{"id":{"repo_id":"uoit","oai_identifier":"oai:ontariotechu.scholaris.ca:10155/1249"},"canonical_url":"https://search.dev.ndltd.org/etd/uoit/oai:ontariotechu.scholaris.ca:10155/1249","repository":{"repo_id":"uoit","name":"Ontario Institute of Technology","base_url":"https://ontariotechu.scholaris.ca/server/oai/request"},"display":{"title":"Polymorphic Adversarial DDoS attack on IDS using GAN","abstract":"IDS are essential components in preventing malicious traffic from penetrating networks. IDS have been rapidly enhancing their detection ability using ML algorithms. As a result, attackers look for new methods to evade the IDS. Polymorphic attacks are favorites among the attackers as they can bypass the IDS. GAN is a method proven in generating various forms of data. It is becoming popular among security researchers as it can produce indistinguishable data from the original data. I proposed a model to generate DDoS attacks using a WGAN. I used several techniques to update the attack feature profile and generate polymorphic data. This data will change the feature profile in every cycle to test if the IDS can detect the new version attack data. Simulation results from the proposed model show that by continuous changing of attack profiles, defensive systems that use incremental learning will still be vulnerable to new attacks.","abstract_html":"IDS are essential components in preventing malicious traffic from penetrating networks. IDS have been rapidly enhancing their detection ability using ML algorithms. As a result, attackers look for new methods to evade the IDS. Polymorphic attacks are favorites among the attackers as they can bypass the IDS. GAN is a method proven in generating various forms of data. It is becoming popular among security researchers as it can produce indistinguishable data from the original data. I proposed a model to generate DDoS attacks using a WGAN. I used several techniques to update the attack feature profile and generate polymorphic data. This data will change the feature profile in every cycle to test if the IDS can detect the new version attack data. Simulation results from the proposed model show that by continuous changing of attack profiles, defensive systems that use incremental learning will still be vulnerable to new attacks.","abstract_has_math":false,"creators":["Chauhan, Ravi"],"institution":"University of Ontario Institute of Technology","degree_name":"Master of Science (MSc)","degree_level":null,"degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":[],"advisors":["Heydari, Shahram"],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-12-01","date_published":"2020-12-01","updated_at":"2026-07-24T05:35:43Z","subjects":["Adversarial attacks","Generative Adversarial Networks (GAN)","Intrusion detection system","DDoS attacks","Machine learning"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10155/1249","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Heydari, Shahram"]},{"key":"dc:creator","label":"Author","values":["Chauhan, Ravi"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2021-02-26T15:54:14Z","2022-03-29T17:26:06Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2021-02-26T15:54:14Z","2022-03-29T17:26:06Z"]},{"key":"dc:date.issued","label":"Date","values":["2020-12-01"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MSc)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Ontario Institute of Technology"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Adversarial attacks","Generative Adversarial Networks (GAN)","Intrusion detection system","DDoS attacks","Machine learning"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10155/1249"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["IDS are essential components in preventing malicious traffic from penetrating networks. IDS have been rapidly enhancing their detection ability using ML algorithms. As a result, attackers look for new methods to evade the IDS. Polymorphic attacks are favorites among the attackers as they can bypass the IDS. GAN is a method proven in generating various forms of data. It is becoming popular among security researchers as it can produce indistinguishable data from the original data. I proposed a model to generate DDoS attacks using a WGAN. I used several techniques to update the attack feature profile and generate polymorphic data. This data will change the feature profile in every cycle to test if the IDS can detect the new version attack data. Simulation results from the proposed model show that by continuous changing of attack profiles, defensive systems that use incremental learning will still be vulnerable to new attacks."]},{"key":"dc:title","label":"Title","values":["Polymorphic Adversarial DDoS attack on IDS using GAN"]}]}],"canonical_facts":{"dc:contributor.advisor":["Heydari, Shahram"],"dc:creator":["Chauhan, Ravi"],"dc:date.accessioned":["2021-02-26T15:54:14Z","2022-03-29T17:26:06Z"],"dc:date.available":["2021-02-26T15:54:14Z","2022-03-29T17:26:06Z"],"dc:date.issued":["2020-12-01"],"dc:description.abstract":["IDS are essential components in preventing malicious traffic from penetrating networks. IDS have been rapidly enhancing their detection ability using ML algorithms. As a result, attackers look for new methods to evade the IDS. Polymorphic attacks are favorites among the attackers as they can bypass the IDS. GAN is a method proven in generating various forms of data. It is becoming popular among security researchers as it can produce indistinguishable data from the original data. I proposed a model to generate DDoS attacks using a WGAN. I used several techniques to update the attack feature profile and generate polymorphic data. This data will change the feature profile in every cycle to test if the IDS can detect the new version attack data. Simulation results from the proposed model show that by continuous changing of attack profiles, defensive systems that use incremental learning will still be vulnerable to new attacks."],"dc:identifier.uri":["https://hdl.handle.net/10155/1249"],"dc:language.iso":["en"],"dc:subject":["Adversarial attacks","Generative Adversarial Networks (GAN)","Intrusion detection system","DDoS attacks","Machine learning"],"dc:title":["Polymorphic Adversarial DDoS attack on IDS using GAN"],"dc:type":["Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_name":["Master of Science (MSc)"],"thesis:institution_name":["University of Ontario Institute of Technology"]},"updated_at":"2026-07-24T05:35:43Z"}