Results 51 to 60 of about 20,533 (244)
Android Malware Clustering through Malicious Payload Mining
, 2017 Clustering has been well studied for desktop malware analysis as an effective triage method. Conventional similarity-based clustering techniques, however, cannot be immediately applied to Android malware analysis due to the excessive use of third-party ...
I Santos +7 more
core +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 +4 more
wiley +1 more source
Eight years of rider measurement in the Android malware ecosystem: evolution and lessons learned [PDF]
, 2018 Despite the growing threat posed by Android malware, the research community is still lacking a comprehensive view of common behaviors and trends exposed by malware families active on the platform.
Stringhini, Gianluca +1 more
core
Explaining Black-box Android Malware Detection [PDF]
2018 26th European Signal Processing Conference (EUSIPCO), 2018 Machine-learning models have been recently used for detecting malicious Android applications, reporting impressive performances on benchmark datasets, even when trained only on features statically extracted from the application, such as system calls and permissions.
Marco Melis +4 more
openaire +4 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 +3 more
wiley +1 more source
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 +5 more
doaj +1 more source
R2-D2: ColoR-inspired Convolutional NeuRal Network (CNN)-based AndroiD Malware Detections
, 2018 The influence of Deep Learning on image identification and natural language processing has attracted enormous attention globally. The convolution neural network that can learn without prior extraction of features fits well in response to the rapid ...
Huang, TonTon Hsien-De, Kao, Hung-Yu
core +1 more source
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 +3 more
wiley +1 more source
EURASIP Journal on Information SecuritySince 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 +2 more
doaj +1 more source
IEEE AccessAndroid 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 +5 more
doaj +1 more source