Fine-Grain Feature Extraction from Malware's Scan Behavior Based on Spectrum Analysis

Masashi ETO; Kotaro SONODA; Daisuke INOUE; Katsunari YOSHIOKA; Koji NAKAO

doi:10.1587/transinf.E93.D.1106

Fine-Grain Feature Extraction from Malware's Scan Behavior Based on Spectrum Analysis

Masashi ETO, Kotaro SONODA, Daisuke INOUE, Katsunari YOSHIOKA, Koji NAKAO

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Network monitoring systems that detect and analyze malicious activities as well as respond against them, are becoming increasingly important. As malwares, such as worms, viruses, and bots, can inflict significant damages on both infrastructure and end user, technologies for identifying such propagating malwares are in great demand. In the large-scale darknet monitoring operation, we can see that malwares have various kinds of scan patterns that involves choosing destination IP addresses. Since many of those oscillations seemed to have a natural periodicity, as if they were signal waveforms, we considered to apply a spectrum analysis methodology so as to extract a feature of malware. With a focus on such scan patterns, this paper proposes a novel concept of malware feature extraction and a distinct analysis method named "SPectrum Analysis for Distinction and Extraction of malware features (SPADE)". Through several evaluations using real scan traffic, we show that SPADE has the significant advantage of recognizing the similarities and dissimilarities between the same and different types of malwares.

Publication: IEICE TRANSACTIONS on Information Vol.E93-D No.5 pp.1106-1116

Publication Date: 2010/05/01

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Online ISSN: 1745-1361

DOI: 10.1587/transinf.E93.D.1106

Type of Manuscript: Special Section PAPER (Special Section on Information and Communication System Security)

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Masashi ETO, Kotaro SONODA, Daisuke INOUE, Katsunari YOSHIOKA, Koji NAKAO, "Fine-Grain Feature Extraction from Malware's Scan Behavior Based on Spectrum Analysis" in IEICE TRANSACTIONS on Information, vol. E93-D, no. 5, pp. 1106-1116, May 2010, doi: 10.1587/transinf.E93.D.1106.
Abstract: Network monitoring systems that detect and analyze malicious activities as well as respond against them, are becoming increasingly important. As malwares, such as worms, viruses, and bots, can inflict significant damages on both infrastructure and end user, technologies for identifying such propagating malwares are in great demand. In the large-scale darknet monitoring operation, we can see that malwares have various kinds of scan patterns that involves choosing destination IP addresses. Since many of those oscillations seemed to have a natural periodicity, as if they were signal waveforms, we considered to apply a spectrum analysis methodology so as to extract a feature of malware. With a focus on such scan patterns, this paper proposes a novel concept of malware feature extraction and a distinct analysis method named "SPectrum Analysis for Distinction and Extraction of malware features (SPADE)". Through several evaluations using real scan traffic, we show that SPADE has the significant advantage of recognizing the similarities and dissimilarities between the same and different types of malwares.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E93.D.1106/_p

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@ARTICLE{e93-d_5_1106,
author={Masashi ETO, Kotaro SONODA, Daisuke INOUE, Katsunari YOSHIOKA, Koji NAKAO, },
journal={IEICE TRANSACTIONS on Information},
title={Fine-Grain Feature Extraction from Malware's Scan Behavior Based on Spectrum Analysis},
year={2010},
volume={E93-D},
number={5},
pages={1106-1116},
abstract={Network monitoring systems that detect and analyze malicious activities as well as respond against them, are becoming increasingly important. As malwares, such as worms, viruses, and bots, can inflict significant damages on both infrastructure and end user, technologies for identifying such propagating malwares are in great demand. In the large-scale darknet monitoring operation, we can see that malwares have various kinds of scan patterns that involves choosing destination IP addresses. Since many of those oscillations seemed to have a natural periodicity, as if they were signal waveforms, we considered to apply a spectrum analysis methodology so as to extract a feature of malware. With a focus on such scan patterns, this paper proposes a novel concept of malware feature extraction and a distinct analysis method named "SPectrum Analysis for Distinction and Extraction of malware features (SPADE)". Through several evaluations using real scan traffic, we show that SPADE has the significant advantage of recognizing the similarities and dissimilarities between the same and different types of malwares.},
keywords={},
doi={10.1587/transinf.E93.D.1106},
ISSN={1745-1361},
month={May},}

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TY - JOUR
TI - Fine-Grain Feature Extraction from Malware's Scan Behavior Based on Spectrum Analysis
T2 - IEICE TRANSACTIONS on Information
SP - 1106
EP - 1116
AU - Masashi ETO
AU - Kotaro SONODA
AU - Daisuke INOUE
AU - Katsunari YOSHIOKA
AU - Koji NAKAO
PY - 2010
DO - 10.1587/transinf.E93.D.1106
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E93-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2010
AB - Network monitoring systems that detect and analyze malicious activities as well as respond against them, are becoming increasingly important. As malwares, such as worms, viruses, and bots, can inflict significant damages on both infrastructure and end user, technologies for identifying such propagating malwares are in great demand. In the large-scale darknet monitoring operation, we can see that malwares have various kinds of scan patterns that involves choosing destination IP addresses. Since many of those oscillations seemed to have a natural periodicity, as if they were signal waveforms, we considered to apply a spectrum analysis methodology so as to extract a feature of malware. With a focus on such scan patterns, this paper proposes a novel concept of malware feature extraction and a distinct analysis method named "SPectrum Analysis for Distinction and Extraction of malware features (SPADE)". Through several evaluations using real scan traffic, we show that SPADE has the significant advantage of recognizing the similarities and dissimilarities between the same and different types of malwares.
ER -