Efficient Non-Linear Covert Channel Detection in TCP Data Streams

Nafea, Hanaa; Kifayat, Kashif; Shi, Qi; Qureshi, Kashif Naseer; Askwith, Bob

doi:10.1109/access.2019.2961609

Cited by 9 publications

(6 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ML for covert channel detection has been widely studied [54]. Nafea et al indicated that the SVM algorithm is the best approach for detecting covert data [55]. However, the success of ML approaches depends on the availability of the traffic samples that represent many types of covert channels, and not only traffic samples that represent specific types of covert channels.…”

Section: Covert Channels Detectionmentioning

confidence: 99%

Covert Channel Detection: Machine Learning Approaches

Elsadig

Gafar

2022

IEEE Access

View full text Add to dashboard Cite

The advanced development of computer networks and communication technologies has made covert communications easier to construct, faster, undetectable and more secure than ever. A covert channel is a path through which secret messages can be leaked by violating a system security policy. The detection of such dangerous, unwatchable, and hidden threats is still one of the most challenging aspects. This threat exploits methods that are not dedicated to communication purposes, meaning that traditional security measures fail to detect its existence. This review has introduced a brief introduction of covert channel definitions, types and developments, with a particular focus on detection techniques using machine learning (ML) approaches. It provides a thorough review of the most common covert channels and ML techniques that are used to counter them, as well as addressing their achievements and limitations. In addition, this paper introduces a comparative experimental study for some common ML approaches that are commonly used in this field. Accordingly, the performance of these classifiers was evaluated and reported. The paper concludes that our information is still at risk, nothing is said to be secured and more work on the detection of covert channels is required.INDEX TERMS Classification algorithms, covert channel detection, machine learning, covert traffic, covert storage channel, cover timing channel, deep learning, network traffic, network covert channels, overt traffic.

show abstract

Section: Covert Channels Detectionmentioning

confidence: 99%

Covert Channel Detection: Machine Learning Approaches

Elsadig

Gafar

2022

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The centralized database stores all the data for further processing and analyzing. The data is collected from the construction activities that start from planning and completion phases [31,32]. As security is one of the challenge especially when the data transmitted to the edge computing or to the cloud for further decision making, we proposed a IDS solution at the edge computing side to protect all the data coming from ground network and sensor devices.…”

Section: Security Model For the Sustainable Development-related Data Securitymentioning

confidence: 99%

Evaluation and of University Building Design Effect Based on Multisensor Perception and Data Security

2022

View full text Add to dashboard Cite

The development of the smart cities with new and integrated information and communication technologies has changed the traditional industries’ processes. One of the domains is construction industry which plays an important supporting role for the economic development of a country, but at the same time, the construction industry is also an industry with high energy consumption and high pollution. Therefore, in order to alleviate the contradiction between economic development and resources and the environment, the construction industry must achieve sustainable development and take the road of green construction. In order to carry out the evaluation of the design effect of colleges and universities, this paper introduces the multisensor perception and fuzzy comprehensive evaluation methods. First, through the design and analysis of the sensor perception system used in the building environment, the collection, acquisition, analysis, and processing of complex information of heterogeneous multiterminals are obtained. Secondly, cluster analysis and genetic algorithms are used in the processing and analysis process of building multiterminal sensor data. The security aspect is also taking into account to design the methods. The system test verifies the performance of the university building design effect evaluation model, which can provide a reference for the sustainable development of the construction industry.

show abstract

“…In the steganography process, SIP tags are used as the carrier of secret information, so the detection end first calculates the three-dimensional vector of each tag, and later obtains the distance vector between the vectors in the reconstruction space. Then, a comparison is made to determine whether it contains steganographic information after calculating the third-order feature value and the threshold [99]. The experimental results showed that the computational complexity was low, and was appropriate for online operation.…”

Section: Steganalysis Based On the Application Layermentioning

confidence: 99%

Steganography and Steganalysis in Voice over IP: A Review

Guo

Zhang

et al. 2021

Sensors

View full text Add to dashboard Cite

The rapid advance and popularization of VoIP (Voice over IP) has also brought security issues. VoIP-based secure voice communication has two sides: first, for legitimate users, the secret voice can be embedded in the carrier and transmitted safely in the channel to prevent privacy leakage and ensure data security; second, for illegal users, the use of VoIP Voice communication hides and transmits illegal information, leading to security incidents. Therefore, in recent years, steganography and steganography analysis based on VoIP have gradually become research hotspots in the field of information security. Steganography and steganalysis based on VoIP can be divided into two categories, depending on where the secret information is embedded: steganography and steganalysis based on voice payload or protocol. The former mainly regards voice payload as the carrier, and steganography or steganalysis is performed with respect to the payload. It can be subdivided into steganography and steganalysis based on FBC (fixed codebook), LPC (linear prediction coefficient), and ACB (adaptive codebook). The latter uses various protocols as the carrier and performs steganography or steganalysis with respect to some fields of the protocol header and the timing of the voice packet. It can be divided into steganography and steganalysis based on the network layer, the transport layer, and the application layer. Recent research results of steganography and steganalysis based on protocol and voice payload are classified in this paper, and the paper also summarizes their characteristics, advantages, and disadvantages. The development direction of future research is analyzed. Therefore, this research can provide good help and guidance for researchers in related fields.

show abstract

Efficient Non-Linear Covert Channel Detection in TCP Data Streams

Cited by 9 publications

References 16 publications

Covert Channel Detection: Machine Learning Approaches

Covert Channel Detection: Machine Learning Approaches

Evaluation and of University Building Design Effect Based on Multisensor Perception and Data Security

Steganography and Steganalysis in Voice over IP: A Review

Contact Info

Product

Resources

About