A Generalized and Robust Nonlinear Approach based on Machine Learning for Intrusion Detection

References

• Abrar, I., Z. Ayub, F. Masoodi, and A. M. Bamhdi. 2020. A machine learning approach for intrusion detection system on NSL-KDD dataset. In 2020 International Conference on Smart Electronics and Communication (ICOSEC), Trichy, India. IEEE.
   Google Scholar
• Abubakar, A. I., H. Chiroma, S. A. Muaz, and L. B. Ila. 2015. A review of the advances in cyber security benchmark datasets for evaluating data-driven based intrusion detection systems. Procedia Computer Science 62:221–34. doi:10.1016/j.procs.2015.08.443.
   Google Scholar
• Almseidin, M., M. Alzubi, S. Kovacs, and M. Alkasassbeh. 2017. Evaluation of machine learning algorithms for intrusion detection system. In 2017 IEEE 15th International Symposium on Intelligent Systems and Informatics (SISY), Subotica, Serbia. IEEE.
   Google Scholar
• Alrowaily, M., F. Alenezi, and Z. Lu. 2019. Effectiveness of machine learning based intrusion detection systems. Security, Privacy, and Anonymity in Computation, Communication, and Storage: 12th International Conference, SpaCCS 2019, Atlanta, GA, USA. Springer.
   Google Scholar
• Amor, N. B., S. Benferhat, and Z. Elouedi. 2004. Naive Bayes vs decision trees in intrusion detection systems. In Proceedings of the 2004 ACM symposium on Applied computing, Nicosia, Cyprus.
   Google Scholar
• Ao, H. 2021. Using machine learning models to detect different intrusion on NSL-KDD. In 2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE), SC, USA. IEEE.
   Google Scholar
• Araki, T., N. Ikeda, D. Shukla, P. K. Jain, N. D. Londhe, V. K. Shrivastava, S. K. Banchhor, L. Saba, A. Nicolaides, S. Shafique, et al. 2016. PCA-based polling strategy in machine learning framework for coronary artery disease risk assessment in intravascular ultrasound: A link between carotid and coronary grayscale plaque morphology. Computer Methods and Programs in Biomedicine 128:137–58. doi:10.1016/j.cmpb.2016.02.004.
   PubMed Web of Science ®Google Scholar
• Baich, M., T. Hamim, N. Sael, and Y. Chemlal. 2022. Machine learning for IoT based networks intrusion detection: A comparative study. Procedia Computer Science 215:742–51. doi:10.1016/j.procs.2022.12.076.
   Google Scholar
• Belavagi, M. C., and B. Muniyal. 2016. Performance evaluation of supervised machine learning algorithms for intrusion detection. Procedia Computer Science 89:117–23. doi:10.1016/j.procs.2016.06.016.
   Google Scholar
• Bhavani, T. T., M. K. Rao, and A. M. Reddy. 2019. Network intrusion detection system using random forest and decision tree machine learning techniques. In First International Conference on Sustainable Technologies for Computational Intelligence: Proceedings of ICTSCI 2019, Jaipur, Rajasthan, India. Springer.
   Google Scholar
• Bindra, N., and M. Sood. 2019. Detecting DDoS attacks using machine learning techniques and contemporary intrusion detection dataset. Automatic Control and Computer Sciences 53 (5):419–28. doi:10.3103/S0146411619050043.
   Web of Science ®Google Scholar
• Biswas, M., V. Kuppili, D. R. Edla, H. S. Suri, L. Saba, R. T. Marinhoe, J. M. Sanches, and J. S. Suri. 2018. Symtosis: A liver ultrasound tissue characterization and risk stratification in optimized deep learning paradigm. Computer Methods and Programs in Biomedicine 155:165–77. doi:10.1016/j.cmpb.2017.12.016.
   PubMed Web of Science ®Google Scholar
• Chakraborty, N. 2013. Intrusion detection system and intrusion prevention system: A comparative study. International Journal of Computing and Business Research (IJCBR) 4 (2):1–8.
