Синергетические подходы к улучшению обнаружения вторжений в Интернет вещей (IoT): балансировка характеристик с помощью комбинированного обучения
Ключевые слова:
минимаксная нормализация, SMOTE-Tomek Link, алгоритм BTLBO, CNN с XGB, оптимизатор AdamАннотация
Интернет вещей (IoT) играет важную роль в обеспечении безопасности, предотвращая несанкционированный доступ, заражения вредоносным ПО и злонамеренные действия. IoT отслеживает сетевой трафик, а также поведение устройств для выявления потенциальных угроз и принятия соответствующих мер противодействия. Тем не менее, существует потребность в системе обнаружения вторжений (IDS) IoT с улучшенными возможностями обобщения, использующей глубокое обучение и передовые методы обнаружения аномалий. В этом исследовании представлен инновационный подход к IoT IDS, который сочетает в себе SMOTE-Tomek и BTLBO, CNN с XGB классификатором, который направлен на устранение дисбаланса данных, повышение производительности модели, снижение количества неправильных классификаций и улучшение общего качества набора данных. Предложенная система обнаружения вторжений IoT, используя набор данных IoT-23, достигает 99,90% точности и низкого уровня ошибок, требуя при этом существенно меньше времени выполнения. Эта работа представляет собой значительный шаг вперед в области безопасности IoT, предлагая надежное и эффективное решение IDS, адаптированное к меняющимся проблемам взаимосвязанного мира.
Литература
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2. Apostol I., Preda M., Nila C., Bica I. IoT botnet anomaly detection using unsupervised deep learning. Electronics. 2021. vol. 10(16). DOI: 10.3390/electronics10161876.
3. Raghuvanshi A., Singh U.K. WITHDRAWN: Internet of Things for smart cities-security issues and challenges. 2020. DOI: 10.1016/j.matpr.2020.10.849.
4. Lokhande M.P., Patil D.D., Patil L.V., Shabaz M. Machine-to-machine communication for device identification and classification in secure telerobotics surgery. Security and communication networks. 2021. no. 1. pp. 1–16. DOI: 10.1155/2021/5287514.
5. Butun I., Osterberg P., Song H. Security of the Internet of Things: Vulnerabilities, attacks, and countermeasures. IEEE Communications Surveys and Tutorials. 2019. vol. 22(1). pp. 616–644.
6. Zahra S.R., Chishti M.A. Ransomware and internet of things: A new security nightmare. In 2019 9th international conference on cloud computing, data science & engineering (confluence). IEEE, 2019. pp. 551–555.
7. Makhdoom I., Abolhasan M., Lipman J., Liu R.P., Ni W. Anatomy of threats to the internet of things. IEEE communications surveys and tutorials. 2018. vol. 21(2). pp. 1636–1675.
8. Liang L., Zheng K., Sheng Q., Huang X. A denial of service attack method for an IoT system. In 8th international conference on Information Technology in Medicine and Education (ITME). IEEE, 2016. pp. 360–364.
9. Gray C., Ayre R., Hinton K., Tucker R.S. Power consumption of IoT access network technologies. In IEEE International Conference on Communication Workshop (ICCW). IEEE, 2015. pp. 2818–2823.
10. Gormuş S., Aydın H., Ulutaş G. Security for the internet of things: a survey of existing mechanisms, protocols and open research issues. Journal of the Faculty of Engineering and Architecture of Gazi University. 2018. vol. 33(4). pp. 1247–1272.
11. Carracedo J.M., Milliken M., Chouhan P.K., Scotney B., Lin Z., Sajjad A., Shackleton M. Cryptography for security in IoT. In Fifth International Conference on Internet of Things: Systems, Management and Security. IEEE, 2018. pp. 23–30.
12. Karati A., Fan C.I., Hsu R.H. Provably secure and generalized signcryption with public verifiability for secure data transmission between resource-constrained IoT devices. IEEE Internet of Things Journal. 2019. vol. 6(6). pp. 10431–10440.
13. Fang D., Qian Y., Hu R.Q. A flexible and efficient authentication and secure data transmission scheme for IoT applications. IEEE Internet of Things Journal. 2020. vol. 7(4). pp. 3474–3484.
