QML-IDS: Quantum Machine Learning Intrusion Detection System
The emergence of quantum computing and related technologies presents opportunities for enhancing network security. The transition towards quantum computational power paves the way for creating strategies to mitigate the constantly advancing threats to network integrity. In response to this technolog...
Gespeichert in:
| Veröffentlicht in: | Proceedings - IEEE Symposium on Computers and Communications S. 1 - 6 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Tagungsbericht |
| Sprache: | Englisch |
| Veröffentlicht: |
IEEE
26.06.2024
|
| Schlagworte: | |
| ISSN: | 2642-7389 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | The emergence of quantum computing and related technologies presents opportunities for enhancing network security. The transition towards quantum computational power paves the way for creating strategies to mitigate the constantly advancing threats to network integrity. In response to this technological advancement, our research presents QML-IDS, a novel Intrusion Detection System (IDS) that combines quantum and classical computing techniques. QML-IDS employs Quantum Machine Learning (QML) methodologies to analyze network patterns and detect attack activities. Through extensive experimental tests on publicly available datasets, we show that QML-IDS is effective at attack detection and performs well in binary and multiclass classification tasks. Our findings reveal that QML-IDS outperforms classical Machine Learning methods, demonstrating the promise of quantum-enhanced cybersecurity solutions for the age of quantum utility. |
|---|---|
| AbstractList | The emergence of quantum computing and related technologies presents opportunities for enhancing network security. The transition towards quantum computational power paves the way for creating strategies to mitigate the constantly advancing threats to network integrity. In response to this technological advancement, our research presents QML-IDS, a novel Intrusion Detection System (IDS) that combines quantum and classical computing techniques. QML-IDS employs Quantum Machine Learning (QML) methodologies to analyze network patterns and detect attack activities. Through extensive experimental tests on publicly available datasets, we show that QML-IDS is effective at attack detection and performs well in binary and multiclass classification tasks. Our findings reveal that QML-IDS outperforms classical Machine Learning methods, demonstrating the promise of quantum-enhanced cybersecurity solutions for the age of quantum utility. |
| Author | Rothenberg, Christian Esteve Abelem, Antonio Abreu, Diego |
| Author_xml | – sequence: 1 givenname: Diego surname: Abreu fullname: Abreu, Diego organization: Federal University of Pará (UFPA) – sequence: 2 givenname: Christian Esteve surname: Rothenberg fullname: Rothenberg, Christian Esteve organization: University of Campinas (UNICAMP) – sequence: 3 givenname: Antonio surname: Abelem fullname: Abelem, Antonio organization: Federal University of Pará (UFPA) |
| BookMark | eNo1j8tKw0AUQEdRsK39A8H8QOI8cuchbiT1EUiREl2XyfSOjpipJJNF_16KujpndeDMyVncRyTkmtGCMWpu6raqJJNKFJzysmBUCSEBTsjSKKMFUAElF_yUzLgsea6ENhdkPo6flFINXM3I3Wbd5PWqvc02k41p6rO1dR8hYtagHWKI71kd0zCNYR-zFSZ06WjtYUzYX5Jzb79GXP5xQd4eH16r57x5eaqr-yYPTMmUYwmdN04rryjslEIPWlHdWQ_Gcs-E8zunJRplLQNgRnZogYKXDjw1VizI1W83IOL2ewi9HQ7b_1vxA3oQSvw |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IL CBEJK RIE RIL |