   Google Scholar
• Chang, Y.-W., and C.-J. Lin. 2008. Feature ranking using linear SVM. In Proceedings of the Workshop on the Causation and Prediction Challenge at WCCI 2008, Hong Kong.
   Google Scholar
• Chauhan, H., V. Kumar, S. Pundir, and E. S. Pilli. 2013. A comparative study of classification techniques for intrusion detection. In 2013 International Symposium on Computational and Business Intelligence, New Delhi, India. IEEE.
   Google Scholar
• Chen, C.-M., Y.-L. Chen, and H.-C. Lin. 2010. An efficient network intrusion detection. Computer Communications 33 (4):477–84. doi:10.1016/j.comcom.2009.10.010.
   Web of Science ®Google Scholar
• Davis, J. J., and A. J. Clark. 2011. Data preprocessing for anomaly based network intrusion detection: A review. Computers & Security 30 (6–7):353–75. doi:10.1016/j.cose.2011.05.008.
   Web of Science ®Google Scholar
• Devarajan, R., and P. Rao. 2021. An efficient intrusion detection system by using behaviour profiling and statistical approach model. The International Arab Journal of Information Technology 18 (1):114–24.
   Web of Science ®Google Scholar
• Dhanabal, L., and S. Shantharajah. 2015. A study on NSL-KDD dataset for intrusion detection system based on classification algorithms. International Journal of Advanced Research in Computer and Communication Engineering 4 (6):446–52.
   Google Scholar
• Elhamahmy, M., H. N. Elmahdy, and I. A. Saroit. 2010. A new approach for evaluating intrusion detection system. CiiT International Journal of Artificial Intelligent Systems and Machine Learning 2 (11):290–98.
   Google Scholar
• Friha, O., M. A. Ferrag, M. Benbouzid, T. Berghout, B. Kantarci, and K.-K. R. Choo. 2023. 2DF-IDS: Decentralized and differentially private federated learning-based intrusion detection system for industrial IoT. Computers & Security 127:103097. doi:10.1016/j.cose.2023.103097.
   Web of Science ®Google Scholar
• Garcia-Teodoro, P., J. Díaz-Verdejo, G. Maciá-Fernández, and E. Vázquez. 2009. Anomaly-based network intrusion detection: Techniques, systems and challenges. Computers & Security 28 (1–2):18–28. doi:10.1016/j.cose.2008.08.003.
   Web of Science ®Google Scholar
• Hady, A. A., A. Ghubaish, T. Salman, D. Unal, and R. Jain. 2020. Intrusion detection system for healthcare systems using medical and network data: A comparison study. Institute of Electrical and Electronics Engineers Access 8:106576–84. doi:10.1109/ACCESS.2020.3000421.
   Google Scholar
• Ibrahimi, K., and M. Ouaddane. 2017. Management of intrusion detection systems based-KDD99: Analysis with LDA and PCA. In 2017 International Conference on Wireless Networks and Mobile Communications (WINCOM), Rabat, Morocco. IEEE.
   Google Scholar
• Jamthikar, A. D., D. Gupta, L. E. Mantella, L. Saba, J. R. Laird, A. M. Johri, and J. S. Suri. 2021. Multiclass machine learning vs. conventional calculators for stroke/CVD risk assessment using carotid plaque predictors with coronary angiography scores as gold standard: A 500 participants study. The International Journal of Cardiovascular Imaging 37 (4):1171–87. doi:10.1007/s10554-020-02099-7.
   PubMed Web of Science ®Google Scholar
• Jiang, H., Z. He, G. Ye, and H. Zhang. 2020. Network intrusion detection based on PSO-XGBoost model. Institute of Electrical and Electronics Engineers Access 8:58392–401. doi:10.1109/ACCESS.2020.2982418.