14. Chaabouni N., Mosbah M., Zemmari A., Sauvignac C., Faruki P. Network intrusion detection for IoT security based on learning techniques. IEEE Communications Surveys and Tutorials. 2019. vol. 21(3). pp. 2671–2701.
15. Aldweesh A., Derhab A., Emam A.Z. Deep learning approaches for anomaly-based intrusion detection systems: A survey, taxonomy, and open issues. Knowledge-Based Systems. 2020. vol. 189(5). DOI: 10.1016/j.knosys.2019.105124.
16. Albulayhi K., Sheldon F.T. An adaptive deep-ensemble anomaly-based intrusion detection system for the internet of things. In 2021 IEEE World AI IoT Congress (AIIoT). IEEE, 2021. pp. 0187–0196.
17. Alrubayyi H., Goteng G., Jaber M., Kelly J. Challenges of malware detection in the IoT and a review of artificial immune system approaches. Journal of Sensor and Actuator Networks. 2021. vol. 10(4). DOI: 10.3390/jsan10040061.
18. Al-Turaiki I., Altwaijry N. A convolutional neural network for improved anomaly-based network intrusion detection. Big Data. 2021. vol. 9(3). pp. 233–252.
19. Lam N.T. Detecting unauthorized network intrusion based on network traffic using behavior analysis techniques. International Journal of Advanced Computer Science and Applications. 2021. vol. 12(4). DOI: 10.14569/IJACSA.2021.0120407.
20. Aljumah A. IoT-based intrusion detection system using convolution neural networks. PeerJ Computer Science. 2021. vol. 7. DOI: 10.7717/peerj-cs.721.
21. Akhtar M.S., Feng T. Deep learning-based framework for the detection of cyberattack using feature engineering. Security and Communication Networks, 2021. no. 1. DOI: 10.1155/2021/6129210.
22. Liu C., Gu Z., Wang J. A hybrid intrusion detection system based on scalable K-means+ random forest and deep learning. IEEE Access. 2021. vol. 9. pp. 75729–75740.
23. Thilagam T., Aruna R. Intrusion detection for network based cloud computing by custom RC-NN and optimization. ICT Express. 2021. vol. 7(4). pp. 512–520.
24. Kanna P.R., Santhi P. Unified deep learning approach for efficient intrusion detection system using integrated spatial-temporal features. Knowledge-Based Systems. 2021. vol. 226. DOI: 10.1016/j.knosys.2021.107132.
25. Yin S.L., Zhang X.L., Liu S. Intrusion detection for capsule networks based on dual routing mechanism. Computer Networks. 2021. vol. 197. DOI: 10.1016/j.knosys.2021.107132.
26. Khan A.S., Ahmad Z., Abdullah J., Ahmad F. A spectrogram image-based network anomaly detection system using deep convolutional neural network. IEEE access. 2021. vol. 9. pp. 87079–87093.
27. Chen Y., Lin Q., Wei W., Ji J., Wong K.C., Coello C.A.C. Intrusion detection using multi-objective evolutionary convolutional neural network for Internet of Things in Fog computing. Knowledge-Based Systems. 2022. vol. 244. DOI: 10.1016/j.knosys.2022.108505.
28. Dahou A., Abd Elaziz M., Chelloug S.A., Awadallah M.A., Al-Betar M.A., Al-Qaness M.A., Forestiero A. 2022. Intrusion detection system for IoT based on deep learning and modified reptile search algorithm. Computational Intelligence and Neuroscience. 2022. no. 1. DOI: 10.1155/2022/6473507.
29. Haq M.A., Rahim Khan M.A., AL-Harbi T. Development of PCCNN-based network intrusion detection system for EDGE computing. Computers, Materials and Continua. 2022. vol. 71(1). DOI: 10.32604/cmc.2022.018708.
30. Albulayhi K., Abu Al-Haija Q., Alsuhibany S.A., Jillepalli A.A., Ashrafuzzaman M., Sheldon F.T. IoT intrusion detection using machine learning with a novel high performing feature selection method. Applied Sciences. 2022. vol. 12(10). DOI: 10.3390/app12105015.
