Results 51 to 60 of about 1,195 (175)

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

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

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

Improved chimp optimization algorithm (ICOA) feature selection and deep neural network framework for internet of things (IOT) based android malware detection

Measurement: Sensors, 2023
Internet of Things (IoT) is extensively implemented using Android applications thus detecting malicious Android apps is necessary. Malicious has been multiplying fast as a result of the growing usage of smartphones.
Tirumala Vasu G, Samreen Fiza, ATA. Kishore Kumar, V. Sowmya Devi, Ch Niranjan Kumar, Afreen Kubra   +5 more
doaj   +1 more source

Cipher‐Guard: A Machine Learning Model for Adaptive and Context‐Aware Password Security

IET Information Security, Volume 2026, Issue 1, 2026.
This research aims to enhance password security by designing, training and testing Cipher‐Guard, a machine learning (ML) algorithm that incorporates complex features and techniques derived from proven feature engineering. The current model is inherently built on the equations of mathematical modelling concerning complexity metrics of passwords and ...
Mohammed Naif Alatawi, Xueqin Liang
wiley   +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

Android Malware Detection Technology Based on Deep Convolutional Neural Network

四川大学学报. 自然科学版, 2020
The rapid iteration of the Android system and its open source features have resulted in many variants of Android malware, which brings great challenges to the classification and detection of Android malware.
GAO Yang-Chen, Fang Yong, Liu Liang, ZHANG Lei   +3 more
doaj  

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

Robust AI‐SCORE Framework: Independent and Adversarial Validation for Malware Detection

Journal of Engineering, Volume 2026, Issue 1, 2026.
Traditional malware detection methods such as signature‐based approaches and statistical analysis are becoming less effective in detecting the new breed of malware, which is holding high levels of complexity in terms of the number of code versions, compilation patterns, time to live (TTL), and jumping through evasion techniques.
Hafiz Talha Arif Zuberi, Usman Ali, Usama Ahmed, Muhammad Usman Tariq, Tariq Shahzad, Muhammad Adnan Khan, Amir H. Gandomi, Luis Rabelo   +7 more
wiley   +1 more source

XAI and Android Malware Models

Android malware detection based on machine learning (ML) and deep learning (DL) models is widely used for mobile device security. Such models offer benefits in terms of detection accuracy and efficiency, but it is often difficult to understand how such learning models make decisions. As a result, these popular malware detection strategies are generally
Maithili Kulkarni, Mark Stamp
openaire   +2 more sources