Low-Latency Intrusion Detection Using a Deep Neural Network
Intrusion detection systems (IDSs) must be implemented across the network to identify and avoid attacks to counter the emerging tactics and techniques employed by hackers. In this research, we propose a lightweight IDS for improving IDS efficiency and reducing attack detection execution time. We use...
Uložené v:
| Vydané v: | IT professional Ročník 24; číslo 3; s. 67 - 72 |
|---|---|
| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Washington
IEEE
01.05.2022
IEEE Computer Society |
| Predmet: | |
| ISSN: | 1520-9202, 1941-045X |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | Intrusion detection systems (IDSs) must be implemented across the network to identify and avoid attacks to counter the emerging tactics and techniques employed by hackers. In this research, we propose a lightweight IDS for improving IDS efficiency and reducing attack detection execution time. We use an RF algorithm to rank features in order of importance and then reduce data dimension by selecting the top 15 important features. We then implement a deep neural network (DNN) architecture to classify anonymous traffic by analyzing TCP/IP packets on the network security laboratory-knowledge discovery in databases (NSL-KDD) dataset. The results indicate that our proposed technique of applying RF to identify important features can improve the DNN-based IDS system’s execution time and performance and has low latency for intrusion detection as compared to other state-of-the-art techniques. |
|---|---|
| AbstractList | Intrusion detection systems (IDSs) must be implemented across the network to identify and avoid attacks to counter the emerging tactics and techniques employed by hackers. In this research, we propose a lightweight IDS for improving IDS efficiency and reducing attack detection execution time. We use an RF algorithm to rank features in order of importance and then reduce data dimension by selecting the top 15 important features. We then implement a deep neural network (DNN) architecture to classify anonymous traffic by analyzing TCP/IP packets on the network security laboratory-knowledge discovery in databases (NSL-KDD) dataset. The results indicate that our proposed technique of applying RF to identify important features can improve the DNN-based IDS system’s execution time and performance and has low latency for intrusion detection as compared to other state-of-the-art techniques. |
| Author | Ahmad, Umair Bin Mian, Adnan Noor Akram, Muhammad Arslan |
| Author_xml | – sequence: 1 givenname: Umair Bin surname: Ahmad fullname: Ahmad, Umair Bin email: msds19036@itu.edu.pk organization: Information Technology University, Lahore, Pakistan – sequence: 2 givenname: Muhammad Arslan orcidid: 0000-0002-9875-557X surname: Akram fullname: Akram, Muhammad Arslan email: arslan.akram@itu.edu.pk organization: Information Technology University, Lahore, Pakistan – sequence: 3 givenname: Adnan Noor orcidid: 0000-0003-1034-0140 surname: Mian fullname: Mian, Adnan Noor email: adnan.noor@itu.edu.pk organization: Information Technology University, Lahore, Pakistan |
| BookMark | eNp9kEtPwzAQhC1UJNrCD0BcInFO8DpOGosTKq9K4XEoEjfLdjYopSTFdlT13-OoFQcOnHa0-mZXMxMyarsWCTkHmgBQcfW0WL4mjDKWpJBxlvIjMgbBIaY8ex8FnTEaiwCckIlzK0oh57wYk-uy28al8tiaXbRove1d07XRLXo0flBvrmk_IhU2uImesbdqHYbfdvbzlBzXau3w7DCnZHl_t5w_xuXLw2J-U8aGsdTHApWiNAMNqHWhQIDO06Keiapmda6FyalSoqKF1hwMozloxXNWYVZxUZt0Si73Zze2--7RebnqetuGj5LlBRMsRGGBmu0pYzvnLNbSNF4NEbxVzVoClUNRcihKDkXJQ1HBCX-cG9t8Kbv713Ox9zSI-MuLAoCzLP0BNt11JQ |
| CODEN | IPMAFM |
| CitedBy_id | crossref_primary_10_1007_s11276_023_03516_0 |
