كشف التسلل بكفاءة من خلال دمج خوارزميات الذكاء الاصطناعي وطرق اختيار الميزات

محتوى المقالة الرئيسي

Marwa Mohammad Obaid
Muna Hadi Saleh

الملخص

أبرزت حالات الاقتحام الأخيرة الحاجة إلى أنظمة الكشف عن اقتحام الشبكة في مقاومة هجمات الشبكة الأكثر تعقيدًا، مع نمو الاتصال بالإنترنت وحجم حركة المرور. كثيرا ما تستخدم أنظمة الكشف عن التسلل تقنيات مثل التعرف على الأنماط واستخراج البيانات لتحديد أنشطة الشبكة كالمعتاد أو الهجوم. نظام الكشف عن التسلل (IDS) هو تقنية نشطة للكشف عن التسلل تقوم تلقائيًا باكتشاف وتصنيف عمليات الاقتحام والاعتداء وانتهاكات السياسة الأمنية على مستوى الشبكة والمضيف. يشير بحثنا إلى أن طرق التعلم الآلي مثل Naiseve Bayes (NB) و K-Nearest Neighbor (KNN) و Decision Tree (DT) قد تعزز فعالية نظام الكشف عن التطفل. دقة الكشف والدقة F1-score والاستدعاء ووقت التنفيذ هي مؤشرات أداء تستخدم لقياس الفعالية. لزيادة كفاءة الكشف، والدقة، وتقليل وقت التنفيذ، تم تطبيق مناهج اختيار الميزات بما في ذلك ANOVA و Mutual Information (MI) و chi-squared (CH-2)، وكانت كل هذه الاستراتيجيات فعالة. عند استخدام ANOVA بنسبة 10٪ من الميزات، تحصل جميع المصنفين على أكبر نتيجة، بدقة 99.99٪ و DT في وقت 0.0089ms

تفاصيل المقالة

كيفية الاقتباس
"كشف التسلل بكفاءة من خلال دمج خوارزميات الذكاء الاصطناعي وطرق اختيار الميزات" (2024) مجلة الهندسة, 30(07), ص 184–201. doi:10.31026/j.eng.2024.07.11.
القسم
Articles

كيفية الاقتباس

"كشف التسلل بكفاءة من خلال دمج خوارزميات الذكاء الاصطناعي وطرق اختيار الميزات" (2024) مجلة الهندسة, 30(07), ص 184–201. doi:10.31026/j.eng.2024.07.11.

تواريخ المنشور

الإستلام

2024-01-29

الموافقة

2024-05-24

النشر الالكتروني

2024-07-01

المراجع

Ahmad, I. Ul Haq, Q. E., Imran, M., Alassafi, M. O., and AlGhamdi, R. A. 2022. An efficient network intrusion detection and classification system, Mathematics, 10(3), p. 530. Doi:10.3390/math10030530

Ali, A.A. and Dawood, F.A.A. 2023. Deep learning of diabetic retinopathy classification in fundus images, Journal of Engineering, 29(12), pp. 139–152. Doi:10.31026/j.eng.2023.12.09

Alkanhel, R. El-kenawy, E. S. M., Abdelhamid, A. A., Ibrahim, A., Alohali, M. A., Abotaleb, M., and Khafaga, D. S. 2023. Network intrusion detection based on feature selection and hybrid metaheuristic optimization., Computers, Materials & Continua, 74(2). Doi:10.32604/cmc.2023.033273

Ambusaidi, M.A. He, X., Nanda, P., and Tan, Z. 2016. Building an intrusion detection system using a filter-based feature selection algorithm, IEEE Transactions on Computers, 65(10), pp. 2986–2998. Doi:10.1109/TC.2016.2519914

Arik, A.O. and Çavdaroğlu, G.Ç. 2024. An intrusion detection approach based on the combination of oversampling and undersampling algorithms, Acta Infologica, 7(1), pp. 125–138. Doi:10.26650/acin.1222890

bhai Gupta, A.R. and Agrawal, J. 2020. A comprehensive survey on various machine learning methods used for intrusion detection systems, in 2020 IEEE 9th International Conference on Communication Systems and Network Technologies (CSNT). IEEE, pp. 282–289. Doi:10.1109/CSNT48778.2020.9115764

Charbuty, B. and Abdulazeez, A. 2021. Classification based on decision tree algorithm for machine learning, Journal of Applied Science and Technology Trends, 2(01), pp. 20–28. Doi:10.38094/jastt20165

Farhana, K., Rahman, M. and Ahmed, M.T. 2020. An intrusion detection system for packet and flow based networks using deep neural network approach, International Journal of Electrical & Computer Engineering (2088-8708), 10(5). Doi:10.11591/ijece.v10i5.pp5514-5525

Fuat, T. 2023. Analysis of intrusion detection systems in UNSW-NB15 and NSL-KDD datasets with machine learning algorithms, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 12(2), pp. 465–477. Doi:10.17798/bitlisfen.1240469

Gu, J. and Lu, S. 2021. An effective intrusion detection approach using SVM with naïve Bayes feature embedding, Computers & Security, 103, p. 102158. Doi:10.1016/j.cose.2020.102158

Hussein, M.A. 2022. Performance analysis of different machine learning models for intrusion detection systems, Journal of Engineering, 28(5), pp. 61–91. Doi:10.31026/j.eng.2022.05.05

Hwang, K., Cai, M., Chen, Y., and Qin, M. 2007. Hybrid intrusion detection with weighted signature generation over anomalous internet episodes, IEEE Transactions on dependable and secure computing, 4(1), pp. 41–55. Doi:10.1109/TDSC.2007.9

