IoT Malware Network Traffic Classification using Visual Representation and Deep Learning

10/04/2020
by   Gueltoum Bendiab, et al.
0

With the increase of IoT devices and technologies coming into service, Malware has risen as a challenging threat with increased infection rates and levels of sophistication. Without strong security mechanisms, a huge amount of sensitive data is exposed to vulnerabilities, and therefore, easily abused by cybercriminals to perform several illegal activities. Thus, advanced network security mechanisms that are able of performing a real-time traffic analysis and mitigation of malicious traffic are required. To address this challenge, we are proposing a novel IoT malware traffic analysis approach using deep learning and visual representation for faster detection and classification of new malware (zero-day malware). The detection of malicious network traffic in the proposed approach works at the package level, significantly reducing the time of detection with promising results due to the deep learning technologies used. To evaluate our proposed method performance, a dataset is constructed which consists of 1000 pcap files of normal and malware traffic that are collected from different network traffic sources. The experimental results of Residual Neural Network (ResNet50) are very promising, providing a 94.50 for detection of malware traffic.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 2

page 5

page 6

03/26/2021

ShellCore: Automating Malicious IoT Software Detection by Using Shell Commands Representation

The Linux shell is a command-line interpreter that provides users with a...
09/08/2021

Malware Squid: A Novel IoT Malware Traffic Analysis Framework using Convolutional Neural Network and Binary Visualisation

Internet of Things devices have seen a rapid growth and popularity in re...
04/16/2019

Decrypting SSL/TLS traffic for hidden threats detection

The paper presents an analysis of the main mechanisms of decryption of S...
07/29/2021

Zooming Into the Darknet: Characterizing Internet Background Radiation and its Structural Changes

Network telescopes or "Darknets" provide a unique window into Internet-w...
06/01/2021

MalPhase: Fine-Grained Malware Detection Using Network Flow Data

Economic incentives encourage malware authors to constantly develop new,...
03/05/2021

NF-GNN: Network Flow Graph Neural Networks for Malware Detection and Classification

Malicious software (malware) poses an increasing threat to the security ...
This week in AI

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