How to cite this paper
APA: Laila, D., Obeidat, I., Amin, M., Alqutaish, A., Obeidat, M & Aldhyani, T. (2026). Deep learning-driven multi-layer intrusion detection and prevention framework for resilient defense against adaptive evasion techniques in modern networks. International Journal of Data and Network Science, 10(1), 37-52.
Chicago/Turabian: Laila, D., Obeidat, I., Amin, M., Alqutaish, A., Obeidat, M & Aldhyani, T. 2026. "Deep learning-driven multi-layer intrusion detection and prevention framework for resilient defense against adaptive evasion techniques in modern networks." International Journal of Data and Network Science 10, no. 1 (2026): 37-52.
AMA: Laila, D., Obeidat, I., Amin, M., Alqutaish, A., Obeidat, M & Aldhyani, T. Deep learning-driven multi-layer intrusion detection and prevention framework for resilient defense against adaptive evasion techniques in modern networks. International Journal of Data and Network Science. 2026;10(1):37-52.
References
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Abu Laila, D. (2025). Responsive Machine Learning Framework and Lightweight Utensil of Prevention of Evasion Attacks in the IoT-Based IDS. STAP Journal of Security Risk Management, 2025(1), 59–70. https://doi.org/10.63180/jsrm.thestap.2025.1.3
Aljumaiah, O., Jiang, W., Reddy Addula, S., & Amin Almaiah, M. (2025). Analyzing cybersecurity risks and threats in IT infrastructure based on NIST framework. Journal of Cyber Security and Risk Auditing, 2025(2), 12–26. https://doi.org/10.63180/jcsra.thestap.2025.2.2
Almaayah, M., & Sulaiman, R. B. (2024). Cyber risk management in the Internet of Things: Frameworks, models, and best practices. STAP Journal of Security Risk Management, 2024(1), 3–23. https://doi.org/10.63180/jsrm.thestap.2024.1.1
Almaiah, M. A., & Kadel, R. (2025). Leveraging ACO, GA, and GWO for enhancing port scan attack detection using machine learning. Journal of Cyber Security and Risk Auditing, 2025(4), 306–326. https://doi.org/10.63180/jcsra.thestap.2025.4.9
Almarshood, R., & Rahman, M. M. H. (2025). Enhancing Intrusion Detection Systems by Using Machine Learning in Smart Cities: Issues, Challenges and Future Research Direction. STAP Journal of Security Risk Management, 2025(1), 3–21. https://doi.org/10.63180/jsrm.thestap.2025.1.1
Al-olaqi, M., Al-gailani, A., & Rahman, M. M. H. (2025). Comprehensive study of SQL injection attacks mitigation methods and future directions. Journal of Cyber Security and Risk Auditing, 2025(4), 347–365. https://doi.org/10.63180/jcsra.thestap.2025.4.11
Alsarhan, A., Al-Aiash, I., Al-Fraihat, D., Aljaidi, M., & Laila, D. A. A. H. A. (2024). Expert phishing detection system. In 2024 IEEE International Conference on Industry 4.0, Artificial Intelligence, IEEE.
Alshinwan, M., Memon, A. G., Ghanem, M. C., & Almaayah, M. (2025). Unsupervised text feature selection approach based on improved Prairie dog algorithm for the text clustering. Jordanian Journal of Informatics and Computing, 2025(1), 27–36. https://doi.org/10.63180/jjic.thestap.2025.1.4
An, J., & Cho, S. (2015). Variational autoencoder based anomaly detection using reconstruction probability. Special Lecture on IE, 2(1), 1–18.
Artail, H., Safa, H., Sraj, M., Kuwatly, I., & Al-Masri, Z. (2006). A hybrid honeypot framework for improving intrusion detection systems in protecting organizational networks. Computers & Security, 25(4), 274–288.
Asassfeh, M., Samara, G., Zaid, A. A., Laila, D. A., Al-Anzi, S., & Alqammaz, A. (2024). Penetration testing overview—Opportunities and ethical considerations: Literature notes. In 2024 International Jordanian Cybersecurity Conference (IJCC) (pp. 131–135). IEEE.
Atwell, K., Brennan, C., Cinicola, G., Danner, G., Pusey, M., & West, A. (2019). DeepFusion: A deep learning framework for information fusion. In Proceedings of the International Conference on Information Fusion (pp. 1–8). IEEE.
Axelsson, S. (2000). Aspects of the modelling and performance of intrusion detection [Technical report]. Department of Computer Engineering, Chalmers University of Technology.
