Machine learning models may be used to classify unknown Android malware utilizing characteristics gathered from the dynamic and static analysis of an Android applications. Anti-virus software simply searches for the signs of the virus instance in a specific programme to detect it while scanning.

Mar 15, 2022 · The support vector machine (SVM), k-nearest neighbors (KNN), linear discriminant analysis (LDA), long short-term memory (LSTM), convolution neural network-long short-term memory (CNN-LSTM), and autoencoder algorithms were applied to …

This paper presents a machine learning approach for Android malware detection. In this work, several machine learning algorithms were utilized, namely k-Nearest neighbor (KNN), Decision Trees (DT), Naive Bayes (NB), Support Vector Machine (SVM) and other ensemble classifiers including Extreme Gradient Boosting (XGBoost), Light Gradient Boosting ...

Jul 10, 2023 · Further, we have used various types of ML algorithms such as Random Forest, KNN (k-Nearest Neighbors), Decision Tree, Gradient Boosting Classifier, SVM (Support Vector Machine), and Logistic Regression to classify the malware and evaluated the performance of each and every machine learning algorithms.

Aug 18, 2023 · By taking into account various machine-learning methods, feature selection is performed and the K-Nearest Neighbor (KNN) machine-learning model is proposed. Testing was carried out on more...

Nov 17, 2018 · Hence, in this paper, two Machine Learning (ML) algorithms, called Support Vector Machine (SVM) and K-Nearest Neighbors (KNN), are applied and evaluated to perform classification of the...

Oct 6, 2024 · Malware remains a critical challenge within the domain of working frameworks and program, with Android frameworks being no special case. In spite of past endeavors utilizing Signature-based methods for malware detection, their restrictions in distinguishing obscure...

This research study explores the usability of machine learning algorithms in classifying whether Android applications are excellent or malicious. The data used.

Apr 18, 2025 · As a response to this challenge, the cyber security community has turned toward utilizing machine-learning (ML) algorithms to enhance detection capabilities. By analyzing datasets to identify patterns and anomalies, machine-learning techniques offer a promising solution for combating various types of Android malware threats including zero-day ...

Sep 15, 2020 · In this study, K-nearest Neighbourhood (KNN) classifier, one of the machine learning methods, is used. Thus, it is aimed to detect malignant mobile software successfully and quickly. The...