EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 540 (2024) E3S Web of Conf., 540 (2024) 14006 References
Open Access
Issue
E3S Web of Conf.
Volume 540, 2024
1st International Conference on Power and Energy Systems (ICPES 2023)
Article Number 14006
Number of page(s) 15
Section VLSI, Artificial Intelligence and Physics
DOI https://doi.org/10.1051/e3sconf/202454014006
Published online 21 June 2024
  1. U. S. Musa, M. Chhabra, A. Ali, and M. Kaur, “Intrusion detection system using machine learning techniques: A review,” in Proc. Int. Conf. Smart Electron. Commun. (ICOSEC), Sep. (2020), pp. 149–155. [Google Scholar]
  2. Ring, Markus, Sarah Wunderlich, DenizScheuring, Dieter Landes, and Andreas Hotho. “A survey of network-based intrusion detection data sets.” Computers & Security 86 (2019): 147–167. [Google Scholar]
  3. Z.H. Wu, “Information Security Technology and Practice”, (2019) [Google Scholar]
  4. Xin, Yang, Lingshuang Kong, Zhi Liu, Yuling Chen, Yanmiao Li, Hongliang Zhu, MingchengGao, HaixiaHou, and Chunhua Wang. “Machine learning and deep learning methods for cybersecurity.” Ieee access 6 (2018): 35365–35381. [Google Scholar]
  5. Gumusbas, Dilara, and Tulay Yildirim. “AI for Cybersecurity: ML-Based Techniques for Intrusion Detection Systems.” Advances in Machine Learning/Deep Learningbased Technologies: Selected Papers in Honour of Professor Nikolaos G. Bourbakis–Vol. 2 (2022): 117–140. [Google Scholar]
  6. Handa, Anand, Ashu Sharma, and Sandeep K. Shukla. “Machine learning in cybersecurity: A review.” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 9, no. 4 (2019): e1306. [CrossRef] [Google Scholar]
  7. Aleesa, B. Zaidan, A. Zaidan, and N. M. Sahar, “Review of intrusion detection systems based on deep learning techniques: Coherent taxonomy, challenges, motivations, recommendations, substantial analysis and future directions,” Neural Comput. Appl., vol. 32, pp. 1–32, Jul. 2020. [Google Scholar]
  8. S. Osken, E. N. Yildirim, G. Karatas, and L. Cuhaci, “Intrusion detection systems with deep learning: A systematic mapping study,” in Proc. Sci. Meeting Elect.-Electron. Biomed. Eng. Comput. Sci. (EBBT), Apr. 2019, pp. 1–4 [Google Scholar]
  9. Aldweesh, A. Derhab, and A. Z. Emam, “Deep learning approaches for anomalybased intrusion detection systems: A survey, taxonomy, and open issues,” Knowl.- Based Syst., vol. 189, Feb. 2020, Art. no. 105124 [Google Scholar]
  10. R. Vinayakumar, K. P. Soman, and P. Poornachandran, “A comparative analysis of deep learning approaches for network intrusion detection systems (N-IDSs): Deep learning for N-IDSs,” Int. J. Digit. Crime Forensics, vol. 11, no. 3, pp. 65–89, Jul. 2019. [CrossRef] [Google Scholar]
  11. Vinayakumar R, Alazab M, Soman K, Poornachandran P, Al-Nemrat A, Venkatraman S (2019) Deep learning approach for intelligent intrusion detection system. IEEE Access 7:41525–41550. [CrossRef] [Google Scholar]
  12. Almomani A, Alauthman M, Albalas F, Dorgham O, Obeidat A (2020) An online intrusion detection system to cloud computing based on neucube algorithms. In: Cognitive analytics: concepts, methodologies, tools, and applications. IGI Global, pp 1042–1059. [Google Scholar]
  13. Chevalier R, Plaquin D, Villatel M, Hiet G (2020) Intrusion detection systems. US Patent App. 16/486,331. [Google Scholar]
  14. Jacob NM, Wanjala MY (2018) A review of intrusion detection systems. Glob J ComputSciTechnol 5:66 [Google Scholar]
  15. Farzaneh B, Montazeri MA, Jamali S (2019) An anomaly-based ids for detecting attacks in rpl-based internet of things. In: 2019 5th International conference on web research (ICWR). IEEE, pp 61– 66. [Google Scholar]
  16. Kabir E, Hu J, Wang H, Zhuo G (2018) A novel statistical technique for intrusion detection systems. FutGenerComputSyst 79:303–318. [Google Scholar]
  17. Gu, Jie, Lihong Wang, Huiwen Wang, and Shanshan Wang. “A novel approach to intrusion detection using SVM ensemble with feature augmentation.” Computers & Security 86 (2019): 53–62. [CrossRef] [Google Scholar]
