A scalable and accurate feature representation method for identifying malicious mobile applications

B Sun, T Ban, SC Chang, YS Sun, T Takahashi… - Proceedings of the 34th …, 2019 - dl.acm.org
With the dramatic growth in smartphone usage, the number of new malicious mobile
applications has increased rapidly. Identifying malicious applications in large-scale datasets …

DEEPSEL: A novel feature selection for early identification of malware in mobile applications

MA Azad, F Riaz, A Aftab, SKJ Rizvi, J Arshad… - Future Generation …, 2022 - Elsevier
Smartphone applications have gained popularity in recent years due to the large footprint of
mobile phone usage and availability of a large number of value-added applications. The …

A mobile malware detection method using behavior features in network traffic

S Wang, Z Chen, Q Yan, B Yang, L Peng… - Journal of Network and …, 2019 - Elsevier
Android has become the most popular mobile platform due to its openness and flexibility.
Meanwhile, it has also become the main target of massive mobile malware. This …

Acquiring and analyzing app metrics for effective mobile malware detection

G Canfora, E Medvet, F Mercaldo… - Proceedings of the 2016 …, 2016 - dl.acm.org
Android malware is becoming very effective in evading detection techniques, and traditional
malware detection techniques are demonstrating their weaknesses. Signature based …

Towards neural network based malware detection on android mobile devices

W Yu, L Ge, G Xu, X Fu - Cybersecurity systems for human cognition …, 2014 - Springer
Due to the exponential increase in the use of smart mobile devices, malware threats on
those devices have been growing and posing security risks. To address this critical issue …

A survey on mobile malware detection methods using machine learning

MEZN Kambar, A Esmaeilzadeh, Y Kim… - 2022 IEEE 12th …, 2022 - ieeexplore.ieee.org
The prevalence of mobile devices (smartphones) along with the availability of high-speed
internet access world-wide resulted in a wide variety of mobile applications that carry a large …

[PDF][PDF] Machine Learning Meets iOS Malware: Identifying Malicious Applications on Apple Environment.

A Cimitile, F Martinelli, F Mercaldo - ICISSP, 2017 - scitepress.org
The huge diffusion of the so-called smartphone devices is boosting the malware writer
community to write more and more aggressive software targeting the mobile platforms. While …

Intelligent mobile malware detection using permission requests and API calls

M Alazab, M Alazab, A Shalaginov, A Mesleh… - Future Generation …, 2020 - Elsevier
Malware is a serious threat that has been used to target mobile devices since its inception.
Two types of mobile malware attacks are standalone: fraudulent mobile apps and injected …

Function‐Oriented Mobile Malware Analysis as First Aid

J Jang, HK Kim - Mobile Information Systems, 2016 - Wiley Online Library
Recently, highly well‐crafted mobile malware has arisen as mobile devices manage highly
valuable and sensitive information. Currently, it is impossible to detect and prevent all …

Improving Malware Detection with a Novel Dataset Based on API Calls

M Torres, R Álvarez, M Cazorla - … Workshop on Soft Computing Models in …, 2022 - Springer
In this paper, we analyze current methods to distinguish malware from benign software
using Machine Learning (ML) and feature engineering techniques that have been …