Kabir, E., Hu, J., Wang, H., and Zhuo, G. 2018. A novel statistical technique for intrusion detection systems, Future Generation Computer Systems, 79, pp. 303–318. Doi:10.1016/j.future.2017.01.029

Kachavimath, A. V, Nazare, S.V. and Akki, S.S. 2020. Distributed denial of service attack detection using naïve bayes and k-nearest neighbor for network forensics, in 2020 2nd International conference on innovative mechanisms for industry applications (ICIMIA). IEEE, pp. 711–717. Doi:10.1109/ICIMIA48430.2020.9074929

Khan, S., Sivaraman, E. and Honnavalli, P.B. 2020. Performance evaluation of advanced machine learning algorithms for network intrusion detection system, in Proceedings of International Conference on IoT Inclusive Life (ICIIL 2019), NITTTR Chandigarh, India. Springer, pp. 51–59. Doi:10.1007/978-981-15-3020-3_6

Kocher, G. and Kumar, G. 2021. Analysis of machine learning algorithms with feature selection for intrusion detection using UNSW-NB15 dataset, Available at SSRN 3784406 [Preprint]. Doi:10.2139/ssrn.3784406

Krishnaveni, S., Sivamohan, S., Sridhar, S. S., and Prabakaran, S. .2021. Efficient feature selection and classification through ensemble method for network intrusion detection on cloud computing, Cluster Computing, 24(3), pp. 1761–1779. Doi:10.1007/s10586-020-03222-y

Larose, D.T. and Larose, C.D. 2014. K‐nearest neighbor algorithm. Doi:10.1002/9781118874059.ch7

Mebawondu, Alowolodu, O. D., Mebawondu, J. O., and Adetunmbi, A. O. 2020. Network intrusion detection system using supervised learning paradigm, Scientific African, 9, p. e00497. Doi:10.1016/j.sciaf.2020.e00497

More, S., Idrissi, M., Mahmoud, H., and Asyhari, A. T. 2024. Enhanced intrusion detection systems performance with UNSW-NB15 data analysis, Algorithms, 17(2), p. 64. Doi:10.3390/a17020064

Mousavi, S.M., Majidnezhad, V. and Naghipour, A. 2022. A new intelligent intrusion detector based on ensemble of decision trees, Journal of Ambient Intelligence and Humanized Computing, 13(7), pp. 3347–3359. Doi:10.1007/s12652-019-01596-5

Moustafa, N. and Slay, J. 2015a. The significant features of the UNSW-NB15 and the KDD99 data sets for network intrusion detection systems, in 2015 4th international workshop on building analysis datasets and gathering experience returns for security (BADGERS). IEEE, pp. 25–31. Doi:10.1109/BADGERS.2015.014

Moustafa, N. and Slay, J. 2015b. UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set), in 2015 military communications and information systems conference (MilCIS). IEEE, pp. 1–6. Doi:10.1109/MilCIS.2015.7348942

Moustafa, N. and Slay, J. 2016. The evaluation of network anomaly detection systems: statistical analysis of the UNSW-NB15 data set and the comparison with the KDD99 data set, Information Security Journal: A Global Perspective, 25(1–3), pp. 18–31. Doi:10.1080/19393555.2015.1125974

Pathak, A. and Pathak, S. 2020. Study on decision tree and KNN algorithm for intrusion detection system, International Journal of Engineering Research & Technology, 9(5), pp. 376–381. Doi:10.17577/IJERTV9IS050303

Pietraszek, T. 2004. Using adaptive alert classification to reduce false positives in intrusion detection, in Recent Advances in Intrusion Detection: 7th International Symposium, RAID 2004, Sophia Antipolis, France, September 15-17, 2004. Proceedings 7. Springer, pp. 102–124. Doi:10.1007/978-3-540-30143-1_6

Pradhan, M., Nayak, C.K. and Pradhan, S.K. 2020. Intrusion detection system (IDS) and their types, in Securing the internet of things: Concepts, methodologies, tools, and applications. IGI Global, pp. 481–497. Doi:10.4018/978-1-5225-9866-4.ch026

Relan, N.G. and Patil, D.R. 2015. Implementation of network intrusion detection system using variant of decision tree algorithm, in 2015 International Conference on Nascent Technologies in the Engineering Field (ICNTE). IEEE, pp. 1–5. Doi:10.1109/ICNTE.2015.7029925

Siraj, M.J., Ahmad, T. and Ijtihadie, R.M. 2022. Analyzing ANOVA F-test and sequential feature selection for intrusion detection systems., International Journal of Advances in Soft Computing & Its Applications, 14(2). Doi:10.15849/IJASCA.220720.13

Song, J., Zhu, Z. and Price, C. 2014. Feature grouping for intrusion detection based on mutual information, Journal of Communications, 9(12), pp. 987–993. Doi:10.12720/jcm.9.12.987-993

Tapiador, J.E., Orfila, A., Ribagorda, A., and Ramos, B. 2013. Key-recovery attacks on KIDS, a keyed anomaly detection system, IEEE Transactions on Dependable and Secure Computing, 12(3), pp. 312–325. Doi:10.1109/TDSC.2013.39

Zeeshan, M., Riaz, Q., Bilal, M. A., Shahzad, M. K., Jabeen, H., Haider, S. A., and Rahim, A. 2021. Protocol-based deep intrusion detection for dos and ddos attacks using unsw-nb15 and bot-iot data-sets, IEEE Access, 10, pp. 2269–2283. Doi:10.1109/ACCESS.2021.3137201

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