| Cites_doi | 10.1109/LCOMM.2020.3048995 10.1109/ACCESS.2020.2986217 10.1016/j.ssci.2020.104604 10.1016/j.eswa.2019.112963 10.1016/j.sciaf.2020.e00500 10.1007/978-3-030-57024-8_10 10.1016/j.measurement.2019.107450 10.4236/jis.2016.73009 10.1023/A:1010933404324 10.1145/3395352.3402621 10.1109/LCOMM.2019.2937097 10.3837/tiis.2017.10.024 10.1109/MITP.2020.2992710 10.1109/SSCI.2017.8280825 10.1109/ICOSEC49089.2020.9215232 |
| ContentType | Journal Article |
| Copyright | Copyright IEEE Computer Society 2022 |
| Copyright_xml | – notice: Copyright IEEE Computer Society 2022 |
| DBID | 97E RIA RIE AAYXX CITATION JQ2 |
| DOI | 10.1109/MITP.2022.3154234 |
| DatabaseName | IEEE Xplore (IEEE) IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef ProQuest Computer Science Collection |
| DatabaseTitle | CrossRef ProQuest Computer Science Collection |
| DatabaseTitleList | ProQuest Computer Science Collection |
| 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 |
| EISSN | 1941-045X |
| EndPage | 72 |
| ExternalDocumentID | 10_1109_MITP_2022_3154234 9811425 |
| Genre | orig-research |
| GroupedDBID | -~X .4S .DC 0R~ 29J 4.4 5GY 5VS 6IK 7WY 8FE 8FG 8FL 8R4 8R5 97E AAJGR AARMG AASAJ AAVXG AAWTH ABAZT ABJCF ABQJQ ABUWG ABVLG ACGFS ACIWK AENEX AETIX AFFNX AFKRA AFOGA AGQYO AGSQL AHBIQ AIBXA AKJIK AKQYR ALLEH ALMA_UNASSIGNED_HOLDINGS ARAPS ARCSS ATWAV AZLTO AZQEC BEFXN BENPR BEZIV BFFAM BGLVJ BGNUA BKEBE BPEOZ BPHCQ CCPQU CS3 DU5 DWQXO EBS EDO EJD FRNLG GNUQQ HCIFZ H~9 I-F IEDLZ IFIPE IFJZH IPLJI ITG ITH JAVBF K60 K6V K6~ K7- L6V LAI M0C M43 M7S OCL P62 PHGZM PHGZT PQBIZ PQBZA PQGLB PQQKQ PROAC PTHSS PUEGO Q2X RIA RIE RNI RNS RZB TN5 XZL ZT3 AAYXX CITATION JQ2 |
| ID | FETCH-LOGICAL-c223t-9eaa0051b1ebb8a191b638f79df2f6b9c60aa9d08bb41c2061ba462de5d49fc3 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 1 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000819825800022&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1520-9202 |
| IngestDate | Mon Jun 30 10:46:10 EDT 2025 Sat Nov 29 05:40:16 EST 2025 Tue Nov 18 21:52:01 EST 2025 Wed Aug 27 02:23:53 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 3 |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c223t-9eaa0051b1ebb8a191b638f79df2f6b9c60aa9d08bb41c2061ba462de5d49fc3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0002-9875-557X 0000-0003-1034-0140 |
| PQID | 2682921642 |
| PQPubID | 32686 |
| PageCount | 6 |
| ParticipantIDs | proquest_journals_2682921642 crossref_citationtrail_10_1109_MITP_2022_3154234 ieee_primary_9811425 crossref_primary_10_1109_MITP_2022_3154234 |
| PublicationCentury | 2000 |
| PublicationDate | 2022-05-01 |
| PublicationDateYYYYMMDD | 2022-05-01 |
| PublicationDate_xml | – month: 05 year: 2022 text: 2022-05-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Washington |
| PublicationPlace_xml | – name: Washington |
| PublicationTitle | IT professional |
| PublicationTitleAbbrev | ITP-M |
| PublicationYear | 2022 |
| Publisher | IEEE IEEE Computer Society |
| Publisher_xml | – name: IEEE – name: IEEE Computer Society |
| References | ref13 ref15 ref14 ref11 ref2 ref1 Neyshabur (ref4) 2017 Aghdam (ref10) 2016; 18 ref17 Najeeb (ref12) 2018; 13 ref18 ref8 ref7 ref9 ref3 ref6 ref5 Breiman (ref16) 2001; 45 |
| References_xml | – ident: ref1 doi: 10.1109/LCOMM.2020.3048995 – ident: ref18 doi: 10.1109/ACCESS.2020.2986217 – ident: ref6 doi: 10.1016/j.ssci.2020.104604 – ident: ref9 doi: 10.1016/j.eswa.2019.112963 – ident: ref13 doi: 10.1016/j.sciaf.2020.e00500 – ident: ref5 doi: 10.1007/978-3-030-57024-8_10 – ident: ref7 doi: 10.1016/j.measurement.2019.107450 – ident: ref15 doi: 10.4236/jis.2016.73009 – volume: 45 start-page: 5 issue: 1 year: 2001 ident: ref16 article-title: Random forests publication-title: Mach. Learn. doi: 10.1023/A:1010933404324 – volume: 13 