Weighted Multiclass Intrusion Detection System
Attackers are continuously coming up with new attack strategies since cyber security is a field that is continually changing. As a result, it’s important to update and enhance the system frequently to ensure its efficiency against fresh threats. Unauthorised entry, usage, or manipulation of a comput...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2023-01-01
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| Series: | ITM Web of Conferences |
| Subjects: | |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2023/07/itmconf_icaect2023_01009.pdf |
| _version_ | 1797343930517291008 |
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| author | Dange Varsha Phadke Soham Solunke Tilak Marne Sidhesh Suryawanshi Snehal Surase Om |
| author_facet | Dange Varsha Phadke Soham Solunke Tilak Marne Sidhesh Suryawanshi Snehal Surase Om |
| author_sort | Dange Varsha |
| collection | DOAJ |
| description | Attackers are continuously coming up with new attack strategies since cyber security is a field that is continually changing. As a result, it’s important to update and enhance the system frequently to ensure its efficiency against fresh threats. Unauthorised entry, usage, or manipulation of a computer system or network by a person or programme is referred to as an intrusion. There are numerous ways for an incursion to happen, including using software flaws, phishing scams, or social engineering techniques. A realistic solution to handle the risks brought on by the interconnectedness and interoperability of computer systems is to use deep learning architectures to build an adaptive and resilient network intrusion detection system (IDS) to identify and categorise network attacks. Artificial neural networks (ANNs) or deep learning can help adaptive intrusion detection systems (IDS) with learning capabilities identify well-known and unique or zero-day network behavioural patterns, which can significantly reduce the risk of compromise. The NSL-KDD dataset, which represents both synthetically manufactured attack actions and real-world network communication activity, is used to show the effectiveness of the model. Model trained with this dataset to detect a wide range of attack patterns, which help in building an effective IDS. |
| first_indexed | 2024-03-08T10:54:57Z |
| format | Article |
| id | doaj.art-12a121eb79484a1589deda39c1cea1ec |
| institution | Directory Open Access Journal |
| issn | 2271-2097 |
| language | English |
| last_indexed | 2024-03-08T10:54:57Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj.art-12a121eb79484a1589deda39c1cea1ec2024-01-26T16:34:27ZengEDP SciencesITM Web of Conferences2271-20972023-01-01570100910.1051/itmconf/20235701009itmconf_icaect2023_01009Weighted Multiclass Intrusion Detection SystemDange Varsha0Phadke Soham1Solunke Tilak2Marne Sidhesh3Suryawanshi Snehal4Surase Om5Department of Multidisciplinary, Engineering Vishwakarma Institute of TechnologyDepartment of Multidisciplinary, Engineering Vishwakarma Institute of TechnologyDepartment of Multidisciplinary, Engineering Vishwakarma Institute of TechnologyDepartment of Multidisciplinary, Engineering Vishwakarma Institute of TechnologyDepartment of Multidisciplinary, Engineering Vishwakarma Institute of TechnologyDepartment of Multidisciplinary, Engineering Vishwakarma Institute of TechnologyAttackers are continuously coming up with new attack strategies since cyber security is a field that is continually changing. As a result, it’s important to update and enhance the system frequently to ensure its efficiency against fresh threats. Unauthorised entry, usage, or manipulation of a computer system or network by a person or programme is referred to as an intrusion. There are numerous ways for an incursion to happen, including using software flaws, phishing scams, or social engineering techniques. A realistic solution to handle the risks brought on by the interconnectedness and interoperability of computer systems is to use deep learning architectures to build an adaptive and resilient network intrusion detection system (IDS) to identify and categorise network attacks. Artificial neural networks (ANNs) or deep learning can help adaptive intrusion detection systems (IDS) with learning capabilities identify well-known and unique or zero-day network behavioural patterns, which can significantly reduce the risk of compromise. The NSL-KDD dataset, which represents both synthetically manufactured attack actions and real-world network communication activity, is used to show the effectiveness of the model. Model trained with this dataset to detect a wide range of attack patterns, which help in building an effective IDS.https://www.itm-conferences.org/articles/itmconf/pdf/2023/07/itmconf_icaect2023_01009.pdfdeep learningdecision treeknnnaive bayesrandom forest. |
| spellingShingle | Dange Varsha Phadke Soham Solunke Tilak Marne Sidhesh Suryawanshi Snehal Surase Om Weighted Multiclass Intrusion Detection System ITM Web of Conferences deep learning decision tree knn naive bayes random forest. |
| title | Weighted Multiclass Intrusion Detection System |
| title_full | Weighted Multiclass Intrusion Detection System |
| title_fullStr | Weighted Multiclass Intrusion Detection System |
| title_full_unstemmed | Weighted Multiclass Intrusion Detection System |
| title_short | Weighted Multiclass Intrusion Detection System |
| title_sort | weighted multiclass intrusion detection system |
| topic | deep learning decision tree knn naive bayes random forest. |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2023/07/itmconf_icaect2023_01009.pdf |
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