Malicious open nature of IT industry has

Malicious applications have been developed since the modern era rises. These
malicious softwares flourishing aggressively since last decade. One of the main
reasons for these high volumes is that in order to evade detection, malware
authors started using polymorphic and metamorphic kind of  techniques. As a result, traditional
signature-based approaches to detect malware are being insufficient against new
malware and the categorization of malware samples had become essential to know
the basis of the behavior of malware and to fight back cybercriminals. During
the last decade, solutions that fight against malicious software had begun
using machine learning approaches. Unfortunately, there are few opensource
datasets available for the academic community. One of the biggest datasets
available was released last year in a competition hosted on Kaggle with data
provided by Microsoft for the Big Data Innovators Gathering. This paper
presents two novel and scalable approaches using LeNet like Convolutional
Neural Networks (CNNs) to assign malware to its corresponding family.


Keywords— polymorphism,
convolution, activation, drebin Introduction

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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.

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.

– 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

 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.

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