A Multimode Network Steganography for Covert Wireless Communication Based on BitTorrent

Network steganography is a hidden communication technique, which utilizes the legitimate traffic as the vehicle to transfer the secret information covertly over the untrusted network. BitTorrent (BT) is one of the most prevalent P2P services for transmitting video files over wireless networks. An en...

Full description

Saved in:
Bibliographic Details
Published in:Security and communication networks 2020, Vol.2020 (2020), p.1-14
Main Authors: Wang, Mingqian, Ma, Changshen, Gu, Weijie
Format: Article
Language:English
Subjects:
Bandwidths
Data transmission
File sharing
Information transfer
Machine learning
Multimedia
Noise
Normal distribution
Peer to peer computing
Statistical analysis
Steganography
Traffic
Video data
Video transmission
Wireless communications
Wireless networks
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Network steganography is a hidden communication technique, which utilizes the legitimate traffic as the vehicle to transfer the secret information covertly over the untrusted network. BitTorrent (BT) is one of the most prevalent P2P services for transmitting video files over wireless networks. An enormous amount of video data is transmitted over BitTorrent traffic continuously, to make it potentially available for confidential information transfer. Hence, in this paper, the BitTorrent file-sharing service of P2P is chosen as the host for information hiding, and a multimode steganographic method based on Bitfield message is proposed. Taking advantage of BitTorrent cooperative transmission and the non-content-authentication mechanism of Bitfield message, the secret information is delivered during the exchange process of BitMapInfo between two peers. The steganographic mode is dynamically selected in view of the secret size, achieving adaptive bandwidth. The experimental results show that our scheme can resist statistical-based detection effectively and outperform the existing method by obtaining a lower degree of detection rate under machine learning-based steganalysis.
ISSN:1939-0114
1939-0122
DOI:10.1155/2020/8848315