Android source code vulnerability detection: a systematic literature review

J Senanayake, H Kalutarage, MO Al-Kadri… - ACM Computing …, 2023 - dl.acm.org
The use of mobile devices is rising daily in this technological era. A continuous and
increasing number of mobile applications are constantly offered on mobile marketplaces to …

Android mobile malware detection using machine learning: A systematic review

J Senanayake, H Kalutarage, MO Al-Kadri - Electronics, 2021 - mdpi.com
With the increasing use of mobile devices, malware attacks are rising, especially on Android
phones, which account for 72.2% of the total market share. Hackers try to attack …

[HTML][HTML] Kronodroid: time-based hybrid-featured dataset for effective android malware detection and characterization

A Guerra-Manzanares, H Bahsi, S Nõmm - Computers & Security, 2021 - Elsevier
Android malware evolution has been neglected by the available data sets, thus providing a
static snapshot of a non-stationary phenomenon. The impact of the time variable has not had …

Android malware detection based on multi-head squeeze-and-excitation residual network

H Zhu, W Gu, L Wang, Z Xu, VS Sheng - Expert Systems with Applications, 2023 - Elsevier
The popularity and flexibility of the Android platform makes it the primary target of malicious
attackers. The behaviors of malware, such as malicious charges and privacy theft, pose …

[HTML][HTML] Machine learning for android malware detection: mission accomplished? a comprehensive review of open challenges and future perspectives

A Guerra-Manzanares - Computers & Security, 2024 - Elsevier
The extensive research in machine learning based Android malware detection showcases
high-performance metrics through a wide range of proposed solutions. Consequently, this …

Wolf at the door: Preventing install-time attacks in npm with latch

E Wyss, A Wittman, D Davidson, L De Carli - … of the 2022 ACM on Asia …, 2022 - dl.acm.org
The npm software ecosystem allows developers to easily import code written by others.
However, manual vetting of every individual installed component is made difficult in many …

MFDroid: A stacking ensemble learning framework for Android malware detection

X Wang, L Zhang, K Zhao, X Ding, M Yu - Sensors, 2022 - mdpi.com
As Android is a popular a mobile operating system, Android malware is on the rise, which
poses a great threat to user privacy and security. Considering the poor detection effects of …

AndroMalPack: enhancing the ML-based malware classification by detection and removal of repacked apps for Android systems

H Rafiq, N Aslam, M Aleem, B Issac, RH Randhawa - Scientific Reports, 2022 - nature.com
Due to the widespread usage of Android smartphones in the present era, Android malware
has become a grave security concern. The research community relies on publicly available …

IMCLNet: A lightweight deep neural network for Image-based Malware Classification

B Zou, C Cao, F Tao, L Wang - Journal of Information Security and …, 2022 - Elsevier
With the increasing number of malware and advanced evasion technology, it is more and
more difficult to detect malware accurately and efficiently. To solve this challenge, a feasible …

GIWRF-SMOTE: Gini impurity-based weighted random forest with SMOTE for effective malware attack and anomaly detection in IoT-Edge

J Manokaran, G Vairavel - Smart Science, 2023 - Taylor & Francis
ABSTRACT The Internet of Things (IoT) is a smart technology that has switched the
conventional way of living into smart living. As their usage becomes unavoidable, malware …