Results 41 to 50 of about 1,195 (175)

A Dynamic DL-Driven Architecture to Combat Sophisticated Android Malware

IEEE Access, 2020
The predominant Android operating system has captured enormous attention globally not only in smart phone industry but also for varied smart devices.
Iram Bibi, Adnan Akhunzada, Jahanzaib Malik, Javed Iqbal, Arslan Musaddiq, Sungwon Kim   +5 more
doaj   +1 more source

Generating Pattern‐Based Datasets for Cyber Attack Detection Using Machine‐Learning Techniques

WIREs Data Mining and Knowledge Discovery, Volume 16, Issue 2, June 2026.
The aim of this work is to review the state of the art in the design, generation, and labeling of attack pattern datasets for training of detection systems based on machine learning. ABSTRACT This work aims to review the state of the art in the design, generation, and labeling of attack pattern datasets for the training of detection systems based on ...
Pedro Díaz García, Diego López Abril, Javier Bermejo Higuera, Víctor Gonzalez Llorente, Miguel Angel Sicilia Urbán   +4 more
wiley   +1 more source

Android Malware Detection

International Journal of Innovative Research in Advanced Engineering
Machine learning based detection system of Android malware and analysis of features which are static. The deigned system extracts non executable features such as permissions, intents, activities, and API calls from Android APK files. We analyze non-executable APK features and classify them using a Random Forest model deployed on a Flask server.
S, Mrs Hamsareka, S, Krishnamoorthy, T, Prasanth, R, Purusothaman, A, Santhosh Kumar   +4 more
  +7 more sources

Mission Aware Cyber‐Physical Security

Systems Engineering, Volume 29, Issue 2, Page 354-367, March 2026.
ABSTRACT Perimeter cybersecurity, while essential, has proven insufficient against sophisticated, coordinated, and cyber‐physical attacks. In contrast, mission‐centric cybersecurity emphasizes finding evidence of attack impact on mission success, allowing for targeted resource allocation to mitigate vulnerabilities and protect critical assets.
Georgios Bakirtzis, Bryan Carter, Cody H. Fleming, Carl R. Elks   +3 more
wiley   +1 more source

Static analysis framework for permission-based dataset generation and android malware detection using machine learning

EURASIP Journal on Information Security
Since Android is the popular mobile operating system worldwide, malicious attackers seek out Android smartphones as targets. The Android malware can be identified through a number of established detection techniques.
Amarjyoti Pathak, Th. Shanta Kumar, Utpal Barman   +2 more
doaj   +1 more source

Intelligent Pattern Recognition Using Equilibrium Optimizer With Deep Learning Model for Android Malware Detection

IEEE Access
Android malware recognition is the procedure of mitigating and identifying malicious software (malware) planned to target Android operating systems (OS) that are extremely utilized in smartphones and tablets.
Mohammed Maray, Mashael Maashi, Haya Mesfer Alshahrani, Sumayh S. Aljameel, Sitelbanat Abdelbagi, Ahmed S. Salama   +5 more
doaj   +1 more source

DroidDetectMW: A Hybrid Intelligent Model for Android Malware Detection

Applied Sciences, 2023
Malicious apps specifically aimed at the Android platform have increased in tandem with the proliferation of mobile devices. Malware is now so carefully written that it is difficult to detect.
Fatma Taher, Omar AlFandi, Mousa Al-kfairy, Hussam Al Hamadi, Saed Alrabaee   +4 more
doaj   +1 more source

Evaluation of Advanced Ensemble Learning Techniques for Android Malware Detection [PDF]

Vietnam Journal of Computer Science, 2020
Android is the most well-known portable working framework having billions of dynamic clients worldwide that pulled in promoters, programmers, and cybercriminals to create malware for different purposes. As of late, wide-running inquiries have been led on
Md. Shohel Rana, Andrew H. Sung
doaj   +1 more source

Securing the Unseen: A Comprehensive Exploration Review of AI‐Powered Models for Zero‐Day Attack Detection

Expert Systems, Volume 43, Issue 3, March 2026.
ABSTRACT Zero‐day exploits remain challenging to detect because they often appear in unknown distributions of signatures and rules. The article entails a systematic review and cross‐sectional synthesis of four fundamental model families for identifying zero‐day intrusions, namely, convolutional neural networks (CNN), deep neural networks (DNN ...
Abdullah Al Siam, Nuruzzaman Faruqui, Akm Azad, Mohammad Ali Moni   +3 more
wiley   +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