MOBILE BOTNET DETECTION

MOBILE BOTNET DETECTION

Vishal Prabhakar Patare
Computer Engineering
Dy Patil College of Engineering
Ambi, Pune, India
vpatare22@gmail.com

Vishant Sharad Tambe
Computer Engineering
Dy Patil College of Engineering
Ambi, Pune, India
vishanttambe2001@gmail.com

Rajeshri Milind Bhole
Computer Engineering
Dy Patil College of Engineering
Ambi, Pune, India
rajeshrimbhole@gmail.com

Hitesh Gohad
Computer Engineering
Dy Patil College of Engineering
Ambi, Pune, India
hiteshgohad983441@gmail.com

Abstract— Now a days mobile has become tbe important aspect of persons life. Highly pervasive mobile operating systems are increasingly targeted for malware and botnet applications designed to turn mobile devices into bots that can be part of a larger botnet, they have become very common and therefore pose a serious threat that this botnet -Calls for more effective methods for detection on the android platform hence in this paper we present a deep learning approach for android botnet detection based on support vector machine. Botnets are fully integrated and optimized for mobile devices. The security of mobile devices is not designed to detect botnets and other threats. This allows botnets to infect machines and go unnoticed. In this paper, we propose an enhanced botnet detection technique called “Logdog” for mobile devices using log analysis. The effectiveness of this method has been demonstrated by experimentation on Android devices.

Keywords—, Botnet, video archives, Support Vector machine and Convolutional neural network, Python, etc.