Results 191 to 200 of about 2,904 (224)

A Comparison of Features for Android Malware Detection

Proceedings of the SouthEast Conference, 2017
With the increase in mobile device use, there is a greater need for increasingly sophisticated malware detection algorithms. The research presented in this paper examines two types of features of Android applications, permission requests and system calls, as a way to detect malware.
Matthew Leeds, Miclain Keffeler, Travis Atkison   +2 more
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

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   +1 more source

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

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

Deep learning for detecting Android malwares

Proceedings of the 4th International Conference on Smart City Applications, 2019
The revolution and development of malwares over time necessitate an intensive researches on advanced techniques to secure user's personal and critical information, the most challenging task is to build a strong and robust classifier allows to detect different types of malwares and being able to defeat zero-day malware attacks.
Soussi Ilham, Abderrahim Ghadi, Boudhir Anouar Abdelhakim   +2 more
openaire   +1 more source

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

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   +1 more source

Model-Checking for Android Malware Detection

2014
The popularity of Android devices results in a significant increase of Android malwares. These malwares commonly steal users’ private data or do malicious tasks. Therefore, it is important to efficiently and automatically analyze Android applications and identify their malicious behaviors.
Fu Song, Tayssir Touili
openaire   +1 more source

Detecting Android Malware Using Clone Detection

Journal of Computer Science and Technology, 2015
Android is currently one of the most popular smartphone operating systems. However, Android has the largest share of global mobile malware and significant public attention has been brought to the security issues of Android. In this paper, we investigate the use of a clone detector to identify known Android malware.
Jian Chen, Manar H. Alalfi, Thomas R. Dean, Ying Zou 0001   +3 more
openaire   +2 more sources