Analysis of Machine learning Techniques Used in Behavior-Based Malware Detection

@article{Firdausi2010AnalysisOM,
  title={Analysis of Machine learning Techniques Used in Behavior-Based Malware Detection},
  author={Ivan Firdausi and Charles Lim and Alva Erwin and Anto Satriyo Nugroho},
  journal={2010 Second International Conference on Advances in Computing, Control, and Telecommunication Technologies},
  year={2010},
  pages={201-203}
}
  • Ivan Firdausi, Charles Lim, A. Nugroho
  • Published 2 December 2010
  • Computer Science
  • 2010 Second International Conference on Advances in Computing, Control, and Telecommunication Technologies
The increase of malware that are exploiting the Internet daily has become a serious threat. The manual heuristic inspection of malware analysis is no longer considered effective and efficient compared against the high spreading rate of malware. Hence, automated behavior-based malware detection using machine learning techniques is considered a profound solution. The behavior of each malware on an emulated (sandbox) environment will be automatically analyzed and will generate behavior reports… 

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References

SHOWING 1-8 OF 8 REFERENCES
Automatic analysis of malware behavior using machine learning
TLDR
An incremental approach for behavior-based analysis, capable of processing the behavior of thousands of malware binaries on a daily basis is proposed, significantly reduces the run-time overhead of current analysis methods, while providing accurate discovery and discrimination of novel malware variants.
Learning and Classification of Malware Behavior
TLDR
The effectiveness of the proposed method for learning and discrimination of malware behavior is demonstrated, especially in detecting novel instances of malware families previously not recognized by commercial anti-virus software.
TTAnalyze: A Tool for Analyzing Malware
TLDR
TTAnalyze is presented, a tool for dynamically analyzing the behavior of Windows executables, which runs binaries in an unmodified Windows environment, which leads to excellent emulation accuracy and makes it more difficult to detect by malicious code.
A Malware Instruction Set for Behavior-Based Analysis
TLDR
A new representation for monitored behavior of malicious software called Malware Instruction Set (MIST) is introduced, optimized for effective and efficient analysis of behavior using data mining and machine learning techniques.
The WEKA data mining software: an update
TLDR
This paper provides an introduction to the WEKA workbench, reviews the history of the project, and, in light of the recent 3.6 stable release, briefly discusses what has been added since the last stable version (Weka 3.4) released in 2003.
and K
  • Rieck, “A Malware Instruction Set for Behavior-Based Analysis”
  • 2009
and T
  • Holz, “Automatic Analysis of Malware Behavior using Machine Learning”
  • 2009
Mining specifications of malicious behavior
TLDR
The technique derives a specification of malicious behavior by comparing the execution behavior of a known malware against the execution behaviors of a set of benign programs, and indicates that the algorithm is effective in extracting malicious behaviors that can be used to detect malware variants.