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On Malware Detection in the Android Operating System

Proceedings of the 2020 4th International Conference on Algorithms, Computing and Systems, 2020
The threat of malware attacks on Android mobile devices is an ever-growing one, as usage and sophistication increases. As the Android OS is fairly new in the overall set of operating systems, there is much need and room for research in the area of Android malware detection.
Charles Badami, Houssain Kettani
openaire   +1 more source

Android Malware Detection

2022
For the past few decades, the growth in usage of mobile phones has been increasing abnormally. Recent surveys hypothesize most of the mobile phone market segment is benignly dominated by Android Operating System and this made the Android OS (Operating System) the most vulnerable Operating System; as more users are adopting to use Android OS (Operating ...
Gadde, Sayi Rosshhun, Kaushal, J. C. Pavan, Rao, T. Vijay, Srikanth, N., Gadde, Sayi Khushhal   +4 more
openaire   +1 more source

Behavioral malware detection approaches for Android

2016 IEEE International Conference on Communications (ICC), 2016
Android, the fastest growing mobile operating system released in November 2007, boasts of a staggering 1.4 billion active users. Android users are susceptible to malicious applications that can hack into their personal data due to the lack of careful monitoring of their in-device security.
Mohammad Rakib Amin, Mehedee Zaman, Md. Shohrab Hossain, Mohammed Atiquzzaman   +3 more
openaire   +1 more source

Towards sustainable Android malware detection

Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings, 2018
Approaches to Android malware detection built on supervised learning are commonly subject to frequent retraining, or the trained classifier may fail to detect newly emerged or emerging kinds of malware. This work targets a sustainable Android malware detector that, once trained on a dataset, can continue to effectively detect new malware without ...
Haipeng Cai, John Jenkins
openaire   +2 more sources

Deep android malware detection and classification

2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2017
Long short-term memory recurrent neural network (LSTM-RNN) have witnessed as a powerful approach for capturing long-range temporal dependencies in sequences of arbitrary length. This paper seeks to model a large set of Android permissions particularly the permissions from Normal, Dangerous, Signature and Signature Or System categories within a large ...
R. Vinayakumar, K. P. Soman, Prabaharan Poornachandran   +2 more
openaire   +2 more sources

XGBoost-Based Android Malware Detection

2017 13th International Conference on Computational Intelligence and Security (CIS), 2017
Malware remains the most significant security threat to smartphones in spite of the constantly upgrading of the system. In this paper, we introduce an Android malware detection method based on XGBoost model. We subsequently discuss the effect of feature selection on the classification.
Jiong Wang, Boquan Li 0002, Yuwei Zeng
openaire   +1 more source

PCSD: A Tool for Android Malware Detection

2017
The increasing amount and diversity of malicious applications are reducing efficiency of conventional defenses and it is necessary to create novel method for detection. Consequently, we propose PCSD, a lightweight tool for detection of Android malware by extracting statistical features from applications.
Bo Leng, Jianbin Li, Yang Xu 0013, Liang She, Wuqiang Gao, Quanrun Zeng   +5 more
openaire   +2 more sources

Characterization of Malware Detection on Android Application

2015
Mobile malware performs malicious activities like stealing private information, sending message SMS, reading contacts and can even harm by exploiting data. Malwares are spreading around the world and infecting not only for end users but also for large organizations and service providers.
Chit La Pyae Myo Hein, Khin Mar Myo
openaire   +1 more source

Detecting malware with similarity to Android applications

2015 International Conference on Information and Communication Technology Convergence (ICTC), 2015
In light of the rapid growth of smartphones, there are unrelenting malicious attacks on smartphones from voice phishing to mobile malwares. Especially, SMiShing malicious application has become a crucial threat on smartphone since it can be easily rampant via URLs embedded in SMS messages and emails.
Wonjoo Park, Sun-Joong Kim, Won Ryu
openaire   +2 more sources

Twitter-enhanced Android malware detection

2017 IEEE International Conference on Big Data (Big Data), 2017
In data-driven Android malware detection, large numbers of both malicious and benign apps are used to train machine learning classifiers to detect malware. Existing approaches have nearly exclusively focused on app contents to extract features for classification. We seek to understand if auxiliary data, specifically Twitter data, can be used to improve
Jordan DeLoach, Doina Caragea
openaire   +1 more source