Biblio

Network covert channels are used in various cyberattacks, including disclosure of sensitive information and enabling stealth tunnels for botnet commands. With time and technology, covert channels are becoming more prevalent, complex, and difficult to detect. The current methods for detection are protocol and pattern specific. This requires the investment of significant time and resources into application of various techniques to catch the different types of covert channels. This paper reviews several patterns of network storage covert channels, describes generation of network traffic dataset with covert channels, and proposes a generic, protocol-independent approach for the detection of network storage covert channels using a supervised machine learning technique. The implementation of the proposed generic detection model can lead to a reduction of necessary techniques to prevent covert channel communication in network traffic. The datasets we have generated for experimentation represent storage covert channels in the IP, TCP, and DNS protocols and are available upon request for future research in this area.

Network covert channels are currently typically seen as a security threat which can result in e.g. confidential data leakage or in a hidden data exchange between malicious parties. However, in this paper we want to investigate network covert channels from a less obvious angle i.e. we want to verify whether it is possible to use them as a green networking technique. Our observation is that usually covert channels utilize various redundant "resources" in network protocols e.g. unused/reserved fields that would have been transmitted anyway. Therefore, using such "resources" for legitimate transmissions can increase the total available bandwidth without sending more packets and thus offering potential energy savings. However, it must be noted that embedding and extracting processes related to data hiding consumes energy, too. That is why, in this paper we try to establish whether the potentially saved energy due to covert channels utilization exceeds the effort needed to establish and maintain covert data transmission. For this purpose, a proof-of-concept implementation has been created to experimentally measure the impact of network covert channels on resulting energy consumption. The obtained results show that the approach can be useful mostly under specific circumstances, i.e., when the total energy consumption of the network devices is already relatively high. Furthermore, the impact of different types of network covert channels on the energy consumption is examined to assess their usefulness from the green networking perspective.

Information security has become a growing concern. Computer covert channel which is regarded as an important area of information security research gets more attention. In order to detect these covert channels, a variety of detection algorithms are proposed in the course of the research. The algorithms of machine learning type show better results in these detection algorithms. However, the common machine learning algorithms have many problems in the testing process and have great limitations. Based on the deep learning algorithm, this paper proposes a new idea of network covert channel detection and forms a new detection model. On the one hand, this algorithmic model can detect more complex covert channels and, on the other hand, greatly improve the accuracy of detection due to the use of a new deep learning model. By optimizing this test model, we can get better results on the evaluation index.