A Novel Video Steganography-Based Botnet Communication Model in Telegram SNS Messenger

Kwak, Minkyung; Cho, Youngho

doi:10.3390/sym13010084

Cited by 11 publications

(5 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The purpose of steganography is to secretly convey messages that others do not notice on the cover stream. This deceptive method prevents a security system from noticing whether the stream is an important message [16,17]. The initial compressed video steganography research began with a video watermarking method that manipulates the lower bits of the brightness value in the raw video domain, rather than in the compressed domain, to make it visually indistinguishable.…”

Section: Compressed Video Steganography Methodsmentioning

confidence: 99%

Deceptive Techniques to Hide a Compressed Video Stream for Information Security

Heo

2021

Sensors

View full text Add to dashboard Cite

With the recent development of video compression methods, video transmission on traditional devices and video distribution using networks has increased in various devices such as drones, IP cameras, and small IoT devices. As a result, the demand for encryption techniques such as MPEG-DASH for transmitting streams over networks is increasing. These video stream security methods guarantee stream confidentiality. However, they do not hide the fact that the encrypted stream is being transmitted over the network. Considering that sniffing attacks can analyze the entropy of the stream and scan huge amounts of traffic on the network, to solve this problem, the deception method is required, which appears unencrypted but a confidential stream. In this paper, we propose the new deception method that utilizes standard NAL unit rules of video codec, where the unpromised device shows the cover video and the promised device shows the secret video for deceptive security. This method allows a low encryption cost and the stream to dodge entropy-based sniffing scan attacks. The proposed stream shows that successful decoding using five standard decoders and processing performance was 61% faster than the conventional encryption method in the test signal conformance set. In addition, a network encrypted stream scan method the HEDGE showed classification results that our stream is similar to a compressed video.

show abstract

Section: Compressed Video Steganography Methodsmentioning

confidence: 99%

Deceptive Techniques to Hide a Compressed Video Stream for Information Security

Heo

2021

Sensors

View full text Add to dashboard Cite

show abstract

“…When considering such late attacks, it was found that many checks on different AI calculations of the CICIDS2017 datasets were not sufficient for port checks and DDoS attacks. Kwak et al [16], proposed a video steganography botnet model. In addition, they plan to use another video steganography technology based on the payload method (DECM: Frequency Division Embedded Component Method), which can use two open devices VirtualDub and Stegano to implant significantly more privileges than existing tools information.…”

Section: Related Workmentioning

confidence: 99%

A Machine Learning-Based Classification and Prediction Technique for DDoS Attacks

Ismail

Mohmand

Hussain³

et al. 2022

IEEE Access

View full text Add to dashboard Cite

Distributed network attacks are often referred to as Distributed Denial of Service (DDoS) attacks. These attacks take advantage of specific limitations that apply to any arrangement asset, such as the framework of the authorized organization's site. In the existing research study, the author worked on an old KDD dataset. It is necessary to work with the latest dataset to identify the current state of DDoS attacks. This paper, used a machine learning approach for DDoS attack types classification and prediction. For this purpose, used Random Forest and XGBoost classification algorithms. To access the research proposed a complete framework for DDoS attacks prediction. For the proposed work, THE UNWS-np-15 dataset from GitHub and python used as a simulator. After applying the machine learning models generated a confusion matrix for model performance identification. In the first classification, the results showed that both Precision (PR) and Recall (RE) are 89% for Random Forest Algorithm. The average Accuracy (AC) of model is 89% which is extremely good. In the second classification, the results showed that both Precision (PR) and Recall (RE) are 90% for XGBoost. The average Accuracy (AC) of model is 90%. By comparing work to existing research work, the accuracy of defect determination improved as compare to existing research work which is 85% and 79%.

show abstract

“…Computer steganography -hiding data or messages in objects of the program structure of a computer or computer network (in computer programs, in protocols, in sectors of disk space, etc.) [29][30][31][32].…”

Section: Technologies Of Stego-embeddingmentioning

confidence: 99%

An Approach for Stego-Insider Detection Based on a Hybrid NoSQL Database

Kotenko

Krasov

Ushakov

et al. 2021

JSAN

View full text Add to dashboard Cite

One of the reasons for the implementation of information security threats in organizations is the insider activity of its employees. There is a big challenge to detect stego-insiders-employees who create stego-channels to secretly receive malicious information and transfer confidential information across the organization’s perimeter. Especially presently, with great popularity of wireless sensor networks (WSNs) and Internet of Things (IoT) devices, there is a big variety of information that could be gathered and processed by stego-insiders. Consequently, the problem arises of identifying such intruders and their transmission channels. The paper proposes an approach to solving this problem. The paper provides a review of the related works in terms of insider models and methods of their identification, including techniques for handling insider attacks in WSN, as well methods of embedding and detection of stego-embeddings. This allows singling out the basic features of stego-insiders, which could be determined by their behavior in the network. In the interests of storing these attributes of user behavior, as well as storing such attributes from large-scale WSN, a hybrid NoSQL database is created based on graph and document-oriented approaches. The algorithms for determining each of the features using the NoSQL database are specified. The general scheme of stego-insider detection is also provided. To confirm the efficiency of the approach, an experiment was carried out on a real network. During the experiment, a database of user behavior was collected. Then, user behavior features were retrieved from the database using special SQL queries. The analysis of the results of SQL queries is carried out, and their applicability for determining the attribute is justified. Weak points of the approach and ways to improve them are indicated.

show abstract

A Novel Video Steganography-Based Botnet Communication Model in Telegram SNS Messenger

Cited by 11 publications

References 35 publications

Deceptive Techniques to Hide a Compressed Video Stream for Information Security

Deceptive Techniques to Hide a Compressed Video Stream for Information Security

A Machine Learning-Based Classification and Prediction Technique for DDoS Attacks

An Approach for Stego-Insider Detection Based on a Hybrid NoSQL Database

Contact Info

Product

Resources

About