Results 41 to 50 of about 11,847 (201)

Understanding Android Obfuscation Techniques: A Large-Scale Investigation in the Wild [PDF]

, 2018
In this paper, we seek to better understand Android obfuscation and depict a holistic view of the usage of obfuscation through a large-scale investigation in the wild.
Chen, Kai, Diao, Wenrui, Dong, Shuaike, Li, Menghao, Li, Zhou, Liu, Jian, Liu, Xiangyu, Wang, Xiaofeng, Xu, Fenghao, Zhang, Kehuan   +9 more
core   +2 more sources

DroidEnemy: Battling adversarial example attacks for Android malware detection

Digital Communications and Networks, 2022
In recent years, we have witnessed a surge in mobile devices such as smartphones, tablets, smart watches, etc., most of which are based on the Android operating system. However, because these Android-based mobile devices are becoming increasingly popular,
Neha Bala, Aemun Ahmar, Wenjia Li, Fernanda Tovar, Arpit Battu, Prachi Bambarkar   +5 more
doaj   +1 more source

A machine learning technique for Android malicious attacks detection based on API calls [PDF]

Decision Science Letters
Android malware is widespread and it is considered as one of the most threatening attacks recently. The threat is targeting to damage access data or information or leaking them; in general, malicious software consists of viruses, worms, and ...
Mousa AL-Akhras, Saud Alghamdi, Hani Omar, Hazzaa Alshareef   +3 more
doaj   +1 more source

Improved chimp optimization algorithm (ICOA) feature selection and deep neural network framework for internet of things (IOT) based android malware detection

Measurement: Sensors, 2023
Internet of Things (IoT) is extensively implemented using Android applications thus detecting malicious Android apps is necessary. Malicious has been multiplying fast as a result of the growing usage of smartphones.
Tirumala Vasu G, Samreen Fiza, ATA. Kishore Kumar, V. Sowmya Devi, Ch Niranjan Kumar, Afreen Kubra   +5 more
doaj   +1 more source

Longitudinal performance analysis of machine learning based Android malware detectors [PDF]

, 2019
This paper presents a longitudinal study of the performance of machine learning classifiers for Android malware detection. The study is undertaken using features extracted from Android applications first seen between 2012 and 2016.
Khan, Sarmadullah, Yerima, Suleiman
core   +1 more source

Analysis and evaluation of SafeDroid v2.0, a framework for detecting malicious Android applications [PDF]

, 2018
Android smartphones have become a vital component of the daily routine of millions of people, running a plethora of applications available in the official and alternative marketplaces.
Argyriou, Marios, Dragoni, Nicola, Spognardi, Angelo   +2 more
core   +2 more sources

DroidDetectMW: A Hybrid Intelligent Model for Android Malware Detection

Applied Sciences, 2023
Malicious apps specifically aimed at the Android platform have increased in tandem with the proliferation of mobile devices. Malware is now so carefully written that it is difficult to detect.
Fatma Taher, Omar AlFandi, Mousa Al-kfairy, Hussam Al Hamadi, Saed Alrabaee   +4 more
doaj   +1 more source

Security Toolbox for Detecting Novel and Sophisticated Android Malware

, 2015
This paper presents a demo of our Security Toolbox to detect novel malware in Android apps. This Toolbox is developed through our recent research project funded by the DARPA Automated Program Analysis for Cybersecurity (APAC) project.
Deering, Tom, Holland, Benjamin, Kothari, Suresh, Mathews, Jon, Ranade, Nikhil   +4 more
core   +1 more source

GA-StackingMD: Android Malware Detection Method Based on Genetic Algorithm Optimized Stacking

Applied Sciences, 2023
With the rapid development of network and mobile communication, intelligent terminals such as smartphones and tablet computers have changed people’s daily life and work.
Nannan Xie, Zhaowei Qin, Xiaoqiang Di
doaj   +1 more source

Automatically combining static malware detection techniques [PDF]

, 2015
Malware detection techniques come in many different flavors, and cover different effectiveness and efficiency trade-offs. This paper evaluates a number of machine learning techniques to combine multiple static Android malware detection techniques using ...
Coppens, Bart, De Lille, David, De Sutter, Bjorn, Raman, Daan   +3 more
core   +1 more source