   Google Scholar
• Johri, A. M., K. V. Singh, L. E. Mantella, L. Saba, A. Sharma, J. R. Laird, K. Utkarsh, I. M. Singh, S. Gupta, M. S. Kalra, et al. 2022. Deep learning artificial intelligence framework for multiclass coronary artery disease prediction using combination of conventional risk factors, carotid ultrasound, and intraplaque neovascularization. Computers in Biology and Medicine 150:106018. doi:10.1016/j.compbiomed.2022.106018.
   PubMed Web of Science ®Google Scholar
• Jyothsna, V., V. Prasad, and K. M. Prasad. 2011. A review of anomaly based intrusion detection systems. International Journal of Computer Applications 28 (7):26–35. doi:10.5120/3399-4730.
   Google Scholar
• Khafajeh, H. 2020. An efficient intrusion detection approach using light gradient boosting. Journal of Theoretical & Applied Information Technology 98 (5):825–35.
   Google Scholar
• Khraisat, A., I. Gondal, P. Vamplew, and J. Kamruzzaman. 2019. Survey of intrusion detection systems: Techniques, datasets and challenges. Cybersecurity 2 (1):1–22. doi:10.1186/s42400-019-0038-7.
   Google Scholar
• Kilincer, I. F., F. Ertam, and A. Sengur. 2021. Machine learning methods for cyber security intrusion detection: Datasets and comparative study. Computer Networks 188:107840. doi:10.1016/j.comnet.2021.107840.
   Web of Science ®Google Scholar
• Kim, K., S. Nalluri, A. Kashinath, Y. Wang, S. Mohan, M. Pajic, and B. Li. 2020. Security analysis against spoofing attacks for distributed UAVs. Workshop on Decentralized IoT Systems and Security (DISS) 2020, San Diego, CA, USA.
   Google Scholar
• Kizza, J. M., W. Kizza, and Wheeler. 2013. Guide to computer network security. Vol. 8.
   Google Scholar
• Konstantonis, G., K. V. Singh, P. P. Sfikakis, A. D. Jamthikar, G. D. Kitas, S. K. Gupta, L. Saba, K. Verrou, N. N. Khanna, Z. Ruzsa, et al. 2022. Cardiovascular disease detection using machine learning and carotid/femoral arterial imaging frameworks in rheumatoid arthritis patients. Rheumatology International 42 (2):215–39. doi:10.1007/s00296-021-05062-4.
   PubMed Web of Science ®Google Scholar
• Kumar, V., and O. P. Sangwan. 2012. Signature based intrusion detection system using SNORT. International Journal of Computer Applications & Information Technology 1 (3):35–41.
   Google Scholar
• Kuppili, V., M. Biswas, A. Sreekumar, H. S. Suri, L. Saba, D. R. Edla, R. T. Marinhoe, J. M. Sanches, and J. S. Suri. 2017. Extreme learning machine framework for risk stratification of fatty liver disease using ultrasound tissue characterization. Journal of Medical Systems 41 (10):1–20. doi:10.1007/s10916-017-0797-1.
   PubMed Web of Science ®Google Scholar
• Le, T. T. H., Y. E. Oktian, and H. Kim. 2022. XGBoost for imbalanced multiclass classification-based industrial internet of things intrusion detection systems. Sustainability 14 (14):8707. doi:10.3390/su14148707.
   Web of Science ®Google Scholar
• Leevy, J. L., and T. M. Khoshgoftaar. 2020. A survey and analysis of intrusion detection models based on CSE-CIC-IDS2018 big data. Journal of Big Data 7 (1):1–19. doi:10.1186/s40537-020-00382-x.
   Google Scholar
• Liu, G., H. Zhao, F. Fan, G. Liu, Q. Xu, and S. Nazir. 2022. An enhanced intrusion detection model based on improved kNN in WSNs. Sensors 22 (4):1407. doi:10.3390/s22041407.
