Results 11 to 20 of about 2,895 (224)
Deep Android Malware Detection [PDF]
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 +10 more
openaire +5 more sources
Trends in Android Malware Detection [PDF]
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 +2 more
core +6 more sources
Android Malware Detection Using BERT [PDF]
Android malware detection using ...
SOUANI, Badr +4 more
openaire +3 more sources
MADLIRA: A Tool for Android Malware Detection
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]
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 +5 more
core +4 more sources
Android malware detection: An eigenspace analysis approach [PDF]
7 pages, 4 figures ...
Yerima, Suleiman Y. +2 more
openaire +7 more sources
DroidEnemy: Battling adversarial example attacks for Android malware detection
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 +5 more
doaj +2 more sources
The rise of obfuscated Android malware and impacts on detection methods [PDF]
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 +2 more
doaj +2 more sources
Android Malware Category and Family Identification Using Parallel Machine Learning [PDF]
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 +2 more
doaj +1 more source
Using Machine Learning to Identify Android Malware Relying on API calling sequences and Permissions [PDF]
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 +6 more
doaj +1 more source

