Malware analysis and detection using data mining and machine learning classification

Mozammel Chowdhury; Azizur Rahman; Rafiqul Islam

doi:10.1007/978-3-319-67071-3_33

Malware analysis and detection using data mining and machine learning classification

Mozammel Chowdhury, Azizur Rahman, Rafiqul Islam

Research output: Book chapter/Published conference paper › Conference paper › peer-review

35 Citations (Scopus)

410 Downloads (Pure)

Abstract

Exfiltration of sensitive data by malicious software or malware is a serious cyber threat around the world that has catastrophic effect on businesses, research organizations, national intelligence, as well as individuals. Thousands of cyber criminals attempt every day to attack computer systems by employing malicious software with an intention to breach crucial data, damage or manipulate data, or to make illegal financial transfers. Protection of this data is therefore, a critical concern in the research community. This manuscript aims to propose a comprehensive framework to classify and detect malicious software to protect sensitive data against malicious threats using data mining and machine learning classification techniques. In this work, we employ a robust and efficient approach for malware classification and detection by analyzing both signature-based and anomaly-based features. Experimental results confirm the superiority of the proposed approach over other similar methods.

Original language	English
Title of host publication	International Conference on Applications and Techniques in Cyber Security and Intelligence
Subtitle of host publication	Applications and Techniques in Cyber Security and Intelligence
Editors	Jemal Abawajy, Kim-Kwang Raymond Choo, Rafiqul Islam
Place of Publication	Cham, Switzerland
Publisher	Springer-Verlag London Ltd.
Pages	266-274
Number of pages	9
Volume	580
ISBN (Electronic)	9783319670713
ISBN (Print)	9783319670706
DOIs	https://doi.org/10.1007/978-3-319-67071-3_33
Publication status	Published - 01 Jan 2018
Event	2017 International Conference on Applications and Techniques in Cyber Intelligence (ATCI) - Zhejiang Business Technology Institute, Ningbo, China Duration: 16 Jun 2017 → 18 Jun 2017 https://web.archive.org/web/20170228045429/http://aibd.us/ (Conference site) https://link.springer.com/book/10.1007/978-3-319-67071-3#bibliographic-information (Conference proceedings)

Publication series

Name	Advances in Intelligent Systems and Computing
Publisher	Edizioni della Normale Cham
Volume	580
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	2017 International Conference on Applications and Techniques in Cyber Intelligence (ATCI)
Country/Territory	China
City	Ningbo
Period	16/06/17 → 18/06/17
Other	The 2017 International Conference on Applications and Techniques in Cyber Security and Intelligence focuses on all aspects on techniques and applications in cyber and electronics security and intelligence research. The purpose of ATCI 2017 is to provide a forum for presentation and discussion of innovative ideas, cutting edge research results, and novel techniques, methods and applications on all aspects of cyber and electronics security and intelligence. As in previous years, the conference intends to attract the cyber security and cyber intelligence (e.g. big data) research community from around the world. Each submission will be reviewed by at least three independent experts, and the acceptance rate will be below 30%. We will invite three internationally renowned keynote speakers for this conference.
Internet address	https://web.archive.org/web/20170228045429/http://aibd.us/ (Conference site) https://link.springer.com/book/10.1007/978-3-319-67071-3#bibliographic-information (Conference proceedings)

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Chowdhury, M., Rahman, A., & Islam, R. (2018). Malware analysis and detection using data mining and machine learning classification. In J. Abawajy, K-K. R. Choo, & R. Islam (Eds.), International Conference on Applications and Techniques in Cyber Security and Intelligence: Applications and Techniques in Cyber Security and Intelligence (Vol. 580, pp. 266-274). (Advances in Intelligent Systems and Computing; Vol. 580). Springer-Verlag London Ltd.. https://doi.org/10.1007/978-3-319-67071-3_33

Chowdhury, Mozammel ; Rahman, Azizur ; Islam, Rafiqul. / Malware analysis and detection using data mining and machine learning classification. International Conference on Applications and Techniques in Cyber Security and Intelligence: Applications and Techniques in Cyber Security and Intelligence. editor / Jemal Abawajy ; Kim-Kwang Raymond Choo ; Rafiqul Islam. Vol. 580 Cham, Switzerland : Springer-Verlag London Ltd., 2018. pp. 266-274 (Advances in Intelligent Systems and Computing).

