Results 71 to 80 of about 11,847 (201)
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
Securing End‐To‐End Encrypted File Sharing Services With the Messaging Layer Security Protocol
Concurrency and Computation: Practice and Experience, Volume 37, Issue 27-28, 25 December 2025.ABSTRACT Secure file sharing is essential in today's digital environment, yet many systems remain vulnerable: if an attacker steals client keys, they can often decrypt both past and future content. To address this challenge, we propose a novel file‐sharing architecture that strengthens post‐compromise security while remaining practical.
Roland Helmich, Lars Braubach
wiley +1 more source
Android Malware Detection Using Autoencoder
, 2019 9 Pages, 4 Figures, 3 ...
Naway, Abdelmonim, Li, Yuancheng
openaire +2 more sources
Towards Explainable CNNs for Android Malware Detection
Procedia Computer Science, 2021 Abstract A challenge for implementing deep learning research in the real-world is the availability of techniques that explain predictions of a model, particularly in light of potential legal requirements to give an account of algorithmic outcomes for certain use-cases.
Kinkead, Martin +3 more
openaire +3 more sources
Alexandria Engineering JournalWith the fast growth of mobile phone usage, malicious threats against Android mobile devices are enhanced. The Android system utilizes a wide range of sensitive apps like banking apps; thus, it develops the aim of malware that uses the vulnerability of ...
Shoayee Dlaim Alotaibi +7 more
doaj +1 more source
A static analysis approach for Android permission-based malware detection systems.
PLoS ONE, 2021 The evolution of malware is causing mobile devices to crash with increasing frequency. Therefore, adequate security evaluations that detect Android malware are crucial.
Juliza Mohamad Arif +5 more
doaj +1 more source
An Automated Vision-Based Deep Learning Model for Efficient Detection of Android Malware Attacks
IEEE Access, 2022 Recently, cybersecurity experts and researchers have given special attention to developing cost-effective deep learning (DL)-based algorithms for Android malware detection (AMD) systems.
Iman Almomani +2 more
doaj +1 more source
Advanced Intelligent Discovery, Volume 1, Issue 2, August 2025.This paper describes the basis for AInsectID Version 1, a GUI‐operable open‐source insect species identification, color processing, and image analysis software. This paper discusses our methods of algorithmic development, coupled to rigorous machine training used to enable high levels of validation accuracy.
Haleema Sadia, Parvez Alam
wiley +1 more source
Leveraging Ethical Narratives to Enhance LLM‐AutoML Generated Machine Learning Models
Expert Systems, Volume 42, Issue 7, July 2025.ABSTRACT The growing popularity of generative AI and large language models (LLMs) has sparked innovation alongside debate, particularly around issues of plagiarism and intellectual property law. However, a less‐discussed concern is the quality of code generated by these models, which often contains errors and encourages poor programming practices. This
Jordan Nelson +4 more
wiley +1 more source
Android Malware Family Classification Based on Resource Consumption over Time
, 2017 The vast majority of today's mobile malware targets Android devices. This has pushed the research effort in Android malware analysis in the last years.
Aniello, Leonardo +5 more
core +1 more source