Bailey, M., Cooke, E., Jahanian, F., Xu, Y., & Karir, M. (2018). A survey of botnet technology and defenses. In Proceedings of the Cybersecurity Applications & Technology Conference for Homeland Security (pp. 299–304). IEEE.
Biggio, B., & Roli, F. (2014). Wild patterns: Ten years after the rise of adversarial machine learning. Pattern Recognition, 84, 317–331.
Cao, V. L., Nicolau, M., & McDermott, J. (2019). One-class classification for anomaly detection with kernel density estimation and genetic programming. In European Conference on Genetic Programming (pp. 3–18). Springer.
Chandran, A. L., Samual, J., Safavi, S., & Ali, A. (2025). A comparative analysis of machine learning models on detecting malware in Android devices. Journal of Cyber Security and Risk Auditing, 2025(4), 327–346. https://doi.org/10.63180/jcsra.thestap.2025.4.10
Chen, L., Sultana, S., & Sahita, R. (2021). HetGNN: Heterogeneous graph neural network for malware detection. In AAAI Conference on Artificial Intelligence (Vol. 35, pp. 14834–14842). AAAI Press.
Corona, I., Giacinto, G., & Roli, F. (2013). Adversarial attacks against intrusion detection systems: Taxonomy, solutions and open issues. Information Sciences, 239, 201–225.
Debar, H., & Wespi, A. (2001). Aggregation and correlation of intrusion-detection alerts. In International Workshop on Recent Advances in Intrusion Detection (pp. 85–103). Springer.
Ghaffarian, S. M., & Shahriari, H. R. (2017). Software vulnerability analysis and discovery using machine-learning and data-mining techniques: A survey. ACM Computing Surveys, 50(4), Article 56.
Hawkins, S., He, H., Williams, G., & Baxter, R. (2002). Outlier detection using replicator neural networks. In International Conference on Data Warehousing and Knowledge Discovery (pp. 170–180). Springer.
Khalaf, Y., Aljaidi, M., Laila, D. A., Alsarhan, A., Alkhawaldeh, A. K., Alsuwaylimi, A. A., & Kharabsheh, M. (2025). An Effective Encryption Algorithm Based on RSA and DES. International Journal of Communication Networks and Information Security, 17(4), 10-19.
Kim, J., Kim, J., Thu, H. L. T., & Kim, H. (2016). Long short term memory recurrent neural network classifier for intrusion detection. In Proceedings of the International Conference on Platform Technology and Service (pp. 1–5). IEEE.
Kumar, S., Zhang, X., & Leskovec, J. (2019). Predicting dynamic embedding trajectory in temporal interaction networks. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 1269–1278). ACM.
Laila, D. A., Aljaidi, M., Almaiah, M. A., AlBourini, M., AlNa'amneh, Q., & Samara, G. (2025). A novel scheme to optimize LSB steganography based on a logistic chaotic map and genetic algorithm. Iraqi Journal for Computer Science and Mathematics, 6(2).
Laila, D. A., Al-Na'amneh, Q., Aljaidi, M., Nasayreh, A. N., & Gharaibeh, H. (2024a). Enhancing 2D logistic chaotic map for gray image encryption (pp. 170–188). [Publisher information not provided].
Laila, D. A., Al-Na'amneh, Q., Aljaidi, M., Nasayreh, A. N., & Gharaibeh, H. (2024b). Simulation of routing protocols for jamming attacks in mobile ad-hoc network (pp. 235–252). [Publisher information not provided].
Lee, J., Park, J., Kim, S., Kim, D., Kim, B., & Oh, H. (2021). PacketFormer: Packet-level transformer for network intrusion detection. In International Conference on Information Networking (ICOIN) (pp. 177–182). IEEE.
Li, D., Chen, D., Jin, B., Shi, L., Goh, J., & Ng, S. K. (2020). MAD-GAN: Multivariate anomaly detection for time series data with generative adversarial networks. In International Conference on Artificial Neural Networks (pp. 703–716). Springer.
Lian, W., Xue, T., Lu, Y., Wang, M., & Deng, W. (2019). Research on hierarchical data fusion of intelligent medical monitoring. IEEE Access, 8, 38355–38367.
Liu, F., Wen, S., Zhang, X., Yang, X., Zhou, P., & Nepal, S. (2020). Log2vec: A heterogeneous graph embedding based approach for detecting cyber threats within enterprise. In ACM SIGSAC Conference on Computer and Communications Security (pp. 1777–1794). ACM.
