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Crytojacking classification based on machine learning algorithm

The rise of cryptocurrency has resulted in a number of concerns. A new threat known as cryptojacking" has entered the picture where cryptojacking malware is the trend for future cyber criminals, who infect computers, install cryptocurrency miners, and use stolen information from victim database...

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Main Authors: Wan Mansor, Wan Nur Aaisyah, Ahmad, Azuan, Zainudin, Wan Shafiuddin, Mohd. Saudi, Madihah, Kama, Mohd. Nazri
Format: Conference or Workshop Item
Language:English
Published: 2020
Subjects:
Q Science (General)
T Technology (General)
Online Access:http://eprints.utm.my/id/eprint/89932/1/MohdNazriKama2020_CrytojackingClassificationbasedonMachineLearning.pdf
http://eprints.utm.my/id/eprint/89932/
http://dx.doi.org/10.1145/3390525.3390537
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spelling my.utm.899322021-03-31T06:31:48Z http://eprints.utm.my/id/eprint/89932/ Crytojacking classification based on machine learning algorithm Wan Mansor, Wan Nur Aaisyah Ahmad, Azuan Zainudin, Wan Shafiuddin Mohd. Saudi, Madihah Kama, Mohd. Nazri Q Science (General) T Technology (General) The rise of cryptocurrency has resulted in a number of concerns. A new threat known as cryptojacking" has entered the picture where cryptojacking malware is the trend for future cyber criminals, who infect computers, install cryptocurrency miners, and use stolen information from victim databases to set up wallets for illicit funds transfers. Worst by 2020, researchers estimate there will be 30 billion of IoT devices in the world. Majority of the devices are highly vulnerable to simple attacks based on weak passwords and unpatched vulnerabilities and poorly monitored. Thus it is the best projection that IoT become a perfect target for cryptojacking malwares. There are lacks of study that provide in depth analysis on cryptojacking malware especially in the classification model. As IoT devices requires small processing capability, a lightweight model are required for the cryptojacking malware detection algorithm to maintain its accuracy without sacrificing the performance of other process. As a solution, we propose a new lightweight cryptojacking classifier model based on instruction simplification and machine learning technique that can detect the cryptojacking classification algorithm. This research aims to study the features of existing cryptojacking classification algorithm, to enhanced existing algorithm and to evaluate the enhanced algorithm for cryptojacking malware classification. The output of this research will be significant used in detecting cryptojacking malware attacks that benefits multiple industries including cyber security contractors, oil and gas, water, power and energy industries which align with the National Cyber Security Policy (NCSP) which address the risks to the Critical National Information Infrastructure (CNII). 2020-04-15 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/89932/1/MohdNazriKama2020_CrytojackingClassificationbasedonMachineLearning.pdf Wan Mansor, Wan Nur Aaisyah and Ahmad, Azuan and Zainudin, Wan Shafiuddin and Mohd. Saudi, Madihah and Kama, Mohd. Nazri (2020) Crytojacking classification based on machine learning algorithm. In: 8th International Conference on Communications and Broadband Networking, ICCBN 2020 and its Workshop on 2020 3rd International Conference on Communication Engineering and Technology, ICCET 2020, 15 April 2020 - 18 April 2020, Auckland, New Zealand. http://dx.doi.org/10.1145/3390525.3390537
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/
language English
topic Q Science (General)
T Technology (General)
spellingShingle Q Science (General)
T Technology (General)
Wan Mansor, Wan Nur Aaisyah
Ahmad, Azuan
Zainudin, Wan Shafiuddin
Mohd. Saudi, Madihah
Kama, Mohd. Nazri
Crytojacking classification based on machine learning algorithm
description The rise of cryptocurrency has resulted in a number of concerns. A new threat known as cryptojacking" has entered the picture where cryptojacking malware is the trend for future cyber criminals, who infect computers, install cryptocurrency miners, and use stolen information from victim databases to set up wallets for illicit funds transfers. Worst by 2020, researchers estimate there will be 30 billion of IoT devices in the world. Majority of the devices are highly vulnerable to simple attacks based on weak passwords and unpatched vulnerabilities and poorly monitored. Thus it is the best projection that IoT become a perfect target for cryptojacking malwares. There are lacks of study that provide in depth analysis on cryptojacking malware especially in the classification model. As IoT devices requires small processing capability, a lightweight model are required for the cryptojacking malware detection algorithm to maintain its accuracy without sacrificing the performance of other process. As a solution, we propose a new lightweight cryptojacking classifier model based on instruction simplification and machine learning technique that can detect the cryptojacking classification algorithm. This research aims to study the features of existing cryptojacking classification algorithm, to enhanced existing algorithm and to evaluate the enhanced algorithm for cryptojacking malware classification. The output of this research will be significant used in detecting cryptojacking malware attacks that benefits multiple industries including cyber security contractors, oil and gas, water, power and energy industries which align with the National Cyber Security Policy (NCSP) which address the risks to the Critical National Information Infrastructure (CNII).
format Conference or Workshop Item
author Wan Mansor, Wan Nur Aaisyah
Ahmad, Azuan
Zainudin, Wan Shafiuddin
Mohd. Saudi, Madihah
Kama, Mohd. Nazri
author_facet Wan Mansor, Wan Nur Aaisyah
Ahmad, Azuan
Zainudin, Wan Shafiuddin
Mohd. Saudi, Madihah
Kama, Mohd. Nazri
author_sort Wan Mansor, Wan Nur Aaisyah
title Crytojacking classification based on machine learning algorithm
title_short Crytojacking classification based on machine learning algorithm
title_full Crytojacking classification based on machine learning algorithm
title_fullStr Crytojacking classification based on machine learning algorithm
title_full_unstemmed Crytojacking classification based on machine learning algorithm
title_sort crytojacking classification based on machine learning algorithm
publishDate 2020
url http://eprints.utm.my/id/eprint/89932/1/MohdNazriKama2020_CrytojackingClassificationbasedonMachineLearning.pdf
http://eprints.utm.my/id/eprint/89932/
http://dx.doi.org/10.1145/3390525.3390537
_version_ 1696976237361102848
score 13.211869

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