{"id":{"repo_id":"bradford","oai_identifier":"oai:bradscholars.brad.ac.uk:10454/15863"},"canonical_url":"https://search.dev.ndltd.org/etd/bradford/oai:bradscholars.brad.ac.uk:10454/15863","repository":{"repo_id":"bradford","name":"University of Bradford","base_url":"https://bradscholars.brad.ac.uk/oai/request"},"display":{"title":"A Heuristic Featured Based Quantification Framework for Efficient Malware Detection. Measuring the Malicious intent of a file using anomaly probabilistic scoring and evidence combinational theory with fuzzy hashing for malware detection in Portable Executable files","abstract":"Malware is still one of the most prominent vectors through which computer networks and systems are compromised. A compromised computer system or network provides data and or processing resources to the world of cybercrime. With cybercrime projected to cost the world $6 trillion by 2021, malware is expected to continue being a growing challenge. Statistics around malware growth over the last decade support this theory as malware numbers enjoy almost an exponential increase over the period. Recent reports on the complexity of the malware show that the fight against malware as a means of building more resilient cyberspace is an evolving challenge. Compounding the problem is the lack of cyber security expertise to handle the expected rise in incidents. This thesis proposes advancing automation of the malware static analysis and detection to improve the decision-making confidence levels of a standard computer user in regards to a file’s malicious status. Therefore, this work introduces a framework that relies on two novel approaches to score the malicious intent of a file. The first approach attaches a probabilistic score to heuristic anomalies to calculate an overall file malicious score while the second approach uses fuzzy hashes and evidence combination theory for more efficient malware detection. The approaches’ resultant quantifiable scores measure the malicious intent of the file. The designed schemes were validated using a dataset of “clean” and “malicious” files. The results obtained show that the framework achieves true positive – false positive detection rate “trade-offs” for efficient malware detection.","abstract_html":"Malware is still one of the most prominent vectors through which computer networks and systems are compromised. A compromised computer system or network provides data and or processing resources to the world of cybercrime. With cybercrime projected to cost the world $6 trillion by 2021, malware is expected to continue being a growing challenge. Statistics around malware growth over the last decade support this theory as malware numbers enjoy almost an exponential increase over the period. Recent reports on the complexity of the malware show that the fight against malware as a means of building more resilient cyberspace is an evolving challenge. Compounding the problem is the lack of cyber security expertise to handle the expected rise in incidents. This thesis proposes advancing automation of the malware static analysis and detection to improve the decision-making confidence levels of a standard computer user in regards to a file’s malicious status. Therefore, this work introduces a framework that relies on two novel approaches to score the malicious intent of a file. The first approach attaches a probabilistic score to heuristic anomalies to calculate an overall file malicious score while the second approach uses fuzzy hashes and evidence combination theory for more efficient malware detection. The approaches’ resultant quantifiable scores measure the malicious intent of the file. The designed schemes were validated using a dataset of “clean” and “malicious” files. The results obtained show that the framework achieves true positive – false positive detection rate “trade-offs” for efficient malware detection.","abstract_has_math":false,"creators":["Namanya, Anitta P."],"institution":"University of Bradford","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Awan, Irfan U.","Disso, Jules P.","Cullen, Andrea J."],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016","date_published":"2016","updated_at":"2026-07-24T01:15:08Z","subjects":["Malware detection","Evidence combinational theory","Hashes","File features","Anomalies","Probabilistic scoring","Automated static analyses","Malware","Portable executable (PE)"],"languages":["en"],"rights":["\"Creative
The University of Bradford theses are licenced under a Creative Commons Licence."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10454/15863","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Awan, Irfan U.","Disso, Jules P.","Cullen, Andrea J."]},{"key":"dc:creator","label":"Author","values":["Namanya, Anitta P."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2018-05-14T15:04:40Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2018-05-14T15:04:40Z"]},{"key":"dc:date.issued","label":"Date","values":["2016"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["School of Electrical Engineering and Computer Science"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Bradford"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["doctoral"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["PhD"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Malware detection","Evidence combinational theory","Hashes","File features","Anomalies","Probabilistic scoring","Automated static analyses","Malware","Portable executable (PE)"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["\"Creative