  18. Liu, Chao, Jing Yang, and Jinqiu Wu. “Web intrusion detection system combined with feature analysis and SVM optimization.” EURASIP Journal on Wireless Communications and Networking 2020 (2020): 1–9. [CrossRef] [Google Scholar]
  19. Gu J, Lu S (2021) An effective intrusion detection approach using SVM with Naïve Bayes feature embedding. ComputSecur 103:102158. [Google Scholar]
  20. Ghorbani, Ali, and SeyedMostafa Fakhrahmad. “A deep learning approach to network intrusion detection using a proposed supervised sparse auto-encoder and svm.” Iranian Journal of Science and Technology, Transactions of Electrical Engineering 46, no. 3 (2022): 829–846. [CrossRef] [Google Scholar]
  21. Saleh AI, Talaat FM, Labib LM (2019) A hybrid intrusion detection system (hids) based on prioritized k-nearest neighbors and optimized svm classifiers. ArtifIntell Rev 51(3):403–443 [Google Scholar]
  22. Rajpoot, V., Agrawal, R. (2022). ITSA-KNN: Feature Selection Model Based on Improved Tree-Seed Algorithm and K-Nearest Neighbor for Network Intrusion Detection. In: Tiwari, S., Trivedi, M.C., Kolhe, M.L., Mishra, K., Singh, B.K. (eds) Advances in Data and Information Sciences. Lecture Notes in Networks and Systems, vol 318. [Google Scholar]
  23. Wazirali, R. An Improved Intrusion Detection System Based on KNN Hyperparameter Tuning and Cross-Validation. Arab J SciEng 45, 10859–10873 (2020). [CrossRef] [Google Scholar]
  24. Sameera, N., Shashi, M. (2020). Encoding Approach for Intrusion Detection Using PCA and KNN Classifier. In: Raju, K., Govardhan, A., Rani, B., Sridevi, R., Murty, M. (eds) Proceedings of the Third International Conference on Computational Intelligence and Informatics. Advances in Intelligent Systems and Computing, vol 1090. [Google Scholar]
  25. Palmieri F (2019) Network anomaly detection based on logistic regression of nonlinear chaotic invariants. J NetwComputAppl 148:102460 [Google Scholar]
  26. B. Du and F. Deng, “The method of network intrusion detection based on descriptive statistics model and Logistic model,” 2022 International Conference on Machine Learning and Knowledge Engineering (MLKE), Guilin, China, 2022, pp. 160–163 [Google Scholar]
  27. Bhati, N.S., Khari, M. (2022). An Ensemble Model for Network Intrusion Detection Using AdaBoost, Random Forest and Logistic Regression. In: Unhelker, B., Pandey, H.M., Raj, G. (eds) Applications of Artificial Intelligence and Machine Learning. Lecture Notes in Electrical Engineering, vol 925. [Google Scholar]
  28. Çavuşoğlu, Ü. A new hybrid approach for intrusion detection using machine learning methods. ApplIntell 49, 2735–2761 (2019 [Google Scholar]
  29. Tabash, M., Abd Allah, M. and Tawfik, B., 2020. Intrusion detection model using naive bayes and deep learning technique. Int. Arab J. Inf. Technol., 17(2), pp.215–224. [Google Scholar]
  30. Singh, S., 2022. Poly Logarithmic Naive Bayes Intrusion Detection System Using Linear Stable PCA Feature Extraction. Wireless Personal Communications, 125(4), pp.3117–3132. [CrossRef] [Google Scholar]
  31. A.J., Ashu, A., Rajani Kanth, A. (2021). Gaussian Naïve Bayes Based Intrusion Detection System. In: Abraham, A., Jabbar, M., Tiwari, S., Jesus, I. (eds) Proceedings of the 11th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2019). SoCPaR 2019. Advances in Intelligent Systems and Computing, vol 1182. [Google Scholar]
  32. Song, Y., Hyun, S. and Cheong, Y.G., 2021. Analysis of autoencoders for network intrusion detection. Sensors, 21(13), p. 4294. [CrossRef] [PubMed] [Google Scholar]
  33. Kalpana, R. “Recurrent nonsymmetric deep auto encoder approach for network intrusion detection system.” Measurement: Sensors 24 (2022): 100527. [CrossRef] [Google Scholar]
  34. Deng, H. and Yang, T., 2021. Network intrusion detection based on sparse autoencoder and IGA-BP network. Wireless Communications and Mobile Computing, 2021, pp.1–11. [Google Scholar]
