The summer heat of cryptojacking season : Detecting cryptojacking using heatmap and fuzzy
Cryptojacking is a subset of cybercrime in which hackers use unauthorised devices (computers, smartphones, tablets, and even servers) to mine cryptocurrencies. Similar to many other forms of cybercrime, the objective of cryptojacking is achieve profit illegally. It is also designed to remain entirel...
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| Format: | Conference or Workshop Item |
| Language: | English English |
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Institute of Electrical and Electronics Engineers Inc.
2022
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| Online Access: | http://umpir.ump.edu.my/id/eprint/39105/1/The%20summer%20heat%20of%20cryptojacking%20season_Detecting%20cryptojacking.pdf http://umpir.ump.edu.my/id/eprint/39105/2/The%20summer%20heat%20of%20cryptojacking%20season_Detecting%20cryptojacking%20using%20heatmap%20and%20fuzzy_ABS.pdf |
| _version_ | 1825815236954816512 |
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| author | Ahmad Firdaus, Zainal Abidin Aldharhani, Ghassan Saleh Zahian, Ismail Mohd Faizal, Ab Razak |
| author_facet | Ahmad Firdaus, Zainal Abidin Aldharhani, Ghassan Saleh Zahian, Ismail Mohd Faizal, Ab Razak |
| author_sort | Ahmad Firdaus, Zainal Abidin |
| collection | UMP |
| description | Cryptojacking is a subset of cybercrime in which hackers use unauthorised devices (computers, smartphones, tablets, and even servers) to mine cryptocurrencies. Similar to many other forms of cybercrime, the objective of cryptojacking is achieve profit illegally. It is also designed to remain entirely concealed from the victim's view. However, its attacks continue to evolve and spread, and their number continues to rise. Therefore, it is essential to detect cryptojacking malware, as it poses a significant risk to users. However, in machine learning intelligence detection, an excessive number of insignificant features will diminish the detection's accuracy. For machine learning-based detection, it's important to find important features in a minimal amount of data. This study therefore proposes the Pearson correlation coefficient (PMCC), a measure of the linear relationship between all features. After that, this study employs the heatmap method to visualise the PMCC value as a colour version of heat. We utilised The Fuzzy Lattice Reasoning (FLR) classifier for classification algorithms in machine learning. This experiment utilised actual cryptojacking samples and achieved a 100 percent detection accuracy rate in simulation. |
| first_indexed | 2024-03-06T13:10:31Z |
| format | Conference or Workshop Item |
| id | UMPir39105 |
| institution | Universiti Malaysia Pahang |
| language | English English |
| last_indexed | 2024-03-06T13:10:31Z |
| publishDate | 2022 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| record_format | dspace |
| spelling | UMPir391052023-11-14T03:57:31Z http://umpir.ump.edu.my/id/eprint/39105/ The summer heat of cryptojacking season : Detecting cryptojacking using heatmap and fuzzy Ahmad Firdaus, Zainal Abidin Aldharhani, Ghassan Saleh Zahian, Ismail Mohd Faizal, Ab Razak QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Cryptojacking is a subset of cybercrime in which hackers use unauthorised devices (computers, smartphones, tablets, and even servers) to mine cryptocurrencies. Similar to many other forms of cybercrime, the objective of cryptojacking is achieve profit illegally. It is also designed to remain entirely concealed from the victim's view. However, its attacks continue to evolve and spread, and their number continues to rise. Therefore, it is essential to detect cryptojacking malware, as it poses a significant risk to users. However, in machine learning intelligence detection, an excessive number of insignificant features will diminish the detection's accuracy. For machine learning-based detection, it's important to find important features in a minimal amount of data. This study therefore proposes the Pearson correlation coefficient (PMCC), a measure of the linear relationship between all features. After that, this study employs the heatmap method to visualise the PMCC value as a colour version of heat. We utilised The Fuzzy Lattice Reasoning (FLR) classifier for classification algorithms in machine learning. This experiment utilised actual cryptojacking samples and achieved a 100 percent detection accuracy rate in simulation. Institute of Electrical and Electronics Engineers Inc. 2022 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/39105/1/The%20summer%20heat%20of%20cryptojacking%20season_Detecting%20cryptojacking.pdf pdf en http://umpir.ump.edu.my/id/eprint/39105/2/The%20summer%20heat%20of%20cryptojacking%20season_Detecting%20cryptojacking%20using%20heatmap%20and%20fuzzy_ABS.pdf Ahmad Firdaus, Zainal Abidin and Aldharhani, Ghassan Saleh and Zahian, Ismail and Mohd Faizal, Ab Razak (2022) The summer heat of cryptojacking season : Detecting cryptojacking using heatmap and fuzzy. In: International Conference on Cyber Resilience, ICCR 2022 , 6-7 October 2022 , Dubai. pp. 1-5. (185824). ISBN 978-166546122-1 (Published) https://doi.org/10.1109/ICCR56254.2022.9995891 |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Ahmad Firdaus, Zainal Abidin Aldharhani, Ghassan Saleh Zahian, Ismail Mohd Faizal, Ab Razak The summer heat of cryptojacking season : Detecting cryptojacking using heatmap and fuzzy |
| title | The summer heat of cryptojacking season : Detecting cryptojacking using heatmap and fuzzy |
| title_full | The summer heat of cryptojacking season : Detecting cryptojacking using heatmap and fuzzy |
| title_fullStr | The summer heat of cryptojacking season : Detecting cryptojacking using heatmap and fuzzy |
| title_full_unstemmed | The summer heat of cryptojacking season : Detecting cryptojacking using heatmap and fuzzy |
| title_short | The summer heat of cryptojacking season : Detecting cryptojacking using heatmap and fuzzy |
| title_sort | summer heat of cryptojacking season detecting cryptojacking using heatmap and fuzzy |
| topic | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) |
| url | http://umpir.ump.edu.my/id/eprint/39105/1/The%20summer%20heat%20of%20cryptojacking%20season_Detecting%20cryptojacking.pdf http://umpir.ump.edu.my/id/eprint/39105/2/The%20summer%20heat%20of%20cryptojacking%20season_Detecting%20cryptojacking%20using%20heatmap%20and%20fuzzy_ABS.pdf |
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