The industry has proved to be a boon for

The inter
connectivity, accessibility and open nature of IT industry has proved to be a boon
for both developers and users. But it comes with some threats as well. The most
significant one is the spread of malwares. Malware referred to as Malicious
software in any software application that can infiltrate into a system and
access or damage resources without the owner’s consent. Malware is a generic
term that may be viruses, worms, Trojan horses, spyware etc.

·      
Adware –
These are malwares which automatically show the advertisement to the user.

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·      
Virus   – It is the software which can harm your
computer by generating its copy automatically. These can be sent through
electronic mails, files etc.

·      
Worm –
They can be sent with the help of networks. They have tendency to self-replicate
itself and disseminate independently. On the other hand, viruses spread when
the user take part in this activity.

·      
Backdoors
– These are the software’s which bypass the login credentials without detected
by the owner. One or more software’s can be installed into system for future
use.  

 The potential harm that may result from the
malware requires the anti-malware authors to stay a step ahead of the malware
authors. This paper describes the use of LeNet like convolution neural network
for malware detection. Malware detection is a technique that is used to
distinguish between a malicious application from a benign one. Not only this,
as there are lots of categories of malwares, malware classification is also
important.

                                               
I.       
ChalLEnges in malwre detection

In present scenario we detect the malwares by signature
based methods and this process has been used by antivirus vendors from last
many years. Malware signature is a kind of algorithm which helps us to identify
the type of the malware. When we identify the malware then it is not easy to
identify its family as hackers use the polymorphic engine and metamorphic
engine to stay step ahead form the anti-virus programmers. Lack of open source
dataset for malware poses a great challenge since success of a machine learning
algorithm largely depends on the quantity and quality of the dataset used. New
malwares get inflected into the system with every tick of the clock. Malware
detection suffers with the problem akin to the problem in virus detection in biological
systems. The files look different but actually belong to the same family. The
malware authors use polymorphism by virtue of which the same binary file are
modified such that they look completely different. This makes use of
traditional techniques inefficient. Another challenge is the large number