[HTML][HTML] An in-depth review of machine learning based Android malware detection
It is estimated that around 70% of mobile phone users have an Android device. Due to this
popularity, the Android operating system attracts a lot of malware attacks. The sensitive …
popularity, the Android operating system attracts a lot of malware attacks. The sensitive …
Threats, attacks and defenses to federated learning: issues, taxonomy and perspectives
Abstract Empirical attacks on Federated Learning (FL) systems indicate that FL is fraught
with numerous attack surfaces throughout the FL execution. These attacks can not only …
with numerous attack surfaces throughout the FL execution. These attacks can not only …
A systematic overview of android malware detection
Due to the completely open-source nature of Android, the exploitable vulnerability of
malware attacks is increasing. To stay ahead of other similar review work attempting to deal …
malware attacks is increasing. To stay ahead of other similar review work attempting to deal …
S3Feature: A static sensitive subgraph-based feature for android malware detection
F Ou, J Xu - Computers & Security, 2022 - Elsevier
As the most popular mobile platform, Android has become the major attack target of
malware, and thus there is an urgent need to effectively thwart them. Recently, the machine …
malware, and thus there is an urgent need to effectively thwart them. Recently, the machine …
Cyber-threat detection system using a hybrid approach of transfer learning and multi-model image representation
Currently, Android apps are easily targeted by malicious network traffic because of their
constant network access. These threats have the potential to steal vital information and …
constant network access. These threats have the potential to steal vital information and …
A deep dive inside drebin: An explorative analysis beyond android malware detection scores
Machine learning advances have been extensively explored for implementing large-scale
malware detection. When reported in the literature, performance evaluation of machine …
malware detection. When reported in the literature, performance evaluation of machine …
Boosting training for PDF malware classifier via active learning
Y Li, X Wang, Z Shi, R Zhang, J Xue… - International journal of …, 2022 - Wiley Online Library
Abstract Machine learning algorithms are widely used for cybersecurity applications, include
spam, malware detection. In these applications, the machine learning model has to face …
spam, malware detection. In these applications, the machine learning model has to face …
HamDroid: permission-based harmful android anti-malware detection using neural networks
S Seraj, S Khodambashi, M Pavlidis… - Neural Computing and …, 2022 - Springer
Android platforms are a popular target for attackers, while many users around the world are
victims of Android malwares threatening their private information. Numerous Android anti …
victims of Android malwares threatening their private information. Numerous Android anti …
On building machine learning pipelines for Android malware detection: a procedural survey of practices, challenges and opportunities
As the smartphone market leader, Android has been a prominent target for malware attacks.
The number of malicious applications (apps) identified for it has increased continually over …
The number of malicious applications (apps) identified for it has increased continually over …
Gaining insights in datasets in the shade of “garbage in, garbage out” rationale: Feature space distribution fitting
G Canbek - Wiley Interdisciplinary Reviews: Data Mining and …, 2022 - Wiley Online Library
This article emphasizes comprehending the “Garbage In, Garbage Out”(GIGO) rationale and
ensuring the dataset quality in Machine Learning (ML) applications to achieve high and …
ensuring the dataset quality in Machine Learning (ML) applications to achieve high and …