Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.700790
Title: Advances in string algorithms for information security applications
Author: Aljamea, Mudhi Mohammed
ISNI:       0000 0004 5988 850X
Awarding Body: King's College London
Current Institution: King's College London (University of London)
Date of Award: 2016
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Abstract:
This thesis focuses on introducing novel algorithms in information security through studying successful algorithms in bioinformatics and adapting them to solve some open problems in information security. Although, the problems in both bioinformatics and information security are different, yet, they might be considered very similar when it comes to identifying and solving them using Stringology techniques. Different successful bioinformatics algorithms have been studied and introduced to solve different security problems such as malware detection, biometrics and big data. Firstly, we present a dynamic computer malware detection model; a novel approach for detecting malware code embedded in different types of computer files, with consistency, accuracy and in high speed without excessive memory usages. This model was inspired by REAL; an efficient read aligner used by next generation sequencing for processing biological data. In addition, we introduce a novel algorithmic approach to detect malicious URLs in image secret communications. Secondly, we also focus on biometrics, specifically fingerprint which is considered to be one of the most reliable and used technique to identify individuals. In particular, we introduce a new fingerprint matching technique, which matches the fingerprint information using circular approximate string matching to solve the rotation problem overcoming the previous methods’ obstacles. Finally, we conclude with an algorithmic approach to analyse big data readings from smart meters to confirm some privacy issues concerns.
Supervisor: Crochemore, Maxime ; Iliopoulos, Costas Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.700790  DOI: Not available
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