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abstract = "Exfiltration of sensitive data by malicious software or malware is a serious cyber threat around the world that has catastrophic effect on businesses, research organizations, national intelligence, as well as individuals. Thousands of cyber criminals attempt every day to attack computer systems by employing malicious software with an intention to breach crucial data, damage or manipulate data, or to make illegal financial transfers. Protection of this data is therefore, a critical concern in the research community. This manuscript aims to propose a comprehensive framework to classify and detect malicious software to protect sensitive data against malicious threats using data mining and machine learning classification techniques. In this work, we employ a robust and efficient approach for malware classification and detection by analyzing both signature-based and anomaly-based features. Experimental results confirm the superiority of the proposed approach over other similar methods.",

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Chowdhury, M, Rahman, A & Islam, R 2018, Malware analysis and detection using data mining and machine learning classification. in J Abawajy, K-KR Choo & R Islam (eds), International Conference on Applications and Techniques in Cyber Security and Intelligence: Applications and Techniques in Cyber Security and Intelligence. vol. 580, Advances in Intelligent Systems and Computing, vol. 580, Springer-Verlag London Ltd., Cham, Switzerland, pp. 266-274, 2017 International Conference on Applications and Techniques in Cyber Intelligence (ATCI), Ningbo, China, 16/06/17. https://doi.org/10.1007/978-3-319-67071-3_33

Malware analysis and detection using data mining and machine learning classification. / Chowdhury, Mozammel; Rahman, Azizur ; Islam, Rafiqul.

International Conference on Applications and Techniques in Cyber Security and Intelligence: Applications and Techniques in Cyber Security and Intelligence. ed. / Jemal Abawajy; Kim-Kwang Raymond Choo; Rafiqul Islam. Vol. 580 Cham, Switzerland : Springer-Verlag London Ltd., 2018. p. 266-274 (Advances in Intelligent Systems and Computing; Vol. 580).

Research output: Book chapter/Published conference paper › Conference paper › peer-review

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T1 - Malware analysis and detection using data mining and machine learning classification

AU - Chowdhury, Mozammel

AU - Rahman, Azizur

AU - Islam, Rafiqul

N1 - Includes bibliographical references.

PY - 2018/1/1

Y1 - 2018/1/1

N2 - Exfiltration of sensitive data by malicious software or malware is a serious cyber threat around the world that has catastrophic effect on businesses, research organizations, national intelligence, as well as individuals. Thousands of cyber criminals attempt every day to attack computer systems by employing malicious software with an intention to breach crucial data, damage or manipulate data, or to make illegal financial transfers. Protection of this data is therefore, a critical concern in the research community. This manuscript aims to propose a comprehensive framework to classify and detect malicious software to protect sensitive data against malicious threats using data mining and machine learning classification techniques. In this work, we employ a robust and efficient approach for malware classification and detection by analyzing both signature-based and anomaly-based features. Experimental results confirm the superiority of the proposed approach over other similar methods.

AB - Exfiltration of sensitive data by malicious software or malware is a serious cyber threat around the world that has catastrophic effect on businesses, research organizations, national intelligence, as well as individuals. Thousands of cyber criminals attempt every day to attack computer systems by employing malicious software with an intention to breach crucial data, damage or manipulate data, or to make illegal financial transfers. Protection of this data is therefore, a critical concern in the research community. This manuscript aims to propose a comprehensive framework to classify and detect malicious software to protect sensitive data against malicious threats using data mining and machine learning classification techniques. In this work, we employ a robust and efficient approach for malware classification and detection by analyzing both signature-based and anomaly-based features. Experimental results confirm the superiority of the proposed approach over other similar methods.

KW - Classification

KW - Cyber threat

KW - Data security

KW - Machine learning

KW - Malware

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VL - 580

T3 - Advances in Intelligent Systems and Computing

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EP - 274

BT - International Conference on Applications and Techniques in Cyber Security and Intelligence

A2 - Abawajy, Jemal

A2 - Choo, Kim-Kwang Raymond

A2 - Islam, Rafiqul

PB - Springer-Verlag London Ltd.

CY - Cham, Switzerland

T2 - 2017 International Conference on Applications and Techniques in Cyber Intelligence (ATCI)

Y2 - 16 June 2017 through 18 June 2017

ER -

Chowdhury M, Rahman A , Islam R. Malware analysis and detection using data mining and machine learning classification. In Abawajy J, Choo K-KR, Islam R, editors, International Conference on Applications and Techniques in Cyber Security and Intelligence: Applications and Techniques in Cyber Security and Intelligence. Vol. 580. Cham, Switzerland: Springer-Verlag London Ltd. 2018. p. 266-274. (Advances in Intelligent Systems and Computing). Epub 2017 Oct 21. doi: 10.1007/978-3-319-67071-3_33

Malware analysis and detection using data mining and machine learning classification

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