Liu, F., Wen, Y., Zhang, D., Jiang, X., Xing, X., & Meng, D. (2019). Log2vec: A heterogeneous graph embedding based approach for detecting cyber threats within enterprise. In Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security (pp. 1777–1794). ACM.
Liu, S., Liu, S., Cai, W., Pujol, S., Kikker, R., & Che, X. (2021). Multimodal deep learning for robust RGB-D object recognition. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 1488–1493). IEEE.
Lopez-Martin, M., Carro, B., Sanchez-Esguevillas, A., & Lloret, J. (2017). Conditional variational autoencoder for prediction and feature recovery applied to intrusion detection in IoT. Sensors, 17(9), Article 1967.
Maayah, M. (2025). Framework for node detection in cloud computing: A multi-metric approach integrating security, availability, and latency factors. Journal of Cyber Security and Risk Auditing, 2025(4), 238–256. https://doi.org/10.63180/jcsra.thestap.2025.4.4
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., & Vladu, A. (2017). Towards deep learning models resistant to adversarial attacks. arXiv. https://arxiv.org/abs/1706.06083
Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I., & Frey, B. (2015). Adversarial autoencoders. arXiv. https://arxiv.org/abs/1511.05044
Mhammed, K. (2025). Cyber intelligence & Moroccan national security: What strategy for managing and mitigating cyber threats against future sporting events. Journal of Cyber Security and Risk Auditing, 2025(4), 366–383. https://doi.org/10.63180/jcsra.thestap.2025.4.12
Mughaid, A., Obaidat, I., Aljammal, A., AlZu'bi, S., Quiam, F., & Laila, D. A. (2023). Simulation and analysis performance of ad-hoc routing protocols under DDoS attack and proposed solution. International Journal of Data & Network Science, 7(2), [page numbers not provided].
Nascita, A., Montieri, A., Aceto, G., Ciuonzo, D., Persico, V., & Pescape, A. (2021). XAI meets mobile traffic classification: Understanding and improving multimodal deep learning architectures. IEEE Transactions on Network and Service Management, 18(4), 4225–4246.
Pouget, F., & Dacier, M. (2004). Honeypot-based forensics. In AusCERT Asia Pacific Information Technology Security Conference. AusCERT.
Rossi, E., Chamberlain, B., Frasca, F., Eynard, D., Monti, F., & Bronstein, M. (2020). Temporal graph networks for deep learning on dynamic graphs. arXiv. https://arxiv.org/abs/2006.10637
Torres, P., Catania, C., Garcia, S., & Garino, C. G. (2022). An analysis of recurrent neural networks for botnet detection behavior. In IEEE Biennial Congress of Argentina (ARGENCON) (pp. 1–8). IEEE.
Valeur, F., Vigna, G., Kruegel, C., & Kemmerer, R. A. (2004). Comprehensive approach to intrusion detection alert correlation. IEEE Transactions on Dependable and Secure Computing, 1(3), 146–169.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. In Advances in Neural Information Processing Systems (Vol. 30). Curran Associates.
Wang, K., Du, M., Yang, D., Zhu, C., Shen, J., & Zhang, Y. (2020). Game theory-based active defense for intrusion detection in cyber-physical systems. Computer Communications, 150, 42–51.
Wang, M., Zheng, K., Yang, Y., & Wang, X. (2021). An explainable machine learning framework for intrusion detection systems. IEEE Access, 8, 73127–73141.
Wang, W., Zhu, M., Wang, J., Zeng, X., & Yang, Z. (2017). End-to-end encrypted traffic classification with one-dimensional convolution neural networks. In 2017 IEEE International Conference on Intelligence and Security Informatics (ISI) (pp. 43–48). IEEE.
Yin, C., Zhu, Y., Fei, J., & He, X. (2017). A deep learning approach for intrusion detection using recurrent neural networks. IEEE Access, 5, 21954–21961.
Zhang, H., Chen, J., & Xu, G. (2021). Ensemble adversarial training for robust deep learning models. Pattern Recognition, 118, Article 108030.
Zhang, H., Yu, L., Wang, J., & Yang, W. (2022). Multiscale transformer for network intrusion detection. In IEEE International Conference on Communications (ICC) (pp. 1–6). IEEE.
Zhou, C., & Paffenroth, R. C. (2019). Anomaly detection with robust deep autoencoders. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 665–674). ACM.
