On a Bayesian Approach to Malware Detection and Classification through n-gram Profiles

11/23/2020
by   José A. Perusquía, et al.
0

Detecting and correctly classifying malicious executables has become one of the major concerns in cyber security, especially because traditional detection systems have become less effective with the increasing number and danger of threats found nowadays. One way to differentiate benign from malicious executables is to leverage on their hexadecimal representation by creating a set of binary features that completely characterise each executable. In this paper we present a novel supervised learning Bayesian nonparametric approach for binary matrices, that provides an effective probabilistic approach for malware detection. Moreover, and due to the model's flexible assumptions, we are able to use it in a multi-class framework where the interest relies in classifying malware into known families. Finally, a generalisation of the model which provides a deeper understanding of the behaviour across groups for each feature is also developed.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 18

page 24

page 25

page 26

page 27

03/02/2019

Detecting and Classifying Android Malware using Static Analysis along with Creator Information

Thousands of malicious applications targeting mobile devices, including ...
11/10/2021

A framework for comprehensible multi-modal detection of cyber threats

Detection of malicious activities in corporate environments is a very co...
09/22/2018

DeepOrigin: End-to-End Deep Learning for Detection of New Malware Families

In this paper, we present a novel method of differentiating known from p...
12/27/2018

Malicious Software Detection and Classification utilizing Temporal-Graphs of System-call Group Relations

In this work we propose a graph-based model that, utilizing relations be...
11/08/2021

OMD: Orthogonal Malware Detection Using Audio, Image, and Static Features

With the growing number of malware and cyber attacks, there is a need fo...
08/21/2018

MLPdf: An Effective Machine Learning Based Approach for PDF Malware Detection

Due to the popularity of portable document format (PDF) and increasing n...
08/15/2016

SandBlaster: Reversing the Apple Sandbox

In order to limit the damage of malware on Mac OS X and iOS, Apple uses ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.