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: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2020
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| Subjects: | |
| 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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| Summary: | 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). |
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