31. Stoyanova M., Nikoloudakis Y., Panagiotakis S., Pallis E., Markakis E.K. A survey on the internet of things (IoT) forensics: challenges, approaches, and open issues. IEEE Communications Surveys and Tutorials. 2020. vol. 22(2). pp. 1191–1221.
32. Henderi H., Wahyuningsih T., Rahwanto E. Comparison of Min-Max normalization and Z-Score Normalization in the K-nearest neighbor (kNN) Algorithm to Test the Accuracy of Types of Breast Cancer. International Journal of Informatics and Information Systems. 2021. vol. 4(1). pp. 13–20.
33. Chawla N.V., Bowyer K.W., Hall L.O., Kegelmeyer W.P. SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research. 2002. vol. 16. pp. 321–357.
34. Allam M., Nandhini M. Optimal feature selection using binary teaching learning based optimization algorithm. Journal of King Saud University-Computer and Information Sciences. 2022. vol. 34(2). pp. 329–341.
35. Smys S., Basar A., Wang H. Hybrid intrusion detection system for internet of things (IoT). Journal of ISMAC 2020. vol. 2(04). pp. 190–199.
36. Raichura M., Chothani N., Patel D. Efficient CNN‐XGBoost technique for classification of power transformer internal faults against various abnormal conditions. IET Generation, Transmission and Distribution. 2021. vol. 15(5). pp. 972–985.
37. Ullah I., Mahmoud Q.H. Design and development of RNN anomaly detection model for IoT networks. IEEE Access. 2022. vol. 10. pp. 62722–62750.
38. Susilo B., Sari R.F. Intrusion detection in IoT networks using deep learning algorithm. Information. 2020. vol. 11(5). DOI: 10.3390/info11050279.
39. Soliman S., Oudah W., Aljuhani A. Deep learning-based intrusion detection approach for securing industrial Internet of Things. Alexandria Engineering Journal. 2023. vol. 81. pp. 371–383.
40. Khelil H., Brahimi M. Toward an efficient web service composition based on an improved BTLBO algorithm. The Journal of Supercomputing. 2024. vol. 80(7). pp. 8592–8613.
41. Ullah I., Mahmoud Q.H. A framework for anomaly detection in IoT networks using conditional generative adversarial networks. IEEE Access. 2021. vol. 9. pp. 165907–165931.
42. Khuat T.T., Le M.H. Binary teaching–learning-based optimization algorithm with a new update mechanism for sample subset optimization in software defect prediction. Soft Computing. 2019. vol. 23(20). pp. 9919–9935.
43. Nazir A., He J., Zhu N., Qureshi S.S., Qureshi S.U., Ullah F., Wajahat A., Pathan M.S. (2024). A deep learning-based novel hybrid CNN-LSTM architecture for efficient detection of threats in the IoT ecosystem. Ain Shams Engineering Journal. 2024. vol. 15. no. 7. DOI: 10.1016/j.asej.2024.102777.
44. Gao X., Jamil N., Ramli M.I., Ariffin S.M.Z.S.Z. A Comparative Analysis of Combination of CNN-Based Models with Ensemble Learning on Imbalanced Data. JOIV: International Journal on Informatics Visualization. 2024. vol. 8. no. 1. pp. 456–464.
45. Zawaideh F.H., Al-Asad G., Swaneh G., Batainah S., Bakkar H. Intrusion Detection System for (IoI) Networks Using Convolutional Neural Network (CNN) and Xgboost Algorithm. Journal of Theoretical and Applied Information Technology. 2024. vol. 102(4). pp. 1750–1759.
46. Swana E.F., Doorsamy W., Bokoro P. Tomek link and SMOTE approaches for machine fault classification with an imbalanced dataset. Sensors. 2022. vol. 22(9). DOI: 10.3390/s22093246.
Опубликован
2024-11-07
Как цитировать
Нараянарао, Ч., Мандапати, В. Р., & Бодду, Б. Р. (2024). Синергетические подходы к улучшению обнаружения вторжений в Интернет вещей (IoT): балансировка характеристик с помощью комбинированного обучения. Информатика и автоматизация, 23(6), 1845-1868. https://doi.org/10.15622/ia.23.6.10
Раздел
Информационная безопасность
Copyright (c) Chokkapu Narayanarao
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