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Continuous Learning for Android Malware Detection [PDF]
CoRR, 2023 Machine learning methods can detect Android malware with very high accuracy. However, these classifiers have an Achilles heel, concept drift: they rapidly become out of date and ineffective, due to the evolution of malware apps and benign apps.
Yizheng Chen +2 more
semanticscholar +4 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 +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 +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 +6 more
doaj +1 more source
Applied Sciences, 2022 As a result of the rapid advancement of mobile and internet technology, a plethora of new mobile security risks has recently emerged. Many techniques have been developed to address the risks associated with Android malware.
Abimbola G. Akintola +9 more
doaj +1 more source
Android malware category detection using a novel feature vector-based machine learning model
Cybersecurity, 2023 Malware attacks on the Android platform are rapidly increasing due to the high consumer adoption of Android smartphones. Advanced technologies have motivated cyber-criminals to actively create and disseminate a wide range of malware on Android ...
Hashida Haidros Rahima Manzil +1 more
doaj +1 more source
A Survey and Evaluation of Android-Based Malware Evasion Techniques and Detection Frameworks
Information, 2023 Android platform security is an active area of research where malware detection techniques continuously evolve to identify novel malware and improve the timely and accurate detection of existing malware.
Parvez Faruki +5 more
doaj +1 more source
IoT-Based Android Malware Detection Using Graph Neural Network With Adversarial Defense [PDF]
IEEE Internet of Things Journal, 2023 Since the Internet of Things (IoT) is widely adopted using Android applications, detecting malicious Android apps is essential. In recent years, Android graph-based deep learning research has proposed many approaches to extract relationships from the ...
Rahul Yumlembam +3 more
semanticscholar +1 more source
IEEE Access, 2023 Nowadays, the malware on the Android platform is found to be increasing. With the prevalent use of code obfuscation technology, the precision of antivirus software and classical detection techniques is low.
Ghadah Aldehim +7 more
doaj +1 more source
LAMD: Context-Driven Android Malware Detection and Classification with LLMs [PDF]
2025 IEEE Security and Privacy Workshops (SPW)The rapid growth of mobile applications has escalated Android mal ware threats. Although there are numerous detection methods, they often struggle with evolving attacks, dataset biases, and limited explainability.
Xingzhi Qian +4 more
semanticscholar +1 more source