Hidden and Uncontrolled - On the Emergence of Network Steganographic Threats

Network steganography is the art of hiding secret information within innocent network transmissions. Recent findings indicate that novel malware is increasingly using network steganography. Similarly, other malicious activities can profit from network steganography, such as data leakage or the exchange of pedophile data. This paper provides an introduction to network steganography and highlights its potential application for harmful purposes. We discuss the issues related to countering network steganography in practice and provide an outlook on further research directions and problems.Comment: 11 page

CloudMoV: Cloud-based Mobile Social TV

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Toward a General Parametric Model for Assessing the Impact of Video Transcoding on Objective Video Quality

Video transcoding can cause degradation to an original video. Currently, there is no general model that assesses the impact of video transcoding on video quality. Such a model could play a critical role in evaluating the quality of the transcoded video, and thereby optimizing delivery of video to end-users while meeting their expectations. The main contribution of this research is the development and substantiation of a general parametric model, called the Video Transcoding Objective-quality Model (VTOM), that provides an extensible video transcoding service selection mechanism, which takes into account both the format and characteristics of the original video and the desired output, i.e., viewing format with preferred quality of service. VTOM represents a mathematical function that uses a set of media-related parameters for the original video and desired output, including codec, bit rate, frame rate, and frame size to predict the quality of the transcoded video generated from a specific transcoding. VTOM includes four quality sub-models, each describing the impact of each of these parameters on objective video quality, as well as a weighted-product aggregation function that combines these quality sub-models with four additional error sub-models in a single function for assessing the overall video quality. I compared the predicted quality results generated from the VTOM with quality values generated from an existing objective-quality metric. These comparisons yielded results that showed good correlations, with low error values. VTOM helps the researchers and developers of video delivery systems and applications to calculate the degradation that video transcoding can cause on the fly, rather than evaluate it statistically using statistical methods that only consider the desired output. Because VTOM takes into account the quality of the input video, i.e., video format and characteristics, and the desired quality of the output video, it can be used for dynamic video transcoding service selection and composition. A number of quality metrics were examined and used in development of VTOM and its assessment. However, this research discovered that, to date, there are no suitable metrics in the literature for comparing two videos with different frame rates. Therefore, this dissertation defines a new metric, called Frame Rate Metric (FRM) as part of its contributions. FRM can use any frame-based quality metric for comparing frames from both videos. Finally, this research presents and adapts four Quality of Service (QoS)-aware video transcoding service selection algorithms. The experimental results showed that these four algorithms achieved good results in terms of time complexity, success ratio, and user satisfaction rate