MLDroid—framework for Android malware detection using machine learning ...
Sep 3, 2020 · To detect malware-infected apps, in this research paper we proposed a framework named MLDroid which is a web-based solution. MLDroid framework is based on the principle of feature selection approaches and trained with the help of distinct machine learning algorithms.
Android Malware Detection System Using Machine Learning
Introducing a machine learning-based malware detection strategy that utilizes publicly available metadata information. Analyzing the effectiveness of this model and assessing its potential as a first-stage filter for detecting Android malware.
BERT ensemble based MBR framework for android malware detection
2 days ago · A novel machine learning approach for android malware detection based on the co-existence of features. IEEE Access 11 , 15471–15484 (2023). Article Google Scholar
A Comprehensive Approach to Android Malware Detection Using Machine ...
Jul 30, 2021 · Thus, in this paper, we explore state of the art methods used for Android Malware Detection. To this end, an overview of the android system uncovered the underlying mechanisms and the challenges facing its security framework. Attack vectors were later …
Android Based Malware Detection Technique Using Machine Learning ...
The paper aims to assess the efficiency of machine-learning techniques in augmenting the detection and identification of Android malware. A comprehensive framework is proposed which relies on the use of machine learning (ML) algorithms for analyzing and identifying malicious apps.
OpCode-Based Malware Classification Using Machine Learning …
4 days ago · The deep learning methodology builds upon the work proposed in “Deep Android Malware Detection” by McLaughlin et al. and evaluates the performance of a CNN model trained to automatically extract features from raw OpCode data. ... This study employs both traditional machine learning and deep learning techniques to classify malware based on ...
Android malware detection and identification frameworks by …
Jun 1, 2024 · Urmila [105] proposed a behaviour-based malware detection system using ML techniques. This framework applied the Ensemble EfficientNet and Xeception with ResNet (EEXR), EfficientNet and LightGBM for malware detection. The highest accuracy obtain by the EEXR classifier is 96.75 %.
Autonomous Defending Approaches Based on Machine Learning for Android ...
The chapter outlines the unique challenges in Android malware detection, such as diverse app ecosystems and the obfuscation techniques employed by malware developers. Through the analysis of real-world datasets and performance benchmarks, the chapter demonstrates the practical effectiveness of ML-based approaches and limitations.
AMDDLmodel: Android smartphones malware detection using deep learning ...
Jan 19, 2024 · AMDDLmodel introduces innovative deep learning for Android malware detection, enhancing accuracy and practical user security through inventive feature engineering and comprehensive performance evaluation. The AMDDLmodel shows the highest accuracy values as compared to the existing techniques.
Android Malware Detection Using Machine Learning
Sep 10, 2024 · This paper presents machine learning methods for Android malware detection, including Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Logistic Regression (LR) and Gradient Boosting (GB).