Results 11 to 20 of about 2,895 (224)

Deep Android Malware Detection [PDF]

Proceedings of the Seventh ACM on Conference on Data and Application Security and Privacy, 2017
In this paper, we propose a novel android malware detection system that uses a deep convolutional neural network (CNN). Malware classification is performed based on static analysis of the raw opcode sequence from a disassembled program. Features indicative of malware are automatically learned by the network from the raw opcode sequence thus removing ...
Niall McLaughlin, Jesús Martínez del Rincón, BooJoong Kang, Suleiman Y. Yerima, Paul Miller 0003, Sakir Sezer, Yeganeh Safaei, Erik Trickel, Ziming Zhao 0001, Adam Doupé, Gail-Joon Ahn   +10 more
openaire   +5 more sources

Trends in Android Malware Detection [PDF]

Journal of Digital Forensics, Security and Law, 2013
This paper analyzes different Android malware detection techniques from several research papers, some of these techniques are novel while others bring a new perspective to the research work done in the past. The techniques are of various kinds ranging from detection using host based frameworks and static analysis of executable to feature extraction and
Kaveh Shaerpour, Ali Dehghantanha, Ramlan Mahmod   +2 more
core   +6 more sources

Android Malware Detection Using BERT [PDF]

, 2022
Android malware detection using ...
SOUANI, Badr, KHANFIR, Ahmed, BARTEL, Alexandre, ALLIX, Kevin, LE TRAON, Yves   +4 more
openaire   +3 more sources

MADLIRA: A Tool for Android Malware Detection

Proceedings of the 7th International Conference on Information Systems Security and Privacy, 2021
Today, there are more threats to Android users since malware writers are changing their target to explore the weakness of Android devices, in order to generate malicious behaviors. Thus, detecting Android malwares is becoming crucial. We present in this paper a tool, called MADLIRA (MAlware Detection using Learning and Information Retrieval for Android)
Khanh-Huu-The Dam, Tayssir Touili
openaire   +3 more sources

Android Malware Detection [PDF]

International Journal of Innovative Research in Advanced Engineering
Machine learning based detection system of Android malware and analysis of features which are static. The deigned system extracts non executable features such as permissions, intents, activities, and API calls from Android APK files. We analyze non-executable APK features and classify them using a Random Forest model deployed on a Flask server.
Aaditya Vikram Saravana Bhavan, Srujana Golla, Yuktha Poral, Alan S. Paul, Prasad B. Honnavalli, S. Supreetha   +5 more
core   +4 more sources

Android malware detection: An eigenspace analysis approach [PDF]

2015 Science and Information Conference (SAI), 2015
7 pages, 4 figures ...
Yerima, Suleiman Y., Sezer, Sakir, Muttik, Igor   +2 more
openaire   +7 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   +2 more sources

The rise of obfuscated Android malware and impacts on detection methods [PDF]

PeerJ Computer Science, 2022
The various application markets are facing an exponential growth of Android malware. Every day, thousands of new Android malware applications emerge. Android malware hackers adopt reverse engineering and repackage benign applications with their malicious
Wael F. Elsersy, Ali Feizollah, Nor Badrul Anuar   +2 more
doaj   +2 more sources

Android Malware Category and Family Identification Using Parallel Machine Learning [PDF]

Journal of Information Technology Management, 2022
Android malware is one of the most dangerous threats on the Internet.  It has been on the rise for several years.  As a result, it has impacted many applications such as healthcare, banking, transportation, government, e-commerce, etc.
Ahmed Hashem El Fiky, Mohamed Ashraf Madkour, Ayman El Shenawy   +2 more
doaj   +1 more source

Using Machine Learning to Identify Android Malware Relying on API calling sequences and Permissions [PDF]

Journal of Computing and Communication, 2022
The revolutionary in cyber attacks, especially in smartphones are rising. The Android operating system is becoming one of the most leading operating systems. Therefore, Android malware is rising in terms of popularity.
Haytham Metwaie, Omar Shereef, Amr Ehab, Mostafa Ashaf, Seif ElDein Mohamed, Eslam Amer, Ammar Mohamed   +6 more
doaj   +1 more source