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

17 Citations (Scopus)

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 - Applications and Techniques in Cyber Security and Intelligence
Editors	Jemal Abawajy, Kim-Kwang Raymond Choo, Rafiqul Islam
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	International Conference on Applications and Techniques in Cyber Security and Intelligence: ATCSI 2017 - Zhejiang Business Technology Institute, Ningbo, China Duration: 16 Jun 2017 → 18 Jun 2017 http://aibd.us/ (Conference website) https://www.springer.com/us/book/9783319670706 (Conference proceedings)

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	580
ISSN (Print)	2194-5357

Conference

Conference	International Conference on Applications and Techniques in Cyber Security and Intelligence
Country	China
City	Ningbo
Period	16/06/17 → 18/06/17
Other	The 2017 International Conference on Applications and Techniques in Cyber Intelligence (ATCI), building on the previous successes in Guangzhou, China (2016), Dallas, USA (2015), Beijing, China (2014), and Sydney, Australia (2013), is proud to be in the 5th consecutive conference year. Previously, the event is known as the International Workshop on Applications and Techniques in Cyber Security (ATCS 2016), held in conjunction with the International Conference on Security and Privacy in Communication Networks (SecureComm). 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.
Internet address	http://aibd.us/ (Conference website) https://www.springer.com/us/book/9783319670706 (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 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., pp. 266-274, International Conference on Applications and Techniques in Cyber Security and Intelligence, 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 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

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AU - Chowdhury, Mozammel

AU - Rahman, Azizur

AU - Islam, Rafiqul

N1 - Chowdhury, M., Rahman, A. and Islam, R. (2017). Malware analysis and detection using data mining and machine learning classification, In Proceedings of the 2017 International Conference on Applications and Techniques in Cyber Intelligence (ATCI) on the Springer's "Advances in Intelligent Systems and Computing" book series, Ningbo (June 16 – 18)

PY - 2018/1/1

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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.

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KW - Data security

KW - Machine learning

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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. Springer-Verlag London Ltd. 2018. p. 266-274. (Advances in Intelligent Systems and Computing). https://doi.org/10.1007/978-3-319-67071-3_33