Academic Journal
Efficient Intrusion Detection Through the Fusion of AI Algorithms and Feature Selection Methods.
| Τίτλος: | Efficient Intrusion Detection Through the Fusion of AI Algorithms and Feature Selection Methods. |
|---|---|
| Alternate Title: | كشف التسلل من خلال دمج خوارزميات الذكاء الاصطناعي وطرق اختيار الميزات. (Arabic) |
| Συγγραφείς: | Obaid, Marwa Mohammad, Saleh, Muna Hadi |
| Πηγή: | Journal of Engineering (17264073); Jul2024, Vol. 30 Issue 7, p184-201, 18p |
| Θεματικοί όροι: | Intrusion detection systems (Computer security), Feature selection, Pattern recognition systems, Artificial intelligence, Computer network traffic, K-nearest neighbor classification, Machine learning |
| Abstract (English): | With the proliferation of both Internet access and data traffic, recent breaches have brought into sharp focus the need for Network Intrusion Detection Systems (NIDS) to protect networks from more complex cyberattacks. To differentiate between normal network processes and possible attacks, Intrusion Detection Systems (IDS) often employ pattern recognition and data mining techniques. Network and host system intrusions, assaults, and policy violations can be automatically detected and classified by an Intrusion Detection System (IDS). Using Python Scikit-Learn the results of this study show that Machine Learning (ML) techniques like Decision Tree (DT), Naïve Bayes (NB), and K-Nearest Neighbor (KNN) can enhance the effectiveness of an Intrusion Detection System (IDS). Success is measured by a variety of metrics, including accuracy, precision, recall, F1-Score, and execution time. Applying feature selection approaches such as Analysis of Variance (ANOVA), Mutual Information (MI), and Chi-Square (Ch-2) reduced execution time, increased detection efficiency and accuracy, and boosted overall performance. All classifiers achieve the greatest performance with 99.99% accuracy and the shortest computation time of 0.0089 seconds while using ANOVA with 10% of features. [ABSTRACT FROM AUTHOR] |
| Abstract (Arabic): | مع انتشار الوصول إلى الإنترنت وحركة البيانات، سلطت الخروقات الأخيرة الضوء على الحاجة إلى أنظمة كشف التسلل إلى الشبكة (NIDS) في حماية الشبكات من الهجمات السيبرانية الأكثر تعقيدا من أجل التمييز بين عمليات الشبكة العادية والهجمات المحتملة، غالبا ما تستخدم أنظمة كشف التسلل (IDS) تقنيات التعرف على الأنماط واستخراج البيانات. يمكن اكتشاف عمليات التطفل على الشبكة ونظام المضيف والاعتداءات وانتهاكات السياسة وتصنيفها تلقائيا بواسطة نظام كشف التسلل (IDS). تظهر نتائج هذه الدراسة أن تقنيات تعلم الآلة مثلK-Nearest Neighbor و ،Naïve Bayes (NB) و ،Decision Tree (DT) نتائج هذه الدراسة أن تقنيات تعلم الآلة مثل(KNN) يمكن أن تعزز فعالية نظام كشف التسلل. يتم قياس النجاح من خلال مجموعة متنوعة من المقاييس، بما في ذلك الدقة والاستدعاء والدقة ودرجة F1 ووقت التنفيذ. أدى تطبيق أساليب اختيار الميزات مثل تحليل التباين (ANOVA)، والمعلومات المتبادلة (MI)، ومربع كاي (2-Ch) إلى تقليل وقت التنفيذ، وزيادة كفاءة الكشف ودقته، وتعزيز الأداء العام. تحقق جميع المصنفات أفضل أداء بدقة تصل إلى 99.99% وأقصر وقت حسابي يبلغ 0.0089 مللي ثانية أثناء استخدام ANOVA مع%10 الميزات. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Engineering (17264073) is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edb&genre=article&issn=17264073&ISBN=&volume=30&issue=7&date=20240701&spage=184&pages=184-201&title=Journal of Engineering (17264073)&atitle=Efficient%20Intrusion%20Detection%20Through%20the%20Fusion%20of%20AI%20Algorithms%20and%20Feature%20Selection%20Methods.&aulast=Obaid%2C%20Marwa%20Mohammad&id=DOI:10.31026/j.eng.2024.07.11 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
|---|---|
| Header | DbId: edb DbLabel: Complementary Index An: 178545877 RelevancyScore: 966 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 965.71044921875 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Efficient Intrusion Detection Through the Fusion of AI Algorithms and Feature Selection Methods. – Name: TitleAlt Label: Alternate Title Group: TiAlt Data: كشف التسلل من خلال دمج خوارزميات الذكاء الاصطناعي وطرق اختيار الميزات. (Arabic) – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Obaid%2C+Marwa+Mohammad%22">Obaid, Marwa Mohammad</searchLink><br /><searchLink fieldCode="AR" term="%22Saleh%2C+Muna+Hadi%22">Saleh, Muna Hadi</searchLink> – Name: TitleSource