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Malware behavior profiling from unstructured data

Recently, the emergence of the new malware has caused a major threat especially in finance sector in which many of the online banking data was stolen by the adversaries. The malware threats information needs to be collected immediately after its outbreak. Early detection can save others from being t...

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Main Authors:	Yoong, Jien Chiam, Maarof, Mohd. Aizaini, Kassim, Mohamad Nizam, Zainal, Anazida
Format:	Conference or Workshop Item
Published:	2020
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://eprints.utm.my/id/eprint/92351/ http://dx.doi.org/10.1007/978-3-030-49345-5_14
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id	my.utm.92351
record_format	eprints
spelling	my.utm.923512021-09-28T07:38:56Z http://eprints.utm.my/id/eprint/92351/ Malware behavior profiling from unstructured data Yoong, Jien Chiam Maarof, Mohd. Aizaini Kassim, Mohamad Nizam Zainal, Anazida QA75 Electronic computers. Computer science Recently, the emergence of the new malware has caused a major threat especially in finance sector in which many of the online banking data was stolen by the adversaries. The malware threats information needs to be collected immediately after its outbreak. Early detection can save others from being the victims. Unfortunately, there is time delay to get the new malware information into the Malware Database such as ExploitDB. A pre-emptive way needs to be taken to gather the first-hand information of the new malware as a preventive measure. One of the methods is by extracting information from open source data such as online news by using Named Entity Recognition (NER). However, the existing NER system is incapable to extract the domain specific entities from the online news accurately. The aim of this paper is to extract the malware entities and its behaviour attributes using extended version of NER with HMM and CRF. A malware annotated corpus is produced in order to conduct the supervise learning for the machine learning approach of the name entity tagger. The results show CRF performs slightly better than HMM. Few experiments are performed in order to optimize the performance of CRF in terms of feature extraction. Finally, the malware behaviour information is visualized onto a dashboard by combining few statistical graphs using matplotlib. The purpose of visualizing the malware behaviour profile extracted from the online news is to help cyber security experts to better understand the malware behaviour. 2020 Conference or Workshop Item PeerReviewed Yoong, Jien Chiam and Maarof, Mohd. Aizaini and Kassim, Mohamad Nizam and Zainal, Anazida (2020) Malware behavior profiling from unstructured data. In: 11th International Conference on Soft Computing & Pattern Recognition (SOCPAR 2019), 13 – 15 December 2019, Hyderabad, India. http://dx.doi.org/10.1007/978-3-030-49345-5_14
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Yoong, Jien Chiam Maarof, Mohd. Aizaini Kassim, Mohamad Nizam Zainal, Anazida Malware behavior profiling from unstructured data
description	Recently, the emergence of the new malware has caused a major threat especially in finance sector in which many of the online banking data was stolen by the adversaries. The malware threats information needs to be collected immediately after its outbreak. Early detection can save others from being the victims. Unfortunately, there is time delay to get the new malware information into the Malware Database such as ExploitDB. A pre-emptive way needs to be taken to gather the first-hand information of the new malware as a preventive measure. One of the methods is by extracting information from open source data such as online news by using Named Entity Recognition (NER). However, the existing NER system is incapable to extract the domain specific entities from the online news accurately. The aim of this paper is to extract the malware entities and its behaviour attributes using extended version of NER with HMM and CRF. A malware annotated corpus is produced in order to conduct the supervise learning for the machine learning approach of the name entity tagger. The results show CRF performs slightly better than HMM. Few experiments are performed in order to optimize the performance of CRF in terms of feature extraction. Finally, the malware behaviour information is visualized onto a dashboard by combining few statistical graphs using matplotlib. The purpose of visualizing the malware behaviour profile extracted from the online news is to help cyber security experts to better understand the malware behaviour.
format	Conference or Workshop Item
author	Yoong, Jien Chiam Maarof, Mohd. Aizaini Kassim, Mohamad Nizam Zainal, Anazida
author_facet	Yoong, Jien Chiam Maarof, Mohd. Aizaini Kassim, Mohamad Nizam Zainal, Anazida
author_sort	Yoong, Jien Chiam
title	Malware behavior profiling from unstructured data
title_short	Malware behavior profiling from unstructured data
title_full	Malware behavior profiling from unstructured data
title_fullStr	Malware behavior profiling from unstructured data
title_full_unstemmed	Malware behavior profiling from unstructured data
title_sort	malware behavior profiling from unstructured data
publishDate	2020
url	http://eprints.utm.my/id/eprint/92351/ http://dx.doi.org/10.1007/978-3-030-49345-5_14
_version_	1712285082152075264
score	13.211869

Malware behavior profiling from unstructured data

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