research-article

Fast malware classification by automated behavioral graph matching

Authors:

Douglas Reeves,

Vikram Mulukutla,

Balaji SundaravelAuthors Info & Claims

CSIIRW '10: Proceedings of the Sixth Annual Workshop on Cyber Security and Information Intelligence Research

Article No.: 45, Pages 1 - 4

https://doi.org/10.1145/1852666.1852716

Published: 21 April 2010 Publication History

Abstract

Malicious software (malware) is a serious problem in the Internet. Malware classification is useful for detection and analysis of new threats for which signatures are not available, or possible (due to polymorphism). This paper proposes a new malware classification method based on maximal common subgraph detection. A behavior graph is obtained by capturing system calls during the execution (in a sandboxed environment) of the suspicious software. The method has been implemented and tested on a set of 300 malware instances in 6 families. Results demonstrate the method effectively groups the malware instances, compared with previous methods of classification, is fast, and has a low false positive rate when presented with benign software.

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Cited By

Yang YWu HWang YWang P(2024)AMN: Attention-based Multimodal Network for Android Malware Classification2024 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE International Conference on Robotics, Automation and Mechatronics (RAM)10.1109/CIS-RAM61939.2024.10672730(7-13)Online publication date: 8-Aug-2024
https://doi.org/10.1109/CIS-RAM61939.2024.10672730
Mester A(2023)Malware Analysis and Static Call Graph Generation with Radare2Studia Universitatis Babeș-Bolyai Informatica10.24193/subbi.2023.1.0168:1(5-20)Online publication date: 20-Jul-2023
https://doi.org/10.24193/subbi.2023.1.01
Casey WMelaragno A(2023)Scaling a Machine Learning Approach to many kinds of Malicious Behavior for Cybersecurity2023 AEIT International Annual Conference (AEIT)10.23919/AEIT60520.2023.10330312(1-6)Online publication date: 5-Oct-2023
https://doi.org/10.23919/AEIT60520.2023.10330312
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Index Terms

Fast malware classification by automated behavioral graph matching
1. General and reference
  1. Cross-computing tools and techniques
    1. Metrics
2. Social and professional topics
  1. Computing / technology policy

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cover image ACM Other conferences

CSIIRW '10: Proceedings of the Sixth Annual Workshop on Cyber Security and Information Intelligence Research

April 2010

257 pages

ISBN:9781450300179

DOI:10.1145/1852666

Editors:
Frederick T. Sheldon
Oak Ridge National Laboratory
,
Stacy Prowell
Oak Ridge National Laboratory
,
Robert K. Abercrombie
Oak Ridge National Laboratory
,
Axel Krings
University of Idaho

Copyright © 2010 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]

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Association for Computing Machinery

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Published: 21 April 2010

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CSIIRW '10

CSIIRW '10: Annual Cyber Security and Information Intelligence Research Workshop

April 21 - 23, 2010

Tennessee, Oak Ridge, USA

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Cited By

Yang YWu HWang YWang P(2024)AMN: Attention-based Multimodal Network for Android Malware Classification2024 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE International Conference on Robotics, Automation and Mechatronics (RAM)10.1109/CIS-RAM61939.2024.10672730(7-13)Online publication date: 8-Aug-2024
https://doi.org/10.1109/CIS-RAM61939.2024.10672730
Mester A(2023)Malware Analysis and Static Call Graph Generation with Radare2Studia Universitatis Babeș-Bolyai Informatica10.24193/subbi.2023.1.0168:1(5-20)Online publication date: 20-Jul-2023
https://doi.org/10.24193/subbi.2023.1.01
Casey WMelaragno A(2023)Scaling a Machine Learning Approach to many kinds of Malicious Behavior for Cybersecurity2023 AEIT International Annual Conference (AEIT)10.23919/AEIT60520.2023.10330312(1-6)Online publication date: 5-Oct-2023
https://doi.org/10.23919/AEIT60520.2023.10330312
Li XYan ZZhang SLi XWang F(2023)Formal Characterization of Malicious Code in Power Information Systems Based on Multidimensional Feature FusionProceedings of the 8th International Conference on Cyber Security and Information Engineering10.1145/3617184.3630149(274-281)Online publication date: 22-Sep-2023
https://dl.acm.org/doi/10.1145/3617184.3630149
Xu YChen Z(2023)Family Classification based on Tree Representations for MalwareProceedings of the 14th ACM SIGOPS Asia-Pacific Workshop on Systems10.1145/3609510.3609818(65-71)Online publication date: 24-Aug-2023
https://dl.acm.org/doi/10.1145/3609510.3609818
Zhong FChen ZXu MZhang GYu DCheng X(2023)Malware-on-the-Brain: Illuminating Malware Byte Codes With Images for Malware ClassificationIEEE Transactions on Computers10.1109/TC.2022.316035772:2(438-451)Online publication date: 1-Feb-2023
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Xu YChen Z(2023)Familial Graph Classification of Malware based on Structured API Call Sequences2023 IEEE 23rd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)10.1109/QRS-C60940.2023.00072(167-175)Online publication date: 22-Oct-2023
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Priya VSathya Sofia A(2023)Review on Malware Classification and Malware Detection Using Transfer Learning Approach2023 5th International Conference on Smart Systems and Inventive Technology (ICSSIT)10.1109/ICSSIT55814.2023.10061076(1042-1049)Online publication date: 23-Jan-2023
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Bayazit ESahingoz ODogan B(2023)Protecting Android Devices From Malware Attacks: A State-of-the-Art Report of Concepts, Modern Learning Models and ChallengesIEEE Access10.1109/ACCESS.2023.332339611(123314-123334)Online publication date: 2023
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Benchadi DBatalo BFukui K(2023)Efficient Malware Analysis Using Subspace-Based Methods on Representative Image PatternsIEEE Access10.1109/ACCESS.2023.331340911(102492-102507)Online publication date: 2023
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