{"id":{"repo_id":"bradford","oai_identifier":"oai:bradscholars.brad.ac.uk:10454/7331"},"canonical_url":"https://search.dev.ndltd.org/etd/bradford/oai:bradscholars.brad.ac.uk:10454/7331","repository":{"repo_id":"bradford","name":"University of Bradford","base_url":"https://bradscholars.brad.ac.uk/oai/request"},"display":{"title":"A self-healing framework to combat cyber attacks. Analysis and development of a self-healing mitigation framework against controlled malware attacks for enterprise networks.","abstract":"Cybercrime costs a total loss of about $338 billion annually which makes it one of the most profitable criminal activities in the world. Controlled malware (Botnet) is one of the most prominent tools used by cybercriminals to infect, compromise computer networks and steal important information. Infecting a computer is relatively easy nowadays with malware that propagates through social networking in addition to the traditional methods like SPAM messages and email attachments. In fact, more than 1/4 of all computers in the world are infected by malware which makes them viable for botnet use. This thesis proposes, implements and presents the Self-healing framework that takes inspiration from the human immune system. The designed self-healing framework utilises the key characteristics and attributes of the nature’s immune system to reverse botnet infections. It employs its main components to heal the infected nodes. If the healing process was not successful for any reason, it immediately removes the infected node from the Enterprise’s network to a quarantined network to avoid any further botnet propagation and alert the Administrators for human intervention. The designed self-healing framework was tested and validated using different experiments and the results show that it efficiently heals the infected workstations in an Enterprise network.","abstract_html":"Cybercrime costs a total loss of about $338 billion annually which makes it one of the most profitable criminal activities in the world. Controlled malware (Botnet) is one of the most prominent tools used by cybercriminals to infect, compromise computer networks and steal important information. Infecting a computer is relatively easy nowadays with malware that propagates through social networking in addition to the traditional methods like SPAM messages and email attachments. In fact, more than 1/4 of all computers in the world are infected by malware which makes them viable for botnet use. This thesis proposes, implements and presents the Self-healing framework that takes inspiration from the human immune system. The designed self-healing framework utilises the key characteristics and attributes of the nature’s immune system to reverse botnet infections. It employs its main components to heal the infected nodes. If the healing process was not successful for any reason, it immediately removes the infected node from the Enterprise’s network to a quarantined network to avoid any further botnet propagation and alert the Administrators for human intervention. The designed self-healing framework was tested and validated using different experiments and the results show that it efficiently heals the infected workstations in an Enterprise network.","abstract_has_math":false,"creators":["Alhomoud, Adeeb M."],"institution":"University of Bradford","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Awan, Irfan U.","Pagna Disso, Jules F."],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-07-08","date_published":"2015-07-08","updated_at":"2026-07-24T01:13:57Z","subjects":["Self-healing, Malware, Botnets, Cyber attacks, Enterprise networks"],"languages":["en"],"rights":["<a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-nd/3.0/\"><img alt=\"Creative Commons License\" style=\"border-width:0\" src=\"http://i.creativecommons.org/l/by-nc-nd/3.0/88x31.png\" /></a><br />The University of Bradford theses are licenced under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-nd/3.0/\">Creative Commons Licence</a>."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10454/7331","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.","Pagna Disso, Jules F."]},{"key":"dc:creator","label":"Author","values":["Alhomoud, Adeeb M."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2015-07-08T15:54:50Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2015-07-08T15:54:50Z"]},{"key":"dc:date.issued","label":"Date","values":["2015-07-08"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["Department of Electrical Engineering and Computer Science, School of Engineering and Informatics"]},{"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":["Self-healing, Malware, Botnets, Cyber attacks, Enterprise networks"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["<a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-nd/3.0/\"><img alt=\"Creative Commons License\" style=\"border-width:0\" src=\"http://i.creativecommons.org/l/by-nc-nd/3.0/88x31.png\" /></a><br />The University of Bradford theses are licenced under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-nd/3.0/\">Creative Commons Licence</a>."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10454/7331"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Cybercrime costs a total loss of about $338 billion annually which makes it one of the most profitable criminal activities in the world. Controlled malware (Botnet) is one of the most prominent tools used by cybercriminals to infect, compromise computer networks and steal important information. Infecting a computer is relatively easy nowadays with malware that propagates through social networking in addition to the traditional methods like SPAM messages and email attachments. In fact, more than 1/4 of all computers in the world are infected by malware which makes them viable for botnet use. This thesis proposes, implements and presents the Self-healing framework that takes inspiration from the human immune system. The designed self-healing framework utilises the key characteristics and attributes of the nature’s immune system to reverse botnet infections. It employs its main components to heal the infected nodes. If the healing process was not successful for any reason, it immediately removes the infected node from the Enterprise’s network to a quarantined network to avoid any further botnet propagation and alert the Administrators for human intervention. The designed self-healing framework was tested and validated using different experiments and the results show that it efficiently heals the infected workstations in an Enterprise network."]},{"key":"dc:title","label":"Title","values":["A self-healing framework to combat cyber attacks. Analysis and development of a self-healing mitigation framework against controlled malware attacks for enterprise networks."]}]}],"canonical_facts":{"dc:contributor.advisor":["Awan, Irfan U.","Pagna Disso, Jules F."],"dc:creator":["Alhomoud, Adeeb M."],"dc:date.accessioned":["2015-07-08T15:54:50Z"],"dc:date.available":["2015-07-08T15:54:50Z"],"dc:date.issued":["2015-07-08"],"dc:description.abstract":["Cybercrime costs a total loss of about $338 billion annually which makes it one of the most profitable criminal activities in the world. Controlled malware (Botnet) is one of the most prominent tools used by cybercriminals to infect, compromise computer networks and steal important information. Infecting a computer is relatively easy nowadays with malware that propagates through social networking in addition to the traditional methods like SPAM messages and email attachments. 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Analysis and development of a self-healing mitigation framework against controlled malware attacks for enterprise networks."],"dc:type":["Thesis"],"dc:type.qualificationlevel":["doctoral"],"dc:type.qualificationname":["PhD"]},"updated_at":"2026-07-24T01:13:57Z"}