Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.811759
Title: A novel component based framework for covert data leakage detection
Author: Nafea, H.
ISNI:       0000 0004 9347 8213
Awarding Body: Liverpool John Moores University
Current Institution: Liverpool John Moores University
Date of Award: 2020
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Abstract:
Cyber-attacks are causing billions of dollars of losses every year and data breaches are one of the major causes of these losses. The problem of data breach/leakage is attributed as a serious threat to organisations where any incident can inflict cost that is not only limited to monetary value but also can cause damage to organization goodwill, branding and reputation. Steganography is the practice of writing hidden messages via a medium in such a way that only the sender and the intended recipient know about the hidden message. Steganography is categorised into different forms including text, image, audio, video and network/protocol steganography. Network steganography is increasingly being used by malwares to facilitate the data leakage. This study focuses on aspects of network steganography at different levels of network packets. The existing tools for data leakage prevention and detection are often bypassed by the use of sophisticated techniques such as network steganography for stealing the data. This is due to several weaknesses of the existing detection systems. First, these techniques have high time and memory training complexities as well as large training data sets. These are challenging issues as the amount of data generated every second becomes very large in many realms. Secondly, the number of their false positives is high, making them inaccurate. Finally, there is a lack of a framework catering for needs such as raising alerts as well as data monitoring and updating/adapting of a threshold value used for checking packets for covert data. To overcome these weaknesses, this study proposes a novel framework that includes elements such as continuous data monitoring, threshold maintenance and alert notification. The study also proposes a model based on statistical measures to detect covert data leakages especially with regard to non-linear chaotic data. The main advantage of the proposed framework is its capability of providing more efficient results with tolerance/threshold values. Experiment outcomes indicate that the proposed framework performs better in comparison with state-of-the-art techniques in terms of accuracy and efficiency. Additionally, the proposed ii mathematical model can also be used for on-the-fly detection of covert data as opposed to offline processing methods.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.811759  DOI:
Keywords: QA75 Electronic computers. Computer science
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