   PubMed Web of Science ®Google Scholar
• Mallik, A. 2019. Man-in-the-middle-attack: Understanding in simple words. Cyberspace: Jurnal Pendidikan Teknologi Informasi 2 (2):109–34. doi:10.22373/cj.v2i2.3453.
   Google Scholar
• Maniruzzaman, M., M. Jahanur Rahman, B. Ahammed, M. M. Abedin, H. S. Suri, M. Biswas, A. El-Baz, P. Bangeas, G. Tsoulfas, J. S. Suri, et al. 2019. Statistical characterization and classification of colon microarray gene expression data using multiple machine learning paradigms. Computer Methods and Programs in Biomedicine 176:173–93. doi:10.1016/j.cmpb.2019.04.008.
   PubMed Web of Science ®Google Scholar
• Mármol Campos, E., J. L. Hernández Ramos, A. González Vidal, G. Baldini, and A. Skarmeta. 2024. Misbehavior detection in intelligent transportation systems based on federated learning. Internet of Things.
   Google Scholar
• Modi, C., D. Patel, B. Borisaniya, H. Patel, A. Patel, and M. Rajarajan. 2013. A survey of intrusion detection techniques in cloud. Journal of Network and Computer Applications 36 (1):42–57. doi:10.1016/j.jnca.2012.05.003.
   Web of Science ®Google Scholar
• Moustafa, N., and J. Slay. 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):18–31. doi:10.1080/19393555.2015.1125974.
   Web of Science ®Google Scholar
• Naiping, S., and Z. Genyuan. 2010. A study on intrusion detection based on data mining. In 2010 International Conference of Information Science and Management Engineering, Shaanxi, China. IEEE.
   Google Scholar
• Pai, V., and N. Adesh. 2021. Comparative analysis of machine learning algorithms for intrusion detection. In IOP Conference Series: Materials Science and Engineering, Bengaluru, India. IOP Publishing.
   Google Scholar
• ParwANI, D., A. Dutta, P. K. Shukla, and M. Tahiliyani. 2015. Various techniques of DDoS attacks detection & prevention at cloud: A survey. Oriental Journal of Computer Science and Technology 8 (2):110–20.
   Google Scholar
• Pathak, A., and S. Pathak. 2020. Study on decision tree and KNN algorithm for intrusion detection system. International Journal of Engineering Research 9 (5):376–81. doi:10.17577/IJERTV9IS050303.
   Google Scholar
• Rahman, M. A., A. T. Asyhari, L. S. Leong, G. B. Satrya, M. Hai Tao, and M. F. Zolkipli. 2020. Scalable machine learning-based intrusion detection system for IoT-enabled smart cities. Sustainable Cities and Society 61:102324. doi:10.1016/j.scs.2020.102324.
   Web of Science ®Google Scholar
• Rashid, O. F. 2020. DNA encoding for misuse intrusion detection system based on UNSW-NB15 data set. Iraqi Journal of Science 3408–16. doi:10.24996/ijs.2020.61.12.29.
   Google Scholar
• Rastogi, S., A. Shrotriya, M. Kumar Singh, and R. V. Potukuchi. 2022. An analysis of intrusion detection classification using supervised machine learning algorithms on NSL-KDD dataset. Journal of Computing Research and Innovation (JCRINN) 7 (1):118–30. doi:10.24191/jcrinn.v7i1.274.
   Google Scholar
• Rauf, B., H. Abbas, M. Usman, T. A. Zia, W. Iqbal, Y. Abbas, and H. Afzal. 2022. Application threats to exploit northbound interface vulnerabilities in software defined networks. ACM Computing Surveys (CSUR) 54 (6):1–36. doi:10.1145/3453648.
   Web of Science ®Google Scholar
• Revathi, S., and A. Malathi. 2013. A detailed analysis on NSL-KDD dataset using various machine learning techniques for intrusion detection. International Journal of Engineering Research & Technology (IJERT) 2 (12):1848–53.
