Protecting data from malware threats using machine learning technique

Mohammad Mozammel Hoque Chowdhury; Azizur Rahman; MD Rafiqul Islam

doi:10.1109/ICIEA.2017.8283111

Protecting data from malware threats using machine learning technique

Mohammad Mozammel Hoque Chowdhury, Azizur Rahman, MD Rafiqul Islam

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

23 Citations (Scopus)

Abstract

Cyber attacks against sensitive data have become as serious threats all over the world due to the rising applications of computer and information technology. New malware or malicious programs are released everyday by cyber criminals through the Internet in an attempt to steal or destroy important data. Hence, research on protecting data receives tremendous interest in the cyber community. In order to cope with new variants of malicious software, machine
learning techniques can be used for accurate classification and detection. This paper proposes an efficient scheme for malware detection for protecting sensitive data from malicious threats using data mining and machine learning techniques. Experimental results shows that the proposed approach gives
better performance compared to other similar methods.

Original language	English
Title of host publication	Proceedings of the 2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA)
Place of Publication	United States
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	1691-1694
Number of pages	4
ISBN (Electronic)	9781538621035
DOIs	https://doi.org/10.1109/ICIEA.2017.8283111
Publication status	Published - 08 Feb 2018
Event	2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA) - Royal Angkor Resort & Spa, Siem Riep, Cambodia Duration: 18 Jun 2017 → 20 Jun 2017 http://www.ieeeiciea.org/2017/?utm_source=researchbib (Conference website) https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8272105 (Conference proceedings)

Conference

Conference	2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA)
Country/Territory	Cambodia
City	Siem Riep
Period	18/06/17 → 20/06/17
Other	The 12th IEEE Conference on Industrial Electronics and Applications (ICIEA 2017) will be held during 18 - 20 June 2017, in Siem Reap, Cambodia. The Conference is organized by IEEE Industrial Electronics Chapter of Singapore and IEEE Singapore Section. IEEE Industrial Electronics Society is the financial and technical sponsor The proceedings of past ICIEA have all been included in the IEEE Xplore database and indexed by EI Compendex and ISI Proceedings. ICIEA 2017 marks the 12th Anniversary of the ICIEA conferences. As a premier conference, ICIEA provides an excellent forum for scientists, researchers, engineers and industrial practitioners throughout the world to present and discuss the latest technology advancement as well as future directions and trends in Industrial Electronics.
Internet address	http://www.ieeeiciea.org/2017/?utm_source=researchbib (Conference website) https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8272105 (Conference proceedings)

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10.1109/ICIEA.2017.8283111

http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8283111

Cite this

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title = "Protecting data from malware threats using machine learning technique",

abstract = "Cyber attacks against sensitive data have become as serious threats all over the world due to the rising applications of computer and information technology. New malware or malicious programs are released everyday by cyber criminals through the Internet in an attempt to steal or destroy important data. Hence, research on protecting data receives tremendous interest in the cyber community. In order to cope with new variants of malicious software, machinelearning techniques can be used for accurate classification and detection. This paper proposes an efficient scheme for malware detection for protecting sensitive data from malicious threats using data mining and machine learning techniques. Experimental results shows that the proposed approach givesbetter performance compared to other similar methods.",

keywords = "Malware, Feature extraction, Support vector machines, Training, Classification algorithms, Data mining",

author = "Chowdhury, {Mohammad Mozammel Hoque} and Azizur Rahman and Islam, {MD Rafiqul}",

year = "2018",

month = feb,

day = "8",

doi = "10.1109/ICIEA.2017.8283111",

language = "English",

pages = "1691--1694",

booktitle = "Proceedings of the 2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA)",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States",

note = "2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA) ; Conference date: 18-06-2017 Through 20-06-2017",

url = "http://www.ieeeiciea.org/2017/?utm_source=researchbib, https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8272105",

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Chowdhury, MMH, Rahman, A & Islam, MDR 2018, Protecting data from malware threats using machine learning technique. in Proceedings of the 2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA). IEEE, Institute of Electrical and Electronics Engineers, United States, pp. 1691-1694, 2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA), Siem Riep, Cambodia, 18/06/17. https://doi.org/10.1109/ICIEA.2017.8283111

Protecting data from malware threats using machine learning technique. / Chowdhury, Mohammad Mozammel Hoque; Rahman, Azizur ; Islam, MD Rafiqul.
Proceedings of the 2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA). United States: IEEE, Institute of Electrical and Electronics Engineers, 2018. p. 1691-1694.

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

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T1 - Protecting data from malware threats using machine learning technique

AU - Chowdhury, Mohammad Mozammel Hoque

AU - Rahman, Azizur

AU - Islam, MD Rafiqul

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N2 - Cyber attacks against sensitive data have become as serious threats all over the world due to the rising applications of computer and information technology. New malware or malicious programs are released everyday by cyber criminals through the Internet in an attempt to steal or destroy important data. Hence, research on protecting data receives tremendous interest in the cyber community. In order to cope with new variants of malicious software, machinelearning techniques can be used for accurate classification and detection. This paper proposes an efficient scheme for malware detection for protecting sensitive data from malicious threats using data mining and machine learning techniques. Experimental results shows that the proposed approach givesbetter performance compared to other similar methods.

AB - Cyber attacks against sensitive data have become as serious threats all over the world due to the rising applications of computer and information technology. New malware or malicious programs are released everyday by cyber criminals through the Internet in an attempt to steal or destroy important data. Hence, research on protecting data receives tremendous interest in the cyber community. In order to cope with new variants of malicious software, machinelearning techniques can be used for accurate classification and detection. This paper proposes an efficient scheme for malware detection for protecting sensitive data from malicious threats using data mining and machine learning techniques. Experimental results shows that the proposed approach givesbetter performance compared to other similar methods.

KW - Malware

KW - Feature extraction

KW - Support vector machines

KW - Training

KW - Classification algorithms

KW - Data mining

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M3 - Conference paper

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BT - Proceedings of the 2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA)

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T2 - 2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA)

Y2 - 18 June 2017 through 20 June 2017

ER -

Protecting data from malware threats using machine learning technique

Abstract

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