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Android Malware Detection

  • Shymala Gowri Selvaganapathy
  • G. Sudha Sadasivam
  • Hema Priya N
  • Rajeshwari N
  • Dharani M
  • K. Karthik
Conference paper
  • 43 Downloads

Abstract

Smartphones and mobile tablets are rapidly becoming essential in daily life. Android has been the most popular mobile operating system since 2012. However, owing to the open nature of Android, countless malwares are intermingled with a large number of benign apps in Android markets that seriously threaten Android security. The botnet is an example of using good technologies for bad intentions. A botnet is a collection of Internet-connected devices, each of which is running one or more bots. The Bot devices include PCs, Internet of Things, mobile devices, etc. Botnets can be used to perform Distributed Denial of Service (DDoS attack), steal data, send spam and allow the attacker access to the device and its connection. To ensure the security of mobile devices, malwares have to be resolved. Malware analysis can be carried out using techniques like static, dynamic, behavioural, hybrid and code analysis. In this chapter, several machine learning techniques and classifiers are used to categorize mobile botnet detection.

Keywords

Android Android security Botnet Static Dynamic Hybrid Malware detection Machine learning techniques 

Abbreviation

DDoS

Distributed Denial of Service

CCTree

Categorical Clustering Tree

APK

Android Package

SVM

Support Vector Machine

ELM

Extreme Learning Machine

SLFN

Single-Layer Feedforward Neural Network

CNN

Convolutional Neural Network

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Shymala Gowri Selvaganapathy
    • 1
  • G. Sudha Sadasivam
    • 1
  • Hema Priya N
    • 1
  • Rajeshwari N
    • 1
  • Dharani M
    • 1
  • K. Karthik
    • 1
  1. 1.PSG College of TechnologyCoimbatoreIndia

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