Zhou, Y., Cheng, G., Jiang, S., & Dai, M. (2020). Building an efficient intrusion detection system based on feature selection and ensemble classifier. Computer Networks, 174, Article 107247.
Zimmermann, C. (2018). A categorization of real-time multimodal interaction errors and their recovery implications. In Proceedings of the 20th ACM International Conference on Multimodal Interaction (pp. 73–82). ACM.
Abu Laila, D. (2025). Responsive Machine Learning Framework and Lightweight Utensil of Prevention of Evasion Attacks in the IoT-Based IDS. STAP Journal of Security Risk Management, 2025(1), 59–70. https://doi.org/10.63180/jsrm.thestap.2025.1.3
Aljumaiah, O., Jiang, W., Reddy Addula, S., & Amin Almaiah, M. (2025). Analyzing cybersecurity risks and threats in IT infrastructure based on NIST framework. Journal of Cyber Security and Risk Auditing, 2025(2), 12–26. https://doi.org/10.63180/jcsra.thestap.2025.2.2
Almaayah, M., & Sulaiman, R. B. (2024). Cyber risk management in the Internet of Things: Frameworks, models, and best practices. STAP Journal of Security Risk Management, 2024(1), 3–23. https://doi.org/10.63180/jsrm.thestap.2024.1.1
Almaiah, M. A., & Kadel, R. (2025). Leveraging ACO, GA, and GWO for enhancing port scan attack detection using machine learning. Journal of Cyber Security and Risk Auditing, 2025(4), 306–326. https://doi.org/10.63180/jcsra.thestap.2025.4.9
Almarshood, R., & Rahman, M. M. H. (2025). Enhancing Intrusion Detection Systems by Using Machine Learning in Smart Cities: Issues, Challenges and Future Research Direction. STAP Journal of Security Risk Management, 2025(1), 3–21. https://doi.org/10.63180/jsrm.thestap.2025.1.1
Al-olaqi, M., Al-gailani, A., & Rahman, M. M. H. (2025). Comprehensive study of SQL injection attacks mitigation methods and future directions. Journal of Cyber Security and Risk Auditing, 2025(4), 347–365. https://doi.org/10.63180/jcsra.thestap.2025.4.11
Alsarhan, A., Al-Aiash, I., Al-Fraihat, D., Aljaidi, M., & Laila, D. A. A. H. A. (2024). Expert phishing detection system. In 2024 IEEE International Conference on Industry 4.0, Artificial Intelligence, IEEE.
Alshinwan, M., Memon, A. G., Ghanem, M. C., & Almaayah, M. (2025). Unsupervised text feature selection approach based on improved Prairie dog algorithm for the text clustering. Jordanian Journal of Informatics and Computing, 2025(1), 27–36. https://doi.org/10.63180/jjic.thestap.2025.1.4
An, J., & Cho, S. (2015). Variational autoencoder based anomaly detection using reconstruction probability. Special Lecture on IE, 2(1), 1–18.
Artail, H., Safa, H., Sraj, M., Kuwatly, I., & Al-Masri, Z. (2006). A hybrid honeypot framework for improving intrusion detection systems in protecting organizational networks. Computers & Security, 25(4), 274–288.
Asassfeh, M., Samara, G., Zaid, A. A., Laila, D. A., Al-Anzi, S., & Alqammaz, A. (2024). Penetration testing overview—Opportunities and ethical considerations: Literature notes. In 2024 International Jordanian Cybersecurity Conference (IJCC) (pp. 131–135). IEEE.
Atwell, K., Brennan, C., Cinicola, G., Danner, G., Pusey, M., & West, A. (2019). DeepFusion: A deep learning framework for information fusion. In Proceedings of the International Conference on Information Fusion (pp. 1–8). IEEE.
Axelsson, S. (2000). Aspects of the modelling and performance of intrusion detection [Technical report]. Department of Computer Engineering, Chalmers University of Technology.
Bailey, M., Cooke, E., Jahanian, F., Xu, Y., & Karir, M. (2018). A survey of botnet technology and defenses. In Proceedings of the Cybersecurity Applications & Technology Conference for Homeland Security (pp. 299–304). IEEE.
Biggio, B., & Roli, F. (2014). Wild patterns: Ten years after the rise of adversarial machine learning. Pattern Recognition, 84, 317–331.
Cao, V. L., Nicolau, M., & McDermott, J. (2019). One-class classification for anomaly detection with kernel density estimation and genetic programming. In European Conference on Genetic Programming (pp. 3–18). Springer.
