Evaluation of Supervised and Unsupervised Machine Learning Classifiers for Mac OS Malware Detection

2022 ◽  
pp. 159-175
Author(s):  
Dilip Sahoo ◽  
Yash Dhawan
Keyword(s):  
Machine Learning ◽  
Malware Detection ◽  
Unsupervised Machine Learning ◽  
Machine Learning Classifiers ◽  
Learning Classifiers

scholarly journals Glaucomatous Patterns in Frequency Doubling Technology (FDT) Perimetry Data Identified by Unsupervised Machine Learning Classifiers

PLoS ONE ◽  
2014 ◽  
Vol 9 (1) ◽  
pp. e85941 ◽  
Author(s):  
Christopher Bowd ◽  
Robert N. Weinreb ◽  
Madhusudhanan Balasubramanian ◽  
Intae Lee ◽  
Giljin Jang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Frequency Doubling ◽  
Unsupervised Machine Learning ◽  
Machine Learning Classifiers ◽  
Frequency Doubling Technology ◽  
Learning Classifiers

scholarly journals Linear SVM-Based Android Malware Detection for Reliable IoT Services

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Hyo-Sik Ham ◽  
Hwan-Hee Kim ◽  
Myung-Sup Kim ◽  
Mi-Jung Choi
Keyword(s):  
Machine Learning ◽  
Mobile Devices ◽  
Malware Detection ◽  
Information Leakage ◽  
Support Vector ◽  
Android Malware ◽  
Machine Learning Classifiers ◽  
Android Malware Detection ◽  
Learning Classifiers ◽  
Linear Svm

Current many Internet of Things (IoT) services are monitored and controlled through smartphone applications. By combining IoT with smartphones, many convenient IoT services have been provided to users. However, there are adverse underlying effects in such services including invasion of privacy and information leakage. In most cases, mobile devices have become cluttered with important personal user information as various services and contents are provided through them. Accordingly, attackers are expanding the scope of their attacks beyond the existing PC and Internet environment into mobile devices. In this paper, we apply a linear support vector machine (SVM) to detect Android malware and compare the malware detection performance of SVM with that of other machine learning classifiers. Through experimental validation, we show that the SVM outperforms other machine learning classifiers.

Evaluation of machine learning classifiers for mobile malware detection

2014 ◽  
Vol 20 (1) ◽  
pp. 343-357 ◽  
Author(s):  
Fairuz Amalina Narudin ◽  
Ali Feizollah ◽  
Nor Badrul Anuar ◽  
Abdullah Gani
Keyword(s):  
Machine Learning ◽  
Malware Detection ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Mobile Malware ◽  
Mobile Malware Detection
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