A Method of Detecting Storage Based Network Steganography Using Machine Learning

被引：9

作者：

Cho, D. X. ^{[1
,2
]}

Thuong, D. T. H. ^{[1
]}

Dung, N. K. ^{[3
]}

机构：

[1] Posts & Telecommun Inst Technol Hanoi, Informat Secur Dept, Hanoi, Vietnam

[2] FPT Univ, Informat Secur Dept, Hanoi, Vietnam

[3] Thank Dong Univ, Informat Technol Dept, Hanoi, Vietnam

来源：

PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE OF INFORMATION AND COMMUNICATION TECHNOLOGY [ICICT-2019] | 2019年 / 154卷

关键词：

Information Security; network steganography; detection storage based network steganography; machine learning;

D O I：

10.1016/j.procs.2019.06.086

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Today, the techniques of network steganography are widely applied. In addition to the outstanding advantages about the ability to hide and transmit secret information, it has a huge disadvantage that is being exploited by hackers to transmit information or communicate with the control host. Network steganography storage method is one of the network steganography techniques that is being much applied. Due to the characteristics of the storage based network steganography are different from other network steganography techniques, the detection of this techniques is difficult. The traditional tools and methods used to detect steganography are difficult to detect the signs of steganographic packets that use this technique. Therefore, in this paper, the authors propose using machine learning to detect abnormal behavior of steganographic packets. (C) 2019 The Authors. Published by Elsevier Ltd.

引用

页码：543 / 548

页数：6

共 50 条

[31] Machine Learning for Detecting Brute Force Attacks at the Network Level [J].

Najafabadi, Maryam M. ;

Khoshgoftaar, Taghi M. ;

Kemp, Clifford ;

Seliya, Naeem ;

Zuech, Richard .

2014 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOENGINEERING (BIBE), 2014, :379-385

[32] Detecting Network Anomalies in NetFlow Traffic with Machine Learning Algorithms [J].

Quoc Vo ;

Ea, Philippe ;

Salem, Osman ;

Mehaoua, Ahmed .

2024 IEEE 49TH CONFERENCE ON LOCAL COMPUTER NETWORKS, LCN 2024, 2024,

[33] New feature Selection method based on neural network and machine learning [J].

Challita, Nicole ;

Khalil, Mohamad ;

Beauseroy, Pierre .

2016 IEEE INTERNATIONAL MULTIDISCIPLINARY CONFERENCE ON ENGINEERING TECHNOLOGY (IMCET), 2016, :81-84

[34] Machine Learning Based Neural Network Solving Methods for the FDTD Method [J].

Yao, He Ming ;

Jiang, Li Jun .

2018 IEEE ANTENNAS AND PROPAGATION SOCIETY INTERNATIONAL SYMPOSIUM ON ANTENNAS AND PROPAGATION & USNC/URSI NATIONAL RADIO SCIENCE MEETING, 2018, :2321-2322

[35] Data fusion method for wireless sensor network based on machine learning [J].

Wu, Mi .

JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING, 2023, 23 (01) :361-373

[36] Analysis of synchronized storage method for multimedia key areas based on machine learning [J].

Lei Chen .

Multimedia Tools and Applications, 2021, 80 :22685-22700

[37] Analysis of synchronized storage method for multimedia key areas based on machine learning [J].

Chen, Lei .

MULTIMEDIA TOOLS AND APPLICATIONS, 2021, 80 (15) :22685-22700

[38] Underground hydrogen storage: A recovery prediction using pore network modeling and machine learning [J].

Zhao, Qingqi ;

Wang, Hongsheng ;

Chen, Cheng .

FUEL, 2024, 357

[39] Naming Scheme Using NLP Machine Learning Method for Network Weather Monitoring System Based on ICN [J].

Mochida, Toru ;

Nozaki, Daichi ;

Okamoto, Koki ;

Qi, Xin ;

Wen, Zheng ;

Sato, Takuro ;

Yu, Keping .

2017 20TH INTERNATIONAL SYMPOSIUM ON WIRELESS PERSONAL MULTIMEDIA COMMUNICATIONS (WPMC), 2017, :428-434

[40] Detecting BGP Anomalies Using Machine Learning Techniques [J].

Ding, Qingye ;

Li, Zhida ;

Batta, Prerna ;

Trajkovic, Ljiljana .

2016 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC), 2016, :3352-3355

← 1 2 3 4 5 →