeshwar, Bezawada. 2008. Signature Generation and Detection of Malware Families. In Information Security and Privacy. 336--349.

Digital Library

Google Scholar

[7]

Carsten Willems, Thorsten Holz, and Felix Freiling. 2007. Toward Automated Dynamic Malware Analysis Using CWSandbox. IEEE Security & Privacy 5, 2 (2007), 32--39.

Digital Library

Google Scholar

[8]

Mihai Christodorescu, Somesh Jha, Sanjit A Seshia, Dawn Song, and Randal E Bryant. 2005. Semantics-aware malware detection. In IEEE Symposium on Security and Privacy. 32--46.

Digital Library

Google Scholar

[9]

Pengbin Feng, Jianfeng Ma, Cong Sun, Xinpeng Xu, and Yuwan Ma. 2018. A Novel Dynamic Android Malware Detection System With Ensemble Learning. IEEE Access 6 (2018), 30996--31011.

Crossref

Google Scholar

[10]

Nataraj, Lakshmanan and Karthikeyan, Sreejith and Jacob, Gregoire and Manjunath, BS. 2011. Malware images: visualization and automatic classification. In International Symposium on Visualization for Cyber Security. 1--7.

Digital Library

Google Scholar

[11]

Lakshmanan Nataraj, Vinod Yegneswaran, Phillip Porras, and Jian Zhang. 2011. A comparative assessment of malware classification using binary texture analysis and dynamic analysis. In ACM Workshop on Security and Artificial Intelligence. 21--30.

Digital Library

Google Scholar

[12]

Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2012. ImageNet classification with deep convolutional neural networks. In International Conference on Neural Information Processing Systems. 1097--1105.

Digital Library

Google Scholar

[13]

Royi Ronen, Marian Radu, Corina Feuerstein, Elad Yom-Tov, and Mansour Ahmadi. 2018. Microsoft Malware Classification Challenge. arXiv preprint arXiv:1802.10135 (2018).

Google Scholar

Cited By

View all

Li JYang GShao Y(2024)Ransomware Detection Model Based on Adaptive Graph Neural Network LearningApplied Sciences10.3390/app1411457914:11(4579)Online publication date: 27-May-2024
https://doi.org/10.3390/app14114579
Li JNiu JQian XLiu Y(2023)Real-Time Ransomware Detection Method Based on TextGCN2023 6th International Conference on Artificial Intelligence and Big Data (ICAIBD)10.1109/ICAIBD57115.2023.10206378(535-541)Online publication date: 26-May-2023
https://doi.org/10.1109/ICAIBD57115.2023.10206378
Cao DNing ZZhang SLiu J(2022)An Effective Data Balancing Strategy Based on Swarm Intelligence Algorithm for Malicious Code Detection and ClassificationBio-Inspired Computing: Theories and Applications10.1007/978-981-19-1256-6_12(160-173)Online publication date: 24-Mar-2022
https://doi.org/10.1007/978-981-19-1256-6_12
Show More Cited By

Index Terms

An Efficient Malicious Code Detection System Based on Convolutional Neural Networks
1. Computing methodologies
  1. Artificial intelligence
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Security and privacy
  1. Intrusion/anomaly detection and malware mitigation
    1. Malware and its mitigation
  2. Security services

Recommendations

Intrusion Detection System for NSL-KDD Dataset Using Convolutional Neural Networks
CSAI '18: Proceedings of the 2018 2nd International Conference on Computer Science and Artificial Intelligence

With the increment of cyber traffic, there is a growing demand for cyber security. How to accurately detect cyber intrusions is the hotspot of recent research. Traditional Intrusion Detection Systems (IDS), based on traditional machine learning methods, ...
Detection of metamorphic malicious mobile code on android-based smartphones

By repackaging a malicious code into reverse compiled legitimate mobile code, malware authors can bypass detection step on existing mobile vaccine software using inserting AES-encrypted root exploits to loading some payload from a malicious remote ...
Malware detection approach based on deep convolutional neural networks

Malware detection field becomes more valuable nowadays regarding the continuously growing number of malware codes emerging everyday. Besides, machine learning techniques have been widely used in various fields. For the purpose of employing machine ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

CSAI '18: Proceedings of the 2018 2nd International Conference on Computer Science and Artificial Intelligence

December 2018

641 pages

ISBN:9781450366069

DOI:10.1145/3297156

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]

In-Cooperation

Shenzhen University: Shenzhen University

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 December 2018

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

CSAI '18

CSAI '18: 2018 2nd International Conference on Computer Science and Artificial Intelligence

December 8 - 10, 2018

Shenzhen, China

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
186
Total Downloads

Downloads (Last 12 months)3
Downloads (Last 6 weeks)1

Reflects downloads up to 11 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Li JYang GShao Y(2024)Ransomware Detection Model Based on Adaptive Graph Neural Network LearningApplied Sciences10.3390/app1411457914:11(4579)Online publication date: 27-May-2024
https://doi.org/10.3390/app14114579
Li JNiu JQian XLiu Y(2023)Real-Time Ransomware Detection Method Based on TextGCN2023 6th International Conference on Artificial Intelligence and Big Data (ICAIBD)10.1109/ICAIBD57115.2023.10206378(535-541)Online publication date: 26-May-2023
https://doi.org/10.1109/ICAIBD57115.2023.10206378
Cao DNing ZZhang SLiu J(2022)An Effective Data Balancing Strategy Based on Swarm Intelligence Algorithm for Malicious Code Detection and ClassificationBio-Inspired Computing: Theories and Applications10.1007/978-981-19-1256-6_12(160-173)Online publication date: 24-Mar-2022
https://doi.org/10.1007/978-981-19-1256-6_12
Liang JNing ZZhou YCao D(2021)Fine-grained Classification of Malicious Code Based on CNN and Multi-resolution Feature Fusion2021 6th International Conference on Computational Intelligence and Applications (ICCIA)10.1109/ICCIA52886.2021.00031(123-127)Online publication date: Jun-2021
https://doi.org/10.1109/ICCIA52886.2021.00031
Zhao JMasood RSeneviratne S(2021)A Review of Computer Vision Methods in Network SecurityIEEE Communications Surveys & Tutorials10.1109/COMST.2021.308647523:3(1838-1878)Online publication date: Nov-2022
https://doi.org/10.1109/COMST.2021.3086475

View Options

Login options

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

Cited By

Index Terms

Recommendations

Intrusion Detection System for NSL-KDD Dataset Using Convolutional Neural Networks

Detection of metamorphic malicious mobile code on android-based smartphones

Malware detection approach based on deep convolutional neural networks