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Crytojacking classification based on machine learning algorithm

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.


Official URL: http://dx.doi.org/10.1145/3390525.3390537


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).

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:classification, cryptojacking, cryptomining
Subjects:Q Science > Q Science (General)
T Technology > T Technology (General)
Divisions:Razak School of Engineering and Advanced Technology
ID Code:89932
Deposited By: Yanti Mohd Shah
Deposited On:31 Mar 2021 14:31
Last Modified:31 Mar 2021 14:31

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