Results 191 to 200 of about 3,123 (222)

Detection of repackaged Android Malware

The 9th International Conference for Internet Technology and Secured Transactions (ICITST-2014), 2014
Android applications are widely used by millions of users to perform many activities. Unfortunately, legitimate and popular applications are targeted by malware authors and they repackage the existing applications by injecting additional code intended to perform malicious activities without the knowledge of end users.
Hossain Shahriar, Victor Clincy
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, Yuwei Zeng
openaire   +1 more source

Cyber Code Intelligence for Android Malware Detection

IEEE Transactions on Cybernetics, 2023
Evolving Android malware poses a severe security threat to mobile users, and machine-learning (ML)-based defense techniques attract active research. Due to the lack of knowledge, many zero-day families' malware may remain undetected until the classifier gains specialized knowledge.
Junyang Qiu, Qing-Long Han, Wei Luo, Lei Pan, Surya Nepal, Jun Zhang, Yang Xiang   +6 more
openaire   +3 more sources

Features to Detect Android Malware

2018 IEEE Colombian Conference on Communications and Computing (COLCOM), 2018
Android is one the most used mobile operating system worldwide. Due to its technological impact, its open source code and the possibility of installing applications from third parties without any central control, Android has recently become a malware target. Even if it includes security mechanisms, the last news about malicious activities and Android’s
Christian Camilo Urcuqui Lopez, Jhoan Steven Delgado Villarreal, Andres Felipe Perez Belalcazar, Andres Navarro Cadavid, Javier Gustavo Diaz Cely   +4 more
openaire   +1 more source

Infrastructure for Detecting Android Malware

2013
Malware for smartphones have sky-rocketed these last years, particularly for Android platforms. To tackle this threat, services such as Google Bouncer have intended to counter-attack. However, it has been of short duration since the malware have circumvented the service by changing their behaviors.
Laurent Delosières, David García
openaire   +1 more source

Android malware detection a survey

2017 IEEE International Conference on Circuits and Systems (ICCS), 2017
The popularity of Android mobile devices has gone up in our lives and are being used for handling a lot of our personal and confidential information. Hence t hey are now an ideal target for attackers. Android based smart-phone users can download a lot of free applications from Android Application Market/Play Store.
Raima Zachariah, K. Akash, Mohammed Sajmal Yousef, Anu Mary Chacko   +3 more
openaire   +1 more source

Android Malware Detection

2023 World Conference on Communication & Computing (WCONF), 2023
N Gagan, S Sai Kumar, M Keerthana, Samarth S Vaidya, Vanshika Rastogi   +4 more
openaire   +1 more source

Android Malware Detection: A Survey

2018
In the world today, smartphones are evolving every day and with this evolution, security becomes a big issue. Security is an important aspect of the human existence and in a world, with inadequate security, it becomes an issue for the safety of the smartphone users. One of the biggest security threats to smartphones is the issue of malware.
Modupe Odusami, Olusola Abayomi-Alli, Sanjay Misra, Olamilekan Shobayo, Robertas Damasevicius, Rytis Maskeliunas   +5 more
openaire   +1 more source

Decision tree based android malware detection system

2018 26th Signal Processing and Communications Applications Conference (SIU), 2018
Developments in mobile device technology are driving mobile malware development especially on popular operating system platforms such as Android. Defensive software developed for malware is limited due to insufficient understanding of the features of malicious software and inaccessible on time to relevant examples.
Utku, Anil, DOĞRU, İBRAHİM ALPER, AKCAYOL, MUHAMMET ALİ   +2 more
openaire   +2 more sources

A Survey of Android Malware Detection with Deep Neural Models

ACM Computing Surveys, 2021
Jun-Yang Qiu, Jun Zhang, Wei Luo
exaly