“Better be despised
for too anxious apprehensions, than ruined by too confident security.” — Edmund
Burke

Cell phones gather
and incorporate an expanding measure of confidential data. We must control this
strategy to provide end-user protection.
Effective countermeasures are being produced and connected to cell phones, from
Android layers security to the dispersal of data to end clients. All levels
from design to end-user usage contains
best practices. These dangers can disturb the Smartphone operation, and transmit or change client
information. So applications must ensure protection and integrity of the data
they handle.

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An ill intended user
can attempt to transfer data on various communication channels like Wi-Fi, Bluetooth. This kind of attack is very
common to cell phones, yet existing solutions are not capable to hinder these
attacks. Security issues identified with Bluetooth on cell phones have been
considered and have demonstrated various issues on various devices. One simple
to abuse vulnerability: unregistered services don’t require verification, and
mentioned applications have a virtual serial port used to control the phone. An
attacker just expected to interface with the port to take full control of the
phone

This work presents
suggested strategies for machine learning based malware classification and
identification, and the rules for its usage. Also, the thesis performed can be
helpful as a base for additionally research about in the field of malware
examination with machine learning methods.

Permission-based malware detection
approach is necessary for any way to deal
with mobile security. Unfortunately, most clients don’t keep their security
software, applications and device with latest security features — and with a huge amount of monetary benefits to be produced
using data stealing techniques, malware authors are always finding better
approaches to surpass security conditions. This solution focuses on network-based approach along with permission-based malware detection technique
that is always on solution and user cannot turn it off and is always ahead in
terms of latest threats and can distinguish different signatures inside system
traffic to recognize specific sorts of malware and lessen the rate of false
positives.