Formalization of Neural Network Applications to Secure 3D Mobile Applications

Formalization of Neural Network Applications to Secure 3D Mobile Applications

Ce travail de thèse fait partie du projet 3D NeuroSecure. C'est un projet d'investissement d'avenir, qui vise à développer une solution de collaboration sécurisée pour l'innovation thérapeutique appliquant les traitements de haute performance (HPC) au monde biomédical. Cette solu...

Full description

Bibliographic Details
Main Author:	Irolla, Paul
Other Authors:	Université Paris-Saclay (ComUE)
Language:	en
Published:	2018
Subjects:	Détection de malware Android Vulnérabilité Data mining Apprentissage artificiel Séquence Android malware detection, Vulnerability Machine learning Sequence
Online Access:	http://www.theses.fr/2018SACLS585/document

Similar Items

Android Mobile Malware Detection Using Machine Learning: A Systematic Review
by: Janaka Senanayake, et al.
Published: (2021-07-01)

BENCHMARKING MACHINE LEARNING ALGORITHMS FOR ANDROID MALWARE DETECTION
by: Somayyeh Fallah, et al.
Published: (2019-12-01)

A Comprehensive Survey on Machine Learning Techniques for Android Malware Detection
by: Vasileios Kouliaridis, et al.
Published: (2021-04-01)

Android application evolution and malware detection
by: Shi, Wenjie
Published: (2016)

Caractérisation et détection de malware Android basées sur les flux d'information.
by: Andriatsimandefitra Ratsisahanana, Radoniaina
Published: (2014)

Detecting Android Locker-Ransomware on Chinese Social Networks
by: Dan Su, et al.
Published: (2019-01-01)

DroidPDF: The Obfuscation Resilient Packer Detection Framework for Android Apps
by: Caijun Sun, et al.
Published: (2020-01-01)

FAMD: A Fast Multifeature Android Malware Detection Framework, Design, and Implementation
by: Hongpeng Bai, et al.
Published: (2020-01-01)

Tweet analysis for Android malware detection in Google Play Store
by: Fan, Zhiang
Published: (2018)

DexX: A Double Layer Unpacking Framework for Android
by: Caijun Sun, et al.
Published: (2018-01-01)

Empirical Analysis of Forest Penalizing Attribute and Its Enhanced Variations for Android Malware Detection
by: Akintola, A.G, et al.
Published: (2022)

Android Malware Detection Based on Factorization Machine
by: Chenglin Li, et al.
Published: (2019-01-01)

Two Anatomists Are Better than One—Dual-Level Android Malware Detection
by: Vasileios Kouliaridis, et al.
Published: (2020-07-01)

Android Malware Detection through Machine Learning Techniques: A Review
by: Abikoye Oluwakemi Christiana, et al.
Published: (2020-02-01)

Malware Detection Inside App Stores Based on Lifespan Measurements
by: Carlos Cilleruelo, et al.
Published: (2021-01-01)

Identifying Malicious Software Using Deep Residual Long-Short Term Memory
by: Aziz Alotaibi
Published: (2019-01-01)

AndroDFA: Android Malware Classification Based on Resource Consumption
by: Luca Massarelli, et al.
Published: (2020-06-01)

A Hybrid Approach for Android Malware Detection and Family Classification
by: Meghna Dhalaria, et al.
Published: (2021-05-01)

Android Applications Security
by: Paul POCATILU
Published: (2011-01-01)

A Lightweight Android Malware Classifier Using Novel Feature Selection Methods
by: Ahmad Salah, et al.
Published: (2020-05-01)

A-Pot: A Comprehensive Android Analysis Platform Based on Container Technology
by: Jungsoo Park, et al.
Published: (2020-01-01)

A Novel Dynamic Android Malware Detection System With Ensemble Learning
by: Pengbin Feng, et al.
Published: (2018-01-01)

A Two-Layer Deep Learning Method for Android Malware Detection Using Network Traffic
by: Jiayin Feng, et al.
Published: (2020-01-01)

Intrusion Detection System for Android : Linux Kernel System Salls Analysis
by: Borek, Martin
Published: (2017)

An in-Depth Study of the Jisut Family of Android Ransomware
by: Alejandro Martin, et al.
Published: (2018-01-01)

Real-Time Behavior Analysis and Identification for Android Application
by: Sixian Sun, et al.
Published: (2018-01-01)

Ransomware Detection System for Android Applications
by: Samah Alsoghyer, et al.
Published: (2019-08-01)

A Hybrid Analysis-Based Approach to Android Malware Family Classification
by: Chao Ding, et al.
Published: (2021-08-01)

A Novel Method for Detecting Future Generations of Targeted and Metamorphic Malware Based on Genetic Algorithm
by: Danial Javaheri, et al.
Published: (2021-01-01)

Web-Based Android Malicious Software Detection and Classification System
by: İbrahim Alper Doğru, et al.
Published: (2018-09-01)

Android Malware Detection Using TCN with Bytecode Image
by: Wenhui Zhang, et al.
Published: (2021-06-01)

MALWARE FOR MOBILE DEVICES AND THEIR SECURITY
by: Ştefan IOVAN, et al.
Published: (2018-05-01)

A Client/Server Malware Detection Model Based on Machine Learning for Android Devices
by: Arthur Fournier, et al.
Published: (2021-06-01)

Android Malware Detection Based on Composition Ratio of Permission Pairs
by: Hiroya Kato, et al.
Published: (2021-01-01)

Three-Phase Detection and Classification for Android Malware Based on Common Behaviors
by: Ying-Dar Lin, et al.
Published: (2016-09-01)

Constructing Features for Detecting Android Malicious Applications: Issues, Taxonomy and Directions
by: Wei Wang, et al.
Published: (2019-01-01)

Open Sesame! Design and Implementation of Backdoor to Secretly Unlock Android Devices
by: Junsung Cho, et al.
Published: (2017-11-01)

An Android Malicious Code Detection Method Based on Improved DCA Algorithm
by: Chundong Wang, et al.
Published: (2017-02-01)

AdvAndMal: Adversarial Training for Android Malware Detection and Family Classification
by: Chenyue Wang, et al.
Published: (2021-06-01)

Android Malware Detection Based on API Pairing
Published: (2020-10-01)