| DOI | 10.1109/ISCC61673.2024.10733655 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume IEEE Xplore All Conference Proceedings IEEE Electronic Library (IEL) IEEE Proceedings Order Plans (POP All) 1998-Present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Computer Science |
| EISBN | 9798350354232 |
| EISSN | 2642-7389 |
| EndPage | 6 |
| ExternalDocumentID | 10733655 |
| Genre | orig-research |
| GroupedDBID | 6IE 6IF 6IH 6IK 6IL 6IN AAJGR AAWTH ACGFS ADZIZ ALMA_UNASSIGNED_HOLDINGS BEFXN BFFAM BGNUA BKEBE BPEOZ CBEJK CHZPO IPLJI M43 OCL RIE RIL |
| ID | FETCH-LOGICAL-i176t-e45bf9c87f705d77ef58708baf59a2f13cfdc86e97aa155196bea505f6c5f09a3 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 3 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001363176200092&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| IngestDate | Wed Aug 27 03:00:27 EDT 2025 |
| IsPeerReviewed | false |
| IsScholarly | true |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-i176t-e45bf9c87f705d77ef58708baf59a2f13cfdc86e97aa155196bea505f6c5f09a3 |
| PageCount | 6 |
| ParticipantIDs | ieee_primary_10733655 |
| PublicationCentury | 2000 |
| PublicationDate | 2024-June-26 |
| PublicationDateYYYYMMDD | 2024-06-26 |
| PublicationDate_xml | – month: 06 year: 2024 text: 2024-June-26 day: 26 |
| PublicationDecade | 2020 |
| PublicationTitle | Proceedings - IEEE Symposium on Computers and Communications |
| PublicationTitleAbbrev | ISCC |
| PublicationYear | 2024 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| SSID | ssj0008527 |
| Score | 2.329497 |
| Snippet | The emergence of quantum computing and related technologies presents opportunities for enhancing network security. The transition towards quantum computational... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 1 |
| SubjectTerms | Computer security Computers Intrusion detection Machine learning Network security Quantum computing Quantum Machine Learning Quantum Network |
| Title | QML-IDS: Quantum Machine Learning Intrusion Detection System |
| URI | https://ieeexplore.ieee.org/document/10733655 |
| WOSCitedRecordID | wos001363176200092&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1NTwIxEJ0I8aAXFDF-pwevRfajX8YbSCQBAkETbqTbnRoOLkTB329bFtSDB2-bTTbNzmTa1_a9NwC32qFcxTNFU0RNU2k1zTBRVDqIFLu5IVFJEAr3xXAop1M1KsXqQQuDiIF8hk3_GO7y84VZ-6MyV-HevI-xClSE4Bux1m7alSwWJYEraqm73qTd5hEXidsDxmlz--mvJiphDenW_jn6ETS-1XhktFtnjmEPizrUtu0YSFmddTj84S14Ag_jQZ_2OpN7Ml676K3fyCDwJpGUlqqvpFd4xYVLDOngKlCyCrJxMG_AS_fxuf1Ey1YJdB4JvqKYsswqI4UVLZYLgZa5QpSZtkzp2EaJsbmRHJXQ2oMklx7UDvxYbphtKZ2cQrVYFHgGJEq8Hw3jMmOYcrc7M3HuUIo2KXfoDKNzaPjYzJYbN4zZNiwXf7y_hAOfAU-vivkVVN2f4TXsm8_V_OP9JuTwCz3Sm90 |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1NTwIxEG0UTdQLihi_7cFrkf3ol_EGEjYuBAIm3Ei3OzUcXIyCv9-2LKgHD942m2yancm0r-17bxC6VRblSpZJEgMoEgujSAaRJMJCpNDODZGMvFA45f2-mEzkoBSrey0MAHjyGTTco7_Lz-d66Y7KbIU78z5Kt9GOa51VyrU2E6-gIS8pXEFT3iWjVosFjEd2FxjGjfXHv9qo-FWkU_3n-Ieo_q3Hw4PNSnOEtqCooeq6IQMu67OGDn64Cx6jh2EvJUl7dI-HSxu_5SvueeYk4NJU9QUnhdNc2NTgNiw8KavAKw_zOnruPI5bXVI2SyCzgLMFgZhmRmrBDW_SnHMw1JaiyJShUoUmiLTJtWAguVIOJtkEgbLwxzBNTVOq6ARVinkBpwgHkXOkoUxkFGJm92c6zC1OUTpmFp9BcIbqLjbTt5UfxnQdlvM_3t-gve64l07TpP90gfZdNhzZKmSXqGL_Eq7Qrv5czD7er30-vwCPMZ8m |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=Proceedings+-+IEEE+Symposium+on+Computers+and+Communications&rft.atitle=QML-IDS%3A+Quantum+Machine+Learning+Intrusion+Detection+System&rft.au=Abreu%2C+Diego&rft.au=Rothenberg%2C+Christian+Esteve&rft.au=Abelem%2C+Antonio&rft.date=2024-06-26&rft.pub=IEEE&rft.eissn=2642-7389&rft.spage=1&rft.epage=6&rft_id=info:doi/10.1109%2FISCC61673.2024.10733655&rft.externalDocID=10733655 |