   Google Scholar
• Ring, M., S. Wunderlich, D. Gruedl, D. Landes, and A. Hotho. 2017. Technical Report CIDDS-001 data set.
   Google Scholar
• Ring, M., S. Wunderlich, D. Scheuring, D. Landes, and A. Hotho. 2019. A survey of network-based intrusion detection data sets. Computers & Security 86:147–67. doi:10.1016/j.cose.2019.06.005.
   Web of Science ®Google Scholar
• Roschke, S., F. Cheng, and C. Meinel. 2009. Intrusion detection in the cloud. In 2009 Eighth IEEE International Conference on Dependable, Autonomic and Secure Computing, Chengdu, China. IEEE.
   Google Scholar
• Rubio, J. E., C. Alcaraz, R. Roman, and J. Lopez. 2017. Analysis of intrusion detection systems in industrial ecosystems. In 14th International Conference on Security and Cryptography (SECRYPT 2017), Madrid, Spain.
   Google Scholar
• Saba, L., S. K. Banchhor, N. D. Londhe, T. Araki, J. R. Laird, A. Gupta, A. Nicolaides, and J. S. Suri. 2017. Web-based accurate measurements of carotid lumen diameter and stenosis severity: An ultrasound-based clinical tool for stroke risk assessment during multicenter clinical trials. Computers in Biology and Medicine 91:306–17. doi:10.1016/j.compbiomed.2017.10.022.
   PubMed Web of Science ®Google Scholar
• Saba, L., S. K. Banchhor, H. S. Suri, N. D. Londhe, T. Araki, N. Ikeda, K. Viskovic, S. Shafique, J. R. Laird, A. Gupta, et al. 2016. Accurate cloud-based smart IMT measurement, its validation and stroke risk stratification in carotid ultrasound: A web-based point-of-care tool for multicenter clinical trial. Computers in Biology and Medicine 75:217–34. doi:10.1016/j.compbiomed.2016.06.010.
   PubMed Web of Science ®Google Scholar
• Saheed, Y. K., A. Idris Abiodun, S. Misra, M. Kristiansen Holone, and R. Colomo-Palacios. 2022. A machine learning-based intrusion detection for detecting internet of things network attacks. Alexandria Engineering Journal 61 (12):9395–409. doi:10.1016/j.aej.2022.02.063.
   Web of Science ®Google Scholar
• Sapalo Sicato, J. C., P. K. Sharma, V. Loia, and J. H. Park. 2019. VPNFilter malware analysis on cyber threat in smart home network. Applied Sciences 9 (13):2763. doi:10.3390/app9132763.
   Google Scholar
• Sarker, I. H., Y. B. Abushark, F. Alsolami, and A. I. Khan. 2020. Intrudtree: A machine learning based cyber security intrusion detection model. Symmetry 12 (5):754. doi:10.3390/sym12050754.
   Google Scholar
• Sayghe, A., Y. Hu, I. Zografopoulos, X. Liu, R. G. Dutta, Y. Jin, and C. Konstantinou. 2020. Survey of machine learning methods for detecting false data injection attacks in power systems. IET Smart Grid 3 (5):581–95. doi:10.1049/iet-stg.2020.0015.
   Google Scholar
• Shrivastava, V. K., N. D. Londhe, R. S. Sonawane, and J. S. Suri. 2017. A novel and robust Bayesian approach for segmentation of psoriasis lesions and its risk stratification. Computer Methods and Programs in Biomedicine 150:9–22. doi:10.1016/j.cmpb.2017.07.011.
   PubMed Web of Science ®Google Scholar
• Silva, B. R., R. Silveira, M. Silva Neto, P. Cortez, and D. Gomes. 2021. A comparative analysis of undersampling techniques for network intrusion detection systems design. Journal of Communication and Information Systems 36 (1):31–43. doi:10.14209/jcis.2021.3.