The University of Bradford theses are licenced under a Creative Commons Licence."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10454/15863"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Malware is still one of the most prominent vectors through which computer networks and systems are compromised. A compromised computer system or network provides data and or processing resources to the world of cybercrime. With cybercrime projected to cost the world $6 trillion by 2021, malware is expected to continue being a growing challenge. Statistics around malware growth over the last decade support this theory as malware numbers enjoy almost an exponential increase over the period. Recent reports on the complexity of the malware show that the fight against malware as a means of building more resilient cyberspace is an evolving challenge. Compounding the problem is the lack of cyber security expertise to handle the expected rise in incidents. This thesis proposes advancing automation of the malware static analysis and detection to improve the decision-making confidence levels of a standard computer user in regards to a file’s malicious status. Therefore, this work introduces a framework that relies on two novel approaches to score the malicious intent of a file. The first approach attaches a probabilistic score to heuristic anomalies to calculate an overall file malicious score while the second approach uses fuzzy hashes and evidence combination theory for more efficient malware detection. The approaches’ resultant quantifiable scores measure the malicious intent of the file. The designed schemes were validated using a dataset of “clean” and “malicious” files. The results obtained show that the framework achieves true positive – false positive detection rate “trade-offs” for efficient malware detection."]},{"key":"dc:title","label":"Title","values":["A Heuristic Featured Based Quantification Framework for Efficient Malware Detection. Measuring the Malicious intent of a file using anomaly probabilistic scoring and evidence combinational theory with fuzzy hashing for malware detection in Portable Executable files"]}]}],"canonical_facts":{"dc:contributor.advisor":["Awan, Irfan U.","Disso, Jules P.","Cullen, Andrea J."],"dc:creator":["Namanya, Anitta P."],"dc:date.accessioned":["2018-05-14T15:04:40Z"],"dc:date.available":["2018-05-14T15:04:40Z"],"dc:date.issued":["2016"],"dc:description.abstract":["Malware is still one of the most prominent vectors through which computer networks and systems are compromised. A compromised computer system or network provides data and or processing resources to the world of cybercrime. With cybercrime projected to cost the world $6 trillion by 2021, malware is expected to continue being a growing challenge. Statistics around malware growth over the last decade support this theory as malware numbers enjoy almost an exponential increase over the period. Recent reports on the complexity of the malware show that the fight against malware as a means of building more resilient cyberspace is an evolving challenge. Compounding the problem is the lack of cyber security expertise to handle the expected rise in incidents. This thesis proposes advancing automation of the malware static analysis and detection to improve the decision-making confidence levels of a standard computer user in regards to a file’s malicious status. Therefore, this work introduces a framework that relies on two novel approaches to score the malicious intent of a file. The first approach attaches a probabilistic score to heuristic anomalies to calculate an overall file malicious score while the second approach uses fuzzy hashes and evidence combination theory for more efficient malware detection. The approaches’ resultant quantifiable scores measure the malicious intent of the file. The designed schemes were validated using a dataset of “clean” and “malicious” files. The results obtained show that the framework achieves true positive – false positive detection rate “trade-offs” for efficient malware detection."],"dc:identifier.uri":["http://hdl.handle.net/10454/15863"],"dc:language.iso":["en"],"dc:publisher.department":["School of Electrical Engineering and Computer Science"],"dc:publisher.institution":["University of Bradford"],"dc:rights":["\"Creative
The University of Bradford theses are licenced under a Creative Commons Licence."],"dc:subject":["Malware detection","Evidence combinational theory","Hashes","File features","Anomalies","Probabilistic scoring","Automated static analyses","Malware","Portable executable (PE)"],"dc:title":["A Heuristic Featured Based Quantification Framework for Efficient Malware Detection. Measuring the Malicious intent of a file using anomaly probabilistic scoring and evidence combinational theory with fuzzy hashing for malware detection in Portable Executable files"],"dc:type":["Thesis"],"dc:type.qualificationlevel":["doctoral"],"dc:type.qualificationname":["PhD"]},"updated_at":"2026-07-24T01:15:08Z"}