Coordinated Malware Eradication And Remediation Project (CMERP) - Abdollah, Mohd Faizal; S.M.M Yassin, S.M.Warusia Mohamed; Mas’ud, Mohd Zaki; Selamat, Siti Rahayu; Yusof, Robiah; Ahmad, Rabiah; Shahrin @ Sahibuddin, Shahrin (UTeM, 2019)
The rate of malware spreading via the internet keep increasing and lead to a serious threat particularly to the host nowadays. A number of researchers keep on proposing various alternative framework consisting detection methods day by days in combating activities such as single classification and rule based approach. However, such detection method still lack in differentiate the malwares behaviours and cause the rate of falsely identified rate i.e. false positive and false negative increased. Therefore, integrated machine learning techniques comprises J48 and JRip are proposed as a solution in 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 identified. The result showed that the integrated classifier between J48 and JRip significantly improved the detection rate as compare to the single classifier.