Android Malware Detection Using Category-Based Machine Learning Classifiers

No abstract is available for this paper.

• Publication year

  2016

• Venue

  Conference on Information Technology Education

• Publication date

  2016-09-28

• Fields of study

  Computer Science

• Identifiers
  DOI 10.1145/2978192.2978218
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• DREBIN: Effective and Explainable Detection of Android Malware in Your Pocket

  2014cited by this paper
• Investigating the android intents and permissions for malware detection

  2014cited by this paper
• Detecting mobile malware threats to homeland security through static analysis

  2014cited by this paper
• Static detection of Android malware by using permissions and API calls

  2014cited by this paper
• A New Android Malware Detection Approach Using Bayesian Classification

  2013cited by this paper
• Permission-Based Android Malware Detection

  2013cited by this paper
• DroidMat: Android Malware Detection through Manifest and API Calls Tracing

  2012cited by this paper
• Android permissions: user attention, comprehension, and behavior

  2012cited by this paper
• “Andromaly”: a behavioral malware detection framework for android devices

  2012cited by this paper
• On the automatic categorisation of android applications

  2012cited by this paper
• A Machine Learning Approach to Android Malware Detection

  2012cited by this paper
• Unsafe exposure analysis of mobile in-app advertisements

  2012cited by this paper
• The Effectiveness of Application Permissions

  2011cited by this paper
• AntiMalDroid: An Efficient SVM-Based Malware Detection Framework for Android

  2011cited by this paper
• Curbing Android Permission Creep

  2011cited by this paper
• Crowdroid: behavior-based malware detection system for Android

  2011cited by this paper
• TaintDroid

  2010cited by this paper
• pBMDS: a behavior-based malware detection system for cellphone devices

  2010cited by this paper
• On lightweight mobile phone application certification

  2009cited by this paper
• SCanDroid: Automated Security Certification of Android

  2009cited by this paper
• VirusMeter: Preventing Your Cellphone from Spies

  2009cited by this paper
• STATIC BASED MALWARE DETECTION FRAMEWORK FOR ANDROID APPLICATION Chit

  year unknowncited by this paper

• From code to security: machine learning approaches in android vulnerability detection

  2025cites this paper
• Investigation of Malware & Threat Analysis on APKs Using SVM & ANN Algorithm. -A New Approach

  2023cites this paper
• A survey on machine learning techniques applied to source code

  2023cites this paper
• Realism versus Performance for Adversarial Examples Against DL-based NIDS

  2023cites this paper
• Android malware detection applying feature selection techniques and machine learning

  2022cites this paper
• Threat Modeling for Machine Learning-Based Network Intrusion Detection Systems

  2022cites this paper
• Malware Detection: A Framework for Reverse Engineered Android Applications through Machine Learning Algorithms

  2022cites this paper
• A Survey on Machine Learning Techniques for Source Code Analysis

  2021cites this paper
• Adversarial Machine Learning In Network Intrusion Detection Domain: A Systematic Review

  2021cites this paper
• Malicious application detection in android - A systematic literature review

  2021cites this paper
• Feature Selection for Malware Detection on the Android Platform Based on Differences of IDF Values

  2020cites this paper
• Android Platformunda Kötücül Yazılım Tespiti: Literatür İncelemesi

  2020influential citation
• Improved real-time permission based malware detection and clustering approach using model independent pruning

  2020cites this paper
• Empirical Study on Intelligent Android Malware Detection based on Supervised Machine Learning

  2020influential citation
• Android malware detection based on multi-category and tag level ratio

  2020cites this paper
• Novel Deep Learning Model for Uncertainty Prediction in Mobile Computing

  2020cites this paper
• Anomaly Network Based Malware Detection System Using Hybrid Techniques

  2020cites this paper
• Design and Implementation of a Malware Detection System on Smartphones

  2020cites this paper
• Constructing Features for Detecting Android Malicious Applications: Issues, Taxonomy and Directions

  2019cites this paper
• Android Malware Detection Combined with Static and Dynamic Analysis

  2019cites this paper
• A Review of Static Malware Detection for Android Apps Permission Based on Deep Learning

  2019cites this paper
• A collaborative policy-based security scheme to enforce resource access controlling mechanism

  2019cites this paper
• Protecting contacts against privacy leaks in smartphones

  2018cites this paper
• ANDROID MALWARE CLASSIFICATION BASE ON APPLICATION CATEGORY USING STATIC CODE ANALYSIS

  2018cites this paper
• Preliminary Findings: Revising Developer Guideline Using Word Frequency for Identifying Apps Miscategorization

  2018cites this paper
• DroidEnsemble: Detecting Android Malicious Applications With Ensemble of String and Structural Static Features

  2018cites this paper
• An Android Malware Detection Technique Based on Optimized Permissions and API

  2018cites this paper
• An Android Malware Detection Technique Using Optimized Permission and API with PCA

  2018cites this paper
• Dynamic Permissions based Android Malware Detection using Machine Learning Techniques

  2017cites this paper
• ENHANCING ANDROID SECURITY: MACHINE LEARNING APPROACHES FOR DETECTING MALWARE

  year unknowncites this paper
• Malware detection on android using Adaboost algorithm

  year unknowncites this paper