Label: Source Group: Src Data: Journal of Engineering (17264073); Jul2024, Vol. 30 Issue 7, p184-201, 18p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Intrusion+detection+systems+%28Computer+security%29%22">Intrusion detection systems (Computer security)</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink><br /><searchLink fieldCode="DE" term="%22Pattern+recognition+systems%22">Pattern recognition systems</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+network+traffic%22">Computer network traffic</searchLink><br /><searchLink fieldCode="DE" term="%22K-nearest+neighbor+classification%22">K-nearest neighbor classification</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: AbstractNonEng Label: Abstract (English) Group: Ab Data: With the proliferation of both Internet access and data traffic, recent breaches have brought into sharp focus the need for Network Intrusion Detection Systems (NIDS) to protect networks from more complex cyberattacks. To differentiate between normal network processes and possible attacks, Intrusion Detection Systems (IDS) often employ pattern recognition and data mining techniques. Network and host system intrusions, assaults, and policy violations can be automatically detected and classified by an Intrusion Detection System (IDS). Using Python Scikit-Learn the results of this study show that Machine Learning (ML) techniques like Decision Tree (DT), Naïve Bayes (NB), and K-Nearest Neighbor (KNN) can enhance the effectiveness of an Intrusion Detection System (IDS). Success is measured by a variety of metrics, including accuracy, precision, recall, F1-Score, and execution time. Applying feature selection approaches such as Analysis of Variance (ANOVA), Mutual Information (MI), and Chi-Square (Ch-2) reduced execution time, increased detection efficiency and accuracy, and boosted overall performance. All classifiers achieve the greatest performance with 99.99% accuracy and the shortest computation time of 0.0089 seconds while using ANOVA with 10% of features. [ABSTRACT FROM AUTHOR] – Name: AbstractNonEng Label: Abstract (Arabic) Group: Ab Data: مع انتشار الوصول إلى الإنترنت وحركة البيانات، سلطت الخروقات الأخيرة الضوء على الحاجة إلى أنظمة كشف التسلل إلى الشبكة (NIDS) في حماية الشبكات من الهجمات السيبرانية الأكثر تعقيدا من أجل التمييز بين عمليات الشبكة العادية والهجمات المحتملة، غالبا ما تستخدم أنظمة كشف التسلل (IDS) تقنيات التعرف على الأنماط واستخراج البيانات. يمكن اكتشاف عمليات التطفل على الشبكة ونظام المضيف والاعتداءات وانتهاكات السياسة وتصنيفها تلقائيا بواسطة نظام كشف التسلل (IDS). تظهر نتائج هذه الدراسة أن تقنيات تعلم الآلة مثلK-Nearest Neighbor و ،Naïve Bayes (NB) و ،Decision Tree (DT) نتائج هذه الدراسة أن تقنيات تعلم الآلة مثل(KNN) يمكن أن تعزز فعالية نظام كشف التسلل. يتم قياس النجاح من خلال مجموعة متنوعة من المقاييس، بما في ذلك الدقة والاستدعاء والدقة ودرجة F1 ووقت التنفيذ. أدى تطبيق أساليب اختيار الميزات مثل تحليل التباين (ANOVA)، والمعلومات المتبادلة (MI)، ومربع كاي (2-Ch) إلى تقليل وقت التنفيذ، وزيادة كفاءة الكشف ودقته، وتعزيز الأداء العام. تحقق جميع المصنفات أفضل أداء بدقة تصل إلى 99.99% وأقصر وقت حسابي يبلغ 0.0089 مللي ثانية أثناء استخدام ANOVA مع%10 الميزات. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Journal of Engineering (17264073) is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edb&AN=178545877 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.31026/j.eng.2024.07.11 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 184 Subjects: – SubjectFull: Intrusion detection systems (Computer security) Type: general – SubjectFull: Feature selection Type: general – SubjectFull: Pattern recognition systems Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Computer network traffic Type: general – SubjectFull: K-nearest neighbor classification Type: general – SubjectFull: Machine learning Type: general Titles: – TitleFull: Efficient Intrusion Detection Through the Fusion of AI Algorithms and Feature Selection Methods. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Obaid, Marwa Mohammad – PersonEntity: Name: NameFull: Saleh, Muna Hadi IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 17264073 Numbering: – Type: volume Value: 30 – Type: issue Value: 7 Titles: – TitleFull: Journal of Engineering (17264073) Type: main |
| ResultId | 1 |