Malware detection in android mobile platform using machine learning algorithms

  title={Malware detection in android mobile platform using machine learning algorithms},
  author={Mariam Al Ali and Davor Svetinovic and Zeyar Aung and Suryani Lukman},
  booktitle={INFOCOM 2017},
Malware has always been a problem in regards to any technological advances in the software world. Thus, it is to be expected that smart phones and other mobile devices are facing the same issues. In this paper, a practical and effective anomaly based malware detection framework is proposed with an emphasis on Android mobile computing platform. A dataset consisting of both benign and malicious applications (apps) were installed on an Android device to analyze the behavioral patterns. We first… 

Figures and Tables from this paper

Malware Detection in Android OS using Machine Learning Techniques
5 Abstract— Malware is a software that is created to distort or obstruct computer or mobile applications, gather sensitive information or execute malicious actions. These malicious activities include
Application of Machine Learning Algorithms for Android Malware Detection
Two Machine Learning algorithms, called Support Vector Machine (SVM) and K-Nearest Neighbors) are applied and evaluated to perform classification of the feature set into either benign or malicious applications (apps) through supervised learning process for Android malware detection.
Detection of Android Malware using Machine Learning
An intelligent model using machine learning algorithms is proposed for detecting the malware applications in smartphones based on the static malware analysis technique and can reliably detect both malware and benign Android applications with high accuracy.
Vulnerability Assessment and Malware Analysis of Android Apps Using Machine Learning
This chapter presents a two-way approach for finding malicious Android packages (APKs) by using different Android applications through static and dynamic analysis.
Mobile-based Malware Detection and Classification using Ensemble Artificial Intelligence
This paper proposes a methodology which brings an ensemble solution between the Support Vector Machine algorithm and the Convolutional Neural Network to create a solution that provides a higher accuracy than available techniques.
Permission-Based Approach for Android Malware Analysis Through Ensemble-Based Voting Model
  • Eslam Amer
  • Computer Science
    2021 International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC)
  • 2021
An ensemble model that uses permission combinations to distinguish between malicious and benign apps and the combination of classifiers into an ensemble model provided better accuracy than an individual classifier is developed.
Android Malware Detection through Machine Learning Techniques: A Review
This paper provided a comprehensive review of machine learning techniques and their applications in Android malware detection as found in contemporary literature.
This paper is focused on the issue of malware detection for Android mobile system by Reverse Engineering of java code. The characteristics of malicious software were identified based on a collected
Malware Detection Using Machine Learning Algorithms and Reverse Engineering of Android Java Code
This research paper is focused on the issue of mobile application malware detection by Reverse Engineering of Android java code and use of Machine Learning algorithms. The malicious software
Security Analysis of Android Applications using Machine Learning
In addition to the static system parameters of an application, framework is built on social data like reviews and ratings obtained from Google Play Store and textual tweets obtained from Twitter which were used to assign a score that evaluates the app in terms of security.


A Study Of Machine Learning Classifiers for Anomaly-Based Mobile Botnet Detection
This study evaluates five machine learning classifiers, namely Naive Bayes, k-nearest neighbour, decision tree, multi-layer perceptron, and support vector machine and finds that knearest neighbour provides the optimum results in terms of performance among the classifiers.
An effective behavior-based Android malware detection system
A behavior-based malware detection system that uses Android APIs and libc Bionic libc function calls along with their arguments to describe sensitive application behaviors and conducts behavior analysis and malware detection using machine learning techniques.
Permission-Based Android Malware Detection
The proposed framework intends to develop a machine learning-based malware detection system on Android to detect malware applications and to enhance security and privacy of smartphone users.
M0Droid: An Android Behavioral-Based Malware Detection Model
M0Droid, a novel Android behavioral-based malware detection technique comprising a lightweight client agent and a server analyzer, is proposed here to generate standardized mobile malware signatures based on their behavior and a method for comparing generated signatures.
“Andromaly”: a behavioral malware detection framework for android devices
Empirical results suggest that the proposed framework, Andromaly, is effective in detecting malware on mobile devices in general and on Android in particular.
Crowdroid: behavior-based malware detection system for Android
The method is shown to be an effective means of isolating the malware and alerting the users of a downloaded malware, showing the potential for avoiding the spreading of a detected malware to a larger community.
DroidMat: Android Malware Detection through Manifest and API Calls Tracing
A static feature-based mechanism to provide a static analyst paradigm for detecting the Android malware and shows that the recall rate of the approach is better than one of well-known tool, Androguard, published in Black hat 2011, which focuses on Android malware analysis.
RiskRanker: scalable and accurate zero-day android malware detection
An automated system called RiskRanker is developed to scalably analyze whether a particular app exhibits dangerous behavior and is used to produce a prioritized list of reduced apps that merit further investigation, demonstrating the efficacy and scalability of riskRanker to police Android markets of all stripes.
Detecting energy-greedy anomalies and mobile malware variants
A power-aware malware-detection framework that monitors, detects, and analyzes previously unknown energy-depletion threats and achieves significant storage-savings without losing the detection accuracy, and a 99% true-positive rate in classifying mobile malware.
Comparative Analysis of Bayes and Lazy Classification Algorithms
This research work has analysed the performance of Bayesian and Lazy classifiers for classifying the files which are stored in the computer hard disk and observed that the lazy classifier is more efficient than Bayesian classifier.