Results 61 to 70 of about 20,533 (244)

TTGNet-AMD: Android malware detection based on multi-modal feature fusion [PDF]

PeerJ Computer Science
The application of static features for Android malware detection has been extensively studied and developed. Existing methods exhibit limitations in both the completeness and discriminability of feature representation, which affects the enhancement of ...
Jiayin Feng, Meiyu Wang, JinLing Song, Chenfeng Yang, Lingzu Zhang, Yang Shen   +5 more
doaj   +2 more sources

Feature selection to enhance android malware detection using modified term frequency-inverse document frequency (MTF-IDF) [PDF]

, 2019
This research synthesizes an evaluation of feature selection algorithm by utilizing Term Frequency-Inverse Document Frequency (TF-IDF) as the main algorithm in Android malware detection.
Mazlan, Nurul Hidayah
core   +1 more source

Exploiting Vision Transformer and Ensemble Learning for Advanced Malware Classification

Engineering Reports, Volume 8, Issue 1, January 2026.
Overview of the proposed RF–ViT ensemble for multi‐class malware classification. Textual (BoW/byte‐frequency) and visual representations are combined via a product rule, achieving improved accuracy and robustness over individual models. ABSTRACT Malware remains a significant concern for modern digital systems, increasing the need for reliable and ...
Fadi Makarem, Rida El Chall, Abed Ellatif Samhat, Khouloud Samrouth, Nader Bakir   +4 more
wiley   +1 more source

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

Scientific Reports, 2022
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 datasets to keep pace with evolving malware.
Husnain Rafiq, Nauman Aslam, Muhammad Aleem, Biju Issac, Rizwan Hamid Randhawa   +4 more
doaj   +1 more source

Malware detection techniques for mobile devices

, 2017
Mobile devices have become very popular nowadays, due to its portability and high performance, a mobile device became a must device for persons using information and communication technologies. In addition to hardware rapid evolution, mobile applications
Amro, Bela
core   +1 more source

Android Malware Detection Using Deep Learning

, 2022
This chapter investigates the potential of deep learning architectures for Android malware detection, specifically convolutional neural networks (CNNs) using natural language processing (NLP) concepts. The proposed solution is based on static analysis of raw opcode sequences from disassembled programs and other complementary features such as API calls ...
Millar, Stuart, McLaughlin, Niall, Martinez-del-Rincon, Jesus, Miller, Paul   +3 more
openaire   +3 more sources

Usability Evaluation of a Push‐Based Passwordless Authentication Model Using Public‐Key Cryptography

IET Biometrics, Volume 2026, Issue 1, 2026.
Despite major advancements in the sphere of the public‐key authentication specifically in the instances of the newly established standards like WebAuthn and the FIDO2, the practical implementation of the passwordless login systems is still hindered by the usability factors, platform‐related requirements, and the very nature of the deployment process is
Ghulam Mustafa, Shah Zaman Nizamani, Irfana Memon, Asif Ali Wagan, Solomiia Fedushko, Abdullah Al Hejaili, M. Srinivas   +6 more
wiley   +1 more source

A-Pot: A Comprehensive Android Analysis Platform Based on Container Technology

IEEE Access, 2020
Recently, intelligent Android malware avoids being analyzed using anti-emulator, anti-debugging, and rooting detection. Existing emulators have problems to be easily detected by malware that check with hardware or sensor information.
Jungsoo Park, Ngoc-Tu Chau, Long Nguyen-Vu, Jaehyeon Yoon, Souhwan Jung   +4 more
doaj   +1 more source

Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detection

, 2018
Machine learning based solutions have been successfully employed for automatic detection of malware in Android applications. However, machine learning models are known to lack robustness against inputs crafted by an adversary.
Chen, Xiao, Li, Chaoran, Nepal, Surya, Ren, Kui, Wang, Derui, Wen, Sheng, Xiang, Yang, Zhang, Jun   +7 more
core   +1 more source

AI‐Powered Defense: Leveraging Deep Learning for Effective Malware Detection

Applied Computational Intelligence and Soft Computing, Volume 2026, Issue 1, 2026.
Traditional malware detection techniques frequently fail to detect and stop malicious activity in an era where cyber threats are becoming more complex. Any software that enters a computer system without the administrator’s consent is considered malicious software.
Nancy Awadallah Awad, Dimitrios A. Karras   +1 more
wiley   +1 more source