scholarly journals Android Malware Classification Using K-Means Clustering Algorithm

2017 ◽  
Vol 226 ◽  
pp. 012105 ◽  
Author(s):  
Isredza Rahmi A Hamid ◽  
Nur Syafiqah Khalid ◽  
Nurul Azma Abdullah ◽  
Nurul Hidayah Ab Rahman ◽  
Chuah Chai Wen
Keyword(s):  
Clustering Algorithm ◽  
Android Malware ◽  
Malware Classification

scholarly journals MobiSentry: Towards Easy and Effective Detection of Android Malware on Smartphones

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Bingfei Ren ◽  
Chuanchang Liu ◽  
Bo Cheng ◽  
Jie Guo ◽  
Junliang Chen
Keyword(s):  
State Of The Art ◽  
Classification Algorithms ◽  
Defense System ◽  
Android Malware ◽  
Malware Classification ◽  
Android Os ◽  
Comprehensive Performance ◽  
Supervised Classifiers ◽  
Expert Analysis ◽  
Time Overhead

Android platform is increasingly targeted by attackers due to its popularity and openness. Traditional defenses to malware are largely reliant on expert analysis to design the discriminative features manually, which are easy to bypass with the use of sophisticated detection avoidance techniques. Therefore, more effective and easy-to-use approaches for detection of Android malware are in demand. In this paper, we present MobiSentry, a novel lightweight defense system for malware classification and categorization on smartphones. Besides conventional static features such as permissions and API calls, MobiSentry also employs the N-gram features of operation codes (n-opcode). We present two comprehensive performance comparisons among several state-of-the-art classification algorithms with multiple evaluation metrics: (1) malware detection on 184,486 benign applications and 21,306 malware samples, and (2) malware categorization on DREBIN, the largest labeled Android malware datasets. We utilize the ensemble of these supervised classifiers to design MobiSentry, which outperforms several related approaches and gives a satisfying performance in the evaluation. Furthermore, we integrate MobiSentry with Android OS that enables smartphones with Android to extract features and to predict whether the application is benign or malicious. Experimental results on real smartphones show that users can easily and effectively protect their devices against malware through this system with a small run-time overhead.

scholarly journals Understanding Deep Learning Architecture to Various Problems of Cyber Security

2021 ◽  
Vol 9 (12) ◽  
pp. 740-746
Author(s):  
Dr. Diwakar Ramanuj Tripathi
Keyword(s):  
Deep Learning ◽  
Cyber Security ◽  
Feature Representation ◽  
Generative Adversarial Networks ◽  
Android Malware ◽  
Security Applications ◽  
Malware Classification ◽  
Android Malware Detection ◽  
Adversarial Networks ◽  
Phishing Detection

Abstract: Traditional machine learning has evolved into deep learning. It's capable of extracting the best feature representation from raw input samples. Intrusion detection, malware classification, Android malware detection, spam and phishing detection, and binary analysis are just a few examples of how this has been used in cyber security. Deep auto encoders, limited Boltzmann machines, recurrent neural networks, generative adversarial networks, and other DL methods are all described in this study in a brief tutorial-style method. After that, we'll go over how each of the DL methods is employed in security applications. Keywords: Machine, Cyber, Security, Architecture, Technology.

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