   Google Scholar
• Singh, A. P., and M. D. Singh. 2014. Analysis of host-based and network-based intrusion detection system. International Journal of Computer Network and Information Security 6 (8):41–47. doi:10.5815/ijcnis.2014.08.06.
   Google Scholar
• Singh, J., K. Sharma, M. Wazid, and A. K. Das. 2023. SINN-RD: Spline interpolation-envisioned neural network-based ransomware detection scheme. Computers & Electrical Engineering 106:108601. doi:10.1016/j.compeleceng.2023.108601.
   Web of Science ®Google Scholar
• Souhail, M. 2019. Network based intrusion detection using the UNSW-NB15 dataset. International Journal of Computing and Digital Systems 8 (5):477–87. doi:10.12785/ijcds/080505.
   Google Scholar
• Srivastava, S. K., S. K. Singh, and J. S. Suri. 2019. Effect of incremental feature enrichment on healthcare text classification system: A machine learning paradigm. Computer Methods and Programs in Biomedicine 172:35–51. doi:10.1016/j.cmpb.2019.01.011.
   PubMed Web of Science ®Google Scholar
• Srivastava, S. K., S. K. Singh, and J. S. Suri. 2020. A healthcare text classification system and its performance evaluation: A source of better intelligence by characterizing healthcare text. Cognitive Informatics, Computer Modelling, and Cognitive Science 2:319–69.
   Google Scholar
• Suri, J. S. 2020. Low-cost preventive screening using carotid ultrasound in patients with diabetes. Frontiers in Bioscience-Landmark 25 (6):1132–71. doi:10.1274/4850.
   PubMedGoogle Scholar
• Suri, J. S., S. Agarwal, G. Chabert, A. Carriero, A. Paschè, P. Danna, L. Saba, A. Mehmedović, G. Faa, I. Singh, et al. 2022. COVLIAS 2.0-cXAI: Cloud-based explainable deep learning system for COVID-19 lesion localization in computed tomography scans. Diagnostics 12 (6):1482. doi:10.3390/diagnostics12061482.
   Web of Science ®Google Scholar
• Suri, J. S., and R. M. Rangayyan. 2006. Breast imaging, mammography, and computer-aided diagnosis of breast cancer. Bellingham, WA, USA: SPIE.
   Google Scholar
• Tan, X., S. Su, Z. Huang, X. Guo, Z. Zuo, X. Sun, and L. Li. 2019. Wireless sensor networks intrusion detection based on SMOTE and the random forest algorithm. Sensors 19 (1):203. doi:10.3390/s19010203.
   PubMed Web of Science ®Google Scholar
• Tao, K., J. Li, and S. Sampalli. 2008. Detection of Spoofed MAC Addresses in 802.11 Wireless Networks. In International Conference on E-Business and Telecommunications, Barcelona, Spain. Springer.
   Google Scholar
• Tauqeer, H., M. M. Iqbal, A. Ali, S. Zaman, and M. U. Chaudhry. 2022. Cyberattacks detection in IoMT using machine learning techniques. Journal of Computing & Biomedical Informatics 4 (1):13–20. doi:10.56979/401/2022/80.
   Google Scholar
• Teji, J. S., S. Jain, S. K. Gupta, and J. S. Suri. 2022. NeoAI 1.0: Machine learning-based paradigm for prediction of neonatal and infant risk of death. Computers in Biology and Medicine 147:105639. doi:10.1016/j.compbiomed.2022.105639.
   PubMed Web of Science ®Google Scholar
• Teng, S., N. Wu, H. Zhu, L. Teng, and W. Zhang. 2018. SVM-DT-based adaptive and collaborative intrusion detection. IEEE/CAA Journal of Automatica Sinica 5 (1):108–18. doi:10.1109/JAS.2017.7510730.
   Google Scholar
• Tiwari, M., K. V. Arya, R. Choudhari, and K. S. Choudhary 2009. Designing intrusion detection to detect black hole and selective forwarding attack in WSN based on local information. In 2009 Fourth International Conference on Computer Sciences and Convergence Information Technology, Seoul, Korea (South). IEEE.