  35. Y. N. Kunang, S. Nurmaini, D. Stiawan, A. Zarkasi, Firdaus and Jasmir, “Automatic Features Extraction Using Autoencoder in Intrusion Detection System,” 2018 International Conference on Electrical Engineering and Computer Science (ICECOS), Pangkal, Indonesia, 2018, pp. 219–224. [Google Scholar]
  36. Y. Zhang, X. Chen, D. Guo, M. Song, Y. Teng and X. Wang, “PCCN: Parallel Cross Convolutional Neural Network for Abnormal Network Traffic Flows Detection in Multi-Class Imbalanced Network Traffic Flows,” in IEEE Access, vol. 7, pp. 119904–119916, 2019. [CrossRef] [Google Scholar]
  37. M. H. Haghighat and J. Li, “Intrusion detection system using voting-based neural network,” in Tsinghua Science and Technology, vol. 26, no. 4, pp. 484–495, Aug. 2021 [CrossRef] [Google Scholar]
  38. A. Halbouni, T. S. Gunawan, M. H. Habaebi, M. Halbouni, M. Kartiwi and R. Ahmad, “CNN-LSTM: Hybrid Deep Neural Network for Network Intrusion Detection System,” in IEEE Access, vol. 10, pp. 99837–99849, 2022. [CrossRef] [Google Scholar]
  39. J. Du, K. Yang, Y. Hu and L. Jiang, “NIDS-CNNLSTM: Network Intrusion Detection Classification Model Based on Deep Learning,” in IEEE Access, vol. 11, pp. 24808–24821, 2023, [CrossRef] [Google Scholar]
  40. I. Ullah and Q. H. Mahmoud, “Design and Development of a Deep Learning-Based Model for Anomaly Detection in IoT Networks,” in IEEE Access, vol. 9, pp. 103906–103926, 2021 [CrossRef] [Google Scholar]
  41. Z. Hu, L. Wang, L. Qi, Y. Li and W. Yang, “A Novel Wireless Network Intrusion Detection Method Based on Adaptive Synthetic Sampling and an Improved Convolutional Neural Network,” in IEEE Access, vol. 8, pp. 195741–195751, 2020 [CrossRef] [Google Scholar]
  42. K. Yu, K. Nguyen and Y. Park, “Flexible and Robust Real-Time Intrusion Detection Systems to Network Dynamics,” in IEEE Access, vol. 10, pp. 98959–98969, 2022 [CrossRef] [Google Scholar]
  43. Z. Wu, H. Zhang, P. Wang and Z. Sun, “RTIDS: A Robust Transformer-Based Approach for Intrusion Detection System,” in IEEE Access, vol. 10, pp. 64375–64387, 2022. [CrossRef] [Google Scholar]
  44. F. Alasmary, S. Alraddadi, S. Al-Ahmadi and J. Al-Muhtadi, “ShieldRNN: A Distributed Flow-Based DDoS Detection Solution for IoT Using Sequence Majority Voting,” in IEEE Access, vol. 10, pp. 88263–88275, 2022. [CrossRef] [Google Scholar]
  45. A. A. E. -B. Donkol, A. G. Hafez, A. I. Hussein and M. M. Mabrook, “Optimization of Intrusion Detection Using Likely Point PSO and Enhanced LSTM-RNN Hybrid Technique in Communication Networks,” in IEEE Access, vol. 11, pp. 9469–9482, 2023 [CrossRef] [Google Scholar]
  46. P. Wei, Y. Li, Z. Zhang, T. Hu, Z. Li and D. Liu, “An Optimization Method for Intrusion Detection Classification Model Based on Deep Belief Network,” in IEEE Access, vol. 7, pp. 87593–87605, 2019. [CrossRef] [Google Scholar]
  47. Y. Wu, W. W. Lee, Z. Xu and M. Ni, “Large-Scale and Robust Intrusion Detection Model Combining Improved Deep Belief Network With Feature-Weighted SVM,” in IEEE Access, vol. 8, pp. 98600–98611, 2020. [CrossRef] [Google Scholar]
  48. K. singh and K. J. Mathai, “Performance Comparison of Intrusion Detection System Between Deep Belief Network (DBN)Algorithm and State Preserving Extreme Learning Machine (SPELM) Algorithm,” 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), Coimbatore, India, 2019, [Google Scholar]
  49. Balakrishnan, Nagaraj, Arunkumar Rajendran, Danilo Pelusi, and Vijayakumar Ponnusamy. “Deep Belief Network enhanced intrusion detection system to prevent security breach in the Internet of Things.” Internet of things 14 (2021): 100112. [CrossRef] [Google Scholar]
  50. Belarbi, O., Khan, A., Carnelli, P., Spyridopoulos, T. (2022). An Intrusion Detection System Based on Deep Belief Networks. In: Su, C., Sakurai, K., Liu, F. (eds) Science of Cyber Security. SciSec 2022. Lecture Notes in Computer Science, vol 13580. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.