Steganography in Social Networks Based on Behavioral Correlation

Hu, Yinghong; Wang, Zichi; Zhang, Xinpeng

doi:10.1080/02564602.2020.1721340

Cited by 8 publications

(6 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hu et al proposed behavioral steganography based on the 0-1 knapsack algorithm. They used CMI coding to solve the high bit error rate when transmitting a large number of continuous and identical confidential information [6].…”

Section: -1 Knapsack Problemmentioning

confidence: 99%

“…Steganography [2,6], known as hiding the existence of data, can be defined as the technology of using information embedding algorithms to hide confidential information on non-secret networks. Continuous improvements in steganalysis techniques reduce the reliability of traditional steganography day by day.…”

Section: Introductionmentioning

confidence: 99%

“…Çıtlak et al analyzed the existing methods for detecting spam accounts in the Twitter network and compared the strengths and weaknesses of the methods for distinguishing real users and fake users [1]. Hu et al proposed a behavioral correlation-based stenographic method for social networks [6]. Kantartopoulos et al analyzed hostile attacks based on AdaBoost on fake Twitter accounts using machine learning and suggested the use of K-NN for defense [7].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Behavioral Steganography in Social Networks

Gençoğlu

2022

Türk Doğa Ve Fen Dergisi

View full text Add to dashboard Cite

Recently, using human behavior to hide the existence of information has been at the center of steganography research. In this study, a behavioral steganography algorithm using CMI (Coded Signal Inversion) coding is proposed to minimize the high bit error rate that occurs when transmitting a large number of continuous and identical confidential information in the knapsack algorithm, which is used to improve information transmission efficiency and flexibility of transmission mode in social networks. In the proposed algorithm; Data redundancy is reduced by reducing the number of mutual friends of the sender and each receiver. Then, the proposed algorithm was applied and the results were analyzed. Experimental analysis shows that this scheme improves the practical value of behavioral steganography in social networks and has high security.

show abstract

Section: -1 Knapsack Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Behavioral Steganography in Social Networks

Gençoğlu

2022

Türk Doğa Ve Fen Dergisi

View full text Add to dashboard Cite

show abstract

“…References [7,16,18] collect and fit the user's behavior frequency to avoid anomalies caused by the sender's behaviors. For this reason, we take the same method to ensure resistance to detection.…”

Section: Experiments and Evaluation On Detection Resistancementioning

confidence: 99%

“…e behavioral steganography method of marking "love" attempts to send message in the WeChat Moments, which is widely used in China, but there are insufficient embedding capacity and detection resistance. In view of the problem that the relationship between friends is not considered in [17], Hu et al [18] use the behavioral correlation between sender and friend to calculate the reasonable probability of marking "love" on a social network, which improves the security, but the embedding capacity needs to be improved. Regarding the "prisoner model," the security issue between sender and receiver is not considered; Yang et al [7] give a constraint framework to ensure content security and behavioral security and provide a reference method for the behavioral security of social network steganography.…”

Section: Introductionmentioning

confidence: 99%

High-Capacity Robust Behavioral Steganography Method Based on Timestamp Modulation across Social Internet of Things

Zhang¹,

Luo²,

Zhang³

et al. 2021

Security and Communication Networks

View full text Add to dashboard Cite

Social Internet of Things (SIoT) is an emerging field that combines IoT and Internet, which can provide many novel and convenient application scenarios but still faces challenges in data privacy protection. In this paper, we propose a robust behavioral steganography method with high embedding capacity across social networks based on timestamp modulation. Firstly, the IoT devices on the sending end modulate the secret message to be embedded into a timestamp by using the common property on social networks. Secondly, the accounts of multiple social networks are used as the vertices, and the timestamp mapping relationship generated by the interaction behaviors between them is used as the edges to construct a directed secret message graph across social networks. Then, the frequency of interaction behaviors generated by users of mainstream social networks is analyzed; the corresponding timestamps and social networks are used to implement interaction behaviors based on the secret message graph and the frequency of interaction behaviors. Next, we analyze the frequency of interaction behaviors generated by users in mainstream social networks, implement the interaction behaviors according to the secret message graph and the frequency of interaction behaviors in the corresponding timestamps and social networks, and combine the redundant mapping control to complete the embedding of secret message. Finally, the receiver constructs the timestamp mapping relationship through the shared account, key, and other parameters to achieve the extraction of secret message. The algorithm is robust and does not have the problem that existing multimedia-based steganography methods are difficult to extract the embedded messages completely. Compared with existing graph theory-based social network steganography methods, using timestamps and behaviors frequencies to hide message in multiple social networks increases the cost of detecting covert communication and improves concealment of steganography. At the same time, the algorithm uses a directed secret message graph to increase the number of bits carried by each behavior and improves the embedding capacity. A large number of tests have been conducted on mainstream social networks such as Facebook, Twitter, and Weibo. The results show that the proposed method successfully distributes secret message to multiple social networks and achieves complete extraction of embedded message at the receiving end. The embedding capacity is increased by 1.98–4.89 times compared with the existing methods SSN, NGTASS, and SGSIR.

show abstract

Applications of Correlation

Chattamvelli

2024

Synthesis Lectures on Mathematics &Amp; Statistics

View full text Add to dashboard Cite

Steganography in Social Networks Based on Behavioral Correlation

Cited by 8 publications

References 16 publications

Behavioral Steganography in Social Networks

Behavioral Steganography in Social Networks

High-Capacity Robust Behavioral Steganography Method Based on Timestamp Modulation across Social Internet of Things

Applications of Correlation

Contact Info

Product

Resources

About