   Google Scholar
• Tu, S., M. Waqas, A. Badshah, M. Yin, and G. Abbas. 2023. Network intrusion detection system (NIDS) based on pseudo-siamese stacked autoencoders in fog computing. IEEE Transactions on Services Computing.
   Web of Science ®Google Scholar
• Viegas, E., A. O. Santin, and V. Abreu. 2021. Machine learning intrusion detection in big data era: A multi-objective approach for longer model lifespans. IEEE Transactions on Network Science and Engineering 8 (1):366–76. doi:10.1109/TNSE.2020.3038618.
   Web of Science ®Google Scholar
• Waqas, M., S. Tu, Z. Halim, S. U. Rehman, G. Abbas, and Z. H. Abbas. 2022. The role of artificial intelligence and machine learning in wireless networks security: Principle, practice and challenges. Artificial Intelligence Review 55 (7):5215–61. doi:10.1007/s10462-022-10143-2.
   Web of Science ®Google Scholar
• Yan, B., and G. Han. 2018. Effective feature extraction via stacked sparse autoencoder to improve intrusion detection system. Institute of Electrical and Electronics Engineers Access 6:41238–48. doi:10.1109/ACCESS.2018.2858277.
   Google Scholar
• Yang, Y., K. McLaughlin, T. Littler, S. Sezer, and H. F. Wang 2013. Rule-based intrusion detection system for SCADA networks. In 2nd IET Renewable Power Generation Conference (RPG 2013), Beijing, China.
   Google Scholar
• Yassin, W., N. I. Udzir, Z. Muda, A. Abdullah, and M. T. Abdullah. 2012. A cloud-based intrusion detection service framework. Proceedings Title: 2012 International Conference on Cyber Security, Cyber Warfare and Digital Forensic (CyberSec), IEEE.
   Google Scholar
• Yassin, W., N. I. Udzir, Z. Muda, and M. N. Sulaiman. 2013. Anomaly-based intrusion detection through k-means clustering and naives Bayes classification. In Proceedings of the 4 th International Conference on Computing and Informatics, ICOCI 2013, Sarawak, Malaysia.
   Google Scholar
• Yihunie, F., E. Abdelfattah, and A. Regmi. 2019. Applying machine learning to anomaly-based intrusion detection systems. In 2019 IEEE Long Island Systems, Applications and Technology Conference (LISAT), Farmingdale, NY, USA. IEEE.
   Google Scholar
• Zamani, M., and M. Movahedi. 2013. Machine learning techniques for intrusion detection. arXiv preprint arXiv:1312.2177.
   Google Scholar
• Zebin, T., S. Rezvy, and Y. Luo. 2022. An explainable ai-based intrusion detection system for dns over https (doh) attacks. IEEE Transactions on Information Forensics and Security 17:2339–49. doi:10.1109/TIFS.2022.3183390.
   Web of Science ®Google Scholar
• Zhang, J., B. Gong, M. Waqas, S. Tu, and S. Chen. 2023. Many-objective optimization based intrusion detection for in-vehicle network security. IEEE Transactions on Intelligent Transportation Systems.
   PubMedGoogle Scholar
• Zhang, Q., G. Hu, and W. Feng. 2010. Design and performance evaluation of a machine learning-based method for intrusion detection. Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing 2010:69–83.
   Google Scholar
• Zhang, Y., and Q. Liu. 2022. On IoT intrusion detection based on data augmentation for enhancing learning on unbalanced samples. Future Generation Computer Systems 133:213–27. doi:10.1016/j.future.2022.03.007.
   Web of Science ®Google Scholar
• Zhang, Z., H. Al Hamadi, E. Damiani, C. Y. Yeun, and F. Taher. 2022. Explainable artificial intelligence applications in cyber security: State-of-the-art in research. IEEE Access.
   Google Scholar