Search Results - (( program _ malware algorithm ) OR ( java implication based algorithm ))

BFEDroid: A Feature Selection Technique to Detect Malware in Android Apps Using Machine Learning by Chimeleze C., Jamil N., Ismail R., Lam K.-Y., Teh J.S., Samual J., Akachukwu Okeke C.

An Analysis Of System Calls Using J48 And JRip For Malware Detection by Abdollah, Mohd Faizal, Abdullah, Raihana Syahirah, S. M. M Yassin, S. M. Warusia Mohamed, Selamat, Siti Rahayu, Mohd Saudi, Nur Hidayah

“…The evolution of malware possesses serious threat ever since the concept of malware took root in the technology industry.The malicious software which is specifically designed to disrupt,damage,or gain authorized access to a computer system has made a lot of researchers try to develop a new and better technique to detect malware but it is still inaccurate in distinguishing the malware activities and ineffective.To solve the problem,this paper proposed the integrated machine learning methods consist of J48 and JRip in detecting the malware accurately.The integrated classifier algorithm applied to examine,classify and generate rules of the pattern and program behaviour of system call information.The outcome then revealed the integrated classifier of J48 and JRip outperforming the other classifier with 100% detection of attack rate. …”
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An Analysis Of System Calls Using J48 And JRip For Malware Detection by Abdollah, Mohd Faizal, Abdullah, Raihana Syahirah, S.M.M Yassin, S.M. Warusia Mohamed, Selamat, Siti Rahayu, Mohd Saudi, Nur Hidayah

“…To solve the problem, this paper proposed the integrated machine learning methods consist of J48 and JRip in detecting the malware accurately. The integrated classifier algorithm applied to examine, classify and generate rules of the pattern and program behaviour of system call information. …”
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Empirical study on intelligent android malware detection based on supervised machine learning by Abdullah, Talal A.A., Ali, Waleed, Abdulghafor, Rawad Abdulkhaleq Abdulmolla

“…In response, specific tools and anti-virus programs used conventional signature-based methods in order to detect such Android malware applications. …”
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An ensemble deep learning classifier stacked with fuzzy ARTMAP for malware detection by Shing, Chiang Tan, Mohammed Al-Andoli, Mohammed Nasser, Kok, Swee Lim, Pey, Yun Goh, Chee, Peng Lim

“…However, legacy anti-malware methods are confronted with millions of potential malicious programs. …”
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Android malware detection using permission based static analysis by Mohd Ariffin, Noor Afiza, Casinto, Hanna Pungo

“…There have been many studies on permission-based Android malware detection. In this study, a permission-based Android malware system is analyzed. …”
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Android malware detection using permission based static analysis by Mohd Ariffin, Noor Afiza, Casinto, Hanna Pungo

“…With this development, mobile phones are supporting many programs, and everyone takes advantage of them. Nevertheless, malware applications are increasing more and more so that people can come across lots of problems. …”
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Coordinated Malware Eradication And Remediation Project (CMERP) by 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

“…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. …”
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Scrutinized System Calls Information Using J48 And Jrip For Malware Behaviour Detection by Abdollah, Mohd Faizal, S. M. M Yassin, S. M. Warusia Mohamed, Mohd Saudi, Nur Hidayah

“…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. …”
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A Preemptive behaviour-based malware detection through analysis of API calls sequence inspired by Human Immune System by Mohd Fadzli Marhusin, Lokan C.J.

Botnet detection using automated script / Norfathin Rosli by Rosli, Norfathin

“…Nowadays, as we know the virus or malware spread can be so fast and usually hard to detect. …”
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The implications for ahybrid detection technique against malicious sqlattacks on web applications by Bahjat Arif, Sarajaldeen Akram, Wani, Sharyar

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Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method by Ashraf Saleh Mohammad Alomoush