Scrutinized System Calls Information Using J48 And Jrip For Malware Behaviour Detection
Malware is considered as one of most emerging threats due to Cybercriminals work diligently to make most of the part of the users’ network of computers as their target. A number of researchers keep on proposing the various alternative framework consisting detection methods day by days in combating a...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | en |
| Published: |
Taylor's University
2019
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| Subjects: | |
| Online Access: | http://eprints.utem.edu.my/id/eprint/24039/2/14_1_21.pdf http://eprints.utem.edu.my/id/eprint/24039/ http://jestec.taylors.edu.my/Vol%2014%20issue%201%20February%202019/14_1_21.pdf |
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| Summary: | Malware is considered as one of most emerging threats due to Cybercriminals work diligently to make most of the part of the users’ network of computers as their target. A number of researchers keep on proposing the various alternative framework consisting detection methods day by days in combating activities such as single classification and the rule-based approach. However, such detection method still lacks in differentiate the malware behaviours and cause the rate of falsely identified rate, i.e., false positive and false negative increased. Therefore, integrated machine learning techniques comprise J48 and Jrip are proposed as a solution to distinguish malware behaviour more accurately. This integrated classifier algorithm applied to analyse, classify and generate rules of the pattern and program behaviour of system call information in which, the legal and illegal behaviours could identify. The result showed that the integrated classifier between J48 and Jrip significantly improved the detection rate as compared to the single classifier. |
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