start-page: 2347 issue: 6 year: 2018 ident: ref12 article-title: A feature selection approach using binary firefly algorithm for network intrusion detection system publication-title: ARPN J. Eng. Appl. Sci. – ident: ref17 doi: 10.1145/3395352.3402621 – ident: ref2 doi: 10.1109/LCOMM.2019.2937097 – ident: ref11 doi: 10.3837/tiis.2017.10.024 – ident: ref14 doi: 10.1109/MITP.2020.2992710 – ident: ref3 doi: 10.1109/SSCI.2017.8280825 – volume: 18 start-page: 420 issue: 3 year: 2016 ident: ref10 article-title: Feature selection for intrusion detection system using ant colony optimization publication-title: Int. J. Netw. Secur. – ident: ref8 doi: 10.1109/ICOSEC49089.2020.9215232 – year: 2017 ident: ref4 article-title: Exploring generalization in deep learning |
| SSID | ssj0016448 |
| Score | 2.2647626 |
| Snippet | Intrusion detection systems (IDSs) must be implemented across the network to identify and avoid attacks to counter the emerging tactics and techniques employed... |
| SourceID | proquest crossref ieee |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 67 |
| SubjectTerms | Algorithms Artificial neural networks Computer hacking Deep learning Intrusion detection Intrusion detection systems Network latency Neural networks Radio frequency Tactics TCP/IP (protocol) TCPIP |
| Title | Low-Latency Intrusion Detection Using a Deep Neural Network |
| URI | https://ieeexplore.ieee.org/document/9811425 https://www.proquest.com/docview/2682921642 |
| Volume | 24 |
| WOSCitedRecordID | wos000819825800022&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 | |
| journalDatabaseRights | – providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 1941-045X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0016448 issn: 1520-9202 databaseCode: RIE dateStart: 19990101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3PS8MwFH5sw4MenG6K0yk5eBLr0qxNGjyJOhTG2GGH3UqSpiBIN9xU_O99SbuiKIK3UpK2vC8v733N-wFwbkPGcsVZEAojA9REhipF4yBTQ52HmaHaKN9sQkwmyXwupw24rHNhrLU--MxeuUt_lp8tzKv7VTaQicv8jJvQFIKXuVr1iYHjGb42KtIhySirTjBDKge4CUyRCTKGBDVG9yH6ZoN8U5UfO7E3L6P2_z5sD3YrN5LclLjvQ8MWHWhvWjSQSmM7sPOl3mAXrseL92CsnJv8QR4Ll2-BsJA7u_YBWQXxAQRE4R27JK5uB75jUgaKH8BsdD-7fQiq7gmBQZO_DqRVyqmcDq3WiUJeplHXciGznOVcS8OpUjKjidZRaBjada0izjIbZ5HMzfAQWsWisEdARKRMgm6Pc80ixYXixvA8UVaEEfKTYQ_oRpypqSqLuwYXz6lnGFSmDoHUIZBWCPTgop6yLMtq_DW460ReD6yk3YP-BrO0UrxVynjCJMPVwI5_n3UC2-7ZZcxiH1ooansKW-Zt_bR6OfNr6hNvfser |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3NS8MwFH_MKagHp5vidGoPnsS6NEs_gidRx4Z17LDDbiVJUxCkG24q_ve-pF1RFMFbKQkp75eX937N-wA41x6lmQio64WKu6iJFFWK-G4qejLzUkWkErbZRDgaRdMpH9fgssqF0Vrb4DN9ZR7tXX46U6_mV1mXRybz01-DdZ8xSopsrerOwDANWx0VCRGnhJZ3mB7hXTwGxsgFKUWK6qMDwb5ZIdtW5cdZbA1Mv_G_T9uFndKRdG4K5PegpvMmNFZNGpxSZ5uw_aXiYAuu49m7GwvjKH84w9xkXCAwzp1e2pCs3LEhBI7AN3rumModuMaoCBXfh0n_fnI7cMv-Ca5Co790uRbCKJ30tJSRQGYmUduykKcZzQLJVUCE4CmJpGSeomjZpWABTbWfMp6p3gHU81muD8EJmVAROj7GOWMiCEWgVJBFQoceQ4bSawNZiTNRZW1x0-LiObEcg_DEIJAYBJISgTZcVFPmRWGNvwa3jMirgaW029BZYZaUqrdIaBBRTnE30KPfZ53B5mDyGCfxcPRwDFtmnSKCsQN1FLs-gQ31tnxavJza_fUJ_xzK8g |
| 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%3Ajournal&rft.genre=article&rft.atitle=Low-Latency+Intrusion+Detection+Using+a+Deep+Neural+Network&rft.jtitle=IT+professional&rft.au=Ahmad%2C+Umair+Bin&rft.au=Akram%2C+Muhammad+Arslan&rft.au=Mian%2C+Adnan+Noor&rft.date=2022-05-01&rft.issn=1520-9202&rft.eissn=1941-045X&rft.volume=24&rft.issue=3&rft.spage=67&rft.epage=72&rft_id=info:doi/10.1109%2FMITP.2022.3154234&rft.externalDBID=n%2Fa&rft.externalDocID=10_1109_MITP_2022_3154234 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1520-9202&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1520-9202&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1520-9202&client=summon |