Chandran, A. L., Samual, J., Safavi, S., & Ali, A. (2025). A comparative analysis of machine learning models on detecting malware in Android devices. Journal of Cyber Security and Risk Auditing, 2025(4), 327–346. https://doi.org/10.63180/jcsra.thestap.2025.4.10
Chen, L., Sultana, S., & Sahita, R. (2021). HetGNN: Heterogeneous graph neural network for malware detection. In AAAI Conference on Artificial Intelligence (Vol. 35, pp. 14834–14842). AAAI Press.
Corona, I., Giacinto, G., & Roli, F. (2013). Adversarial attacks against intrusion detection systems: Taxonomy, solutions and open issues. Information Sciences, 239, 201–225.
Debar, H., & Wespi, A. (2001). Aggregation and correlation of intrusion-detection alerts. In International Workshop on Recent Advances in Intrusion Detection (pp. 85–103). Springer.
Ghaffarian, S. M., & Shahriari, H. R. (2017). Software vulnerability analysis and discovery using machine-learning and data-mining techniques: A survey. ACM Computing Surveys, 50(4), Article 56.
Hawkins, S., He, H., Williams, G., & Baxter, R. (2002). Outlier detection using replicator neural networks. In International Conference on Data Warehousing and Knowledge Discovery (pp. 170–180). Springer.
Khalaf, Y., Aljaidi, M., Laila, D. A., Alsarhan, A., Alkhawaldeh, A. K., Alsuwaylimi, A. A., & Kharabsheh, M. (2025). An Effective Encryption Algorithm Based on RSA and DES. International Journal of Communication Networks and Information Security, 17(4), 10-19.
Kim, J., Kim, J., Thu, H. L. T., & Kim, H. (2016). Long short term memory recurrent neural network classifier for intrusion detection. In Proceedings of the International Conference on Platform Technology and Service (pp. 1–5). IEEE.
Kumar, S., Zhang, X., & Leskovec, J. (2019). Predicting dynamic embedding trajectory in temporal interaction networks. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 1269–1278). ACM.
Laila, D. A., Aljaidi, M., Almaiah, M. A., AlBourini, M., AlNa'amneh, Q., & Samara, G. (2025). A novel scheme to optimize LSB steganography based on a logistic chaotic map and genetic algorithm. Iraqi Journal for Computer Science and Mathematics, 6(2).
Laila, D. A., Al-Na'amneh, Q., Aljaidi, M., Nasayreh, A. N., & Gharaibeh, H. (2024a). Enhancing 2D logistic chaotic map for gray image encryption (pp. 170–188). [Publisher information not provided].
Laila, D. A., Al-Na'amneh, Q., Aljaidi, M., Nasayreh, A. N., & Gharaibeh, H. (2024b). Simulation of routing protocols for jamming attacks in mobile ad-hoc network (pp. 235–252). [Publisher information not provided].
Lee, J., Park, J., Kim, S., Kim, D., Kim, B., & Oh, H. (2021). PacketFormer: Packet-level transformer for network intrusion detection. In International Conference on Information Networking (ICOIN) (pp. 177–182). IEEE.
Li, D., Chen, D., Jin, B., Shi, L., Goh, J., & Ng, S. K. (2020). MAD-GAN: Multivariate anomaly detection for time series data with generative adversarial networks. In International Conference on Artificial Neural Networks (pp. 703–716). Springer.
Lian, W., Xue, T., Lu, Y., Wang, M., & Deng, W. (2019). Research on hierarchical data fusion of intelligent medical monitoring. IEEE Access, 8, 38355–38367.
Liu, F., Wen, S., Zhang, X., Yang, X., Zhou, P., & Nepal, S. (2020). Log2vec: A heterogeneous graph embedding based approach for detecting cyber threats within enterprise. In ACM SIGSAC Conference on Computer and Communications Security (pp. 1777–1794). ACM.
Liu, F., Wen, Y., Zhang, D., Jiang, X., Xing, X., & Meng, D. (2019). Log2vec: A heterogeneous graph embedding based approach for detecting cyber threats within enterprise. In Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security (pp. 1777–1794). ACM.
Liu, S., Liu, S., Cai, W., Pujol, S., Kikker, R., & Che, X. (2021). Multimodal deep learning for robust RGB-D object recognition. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 1488–1493). IEEE.
Lopez-Martin, M., Carro, B., Sanchez-Esguevillas, A., & Lloret, J. (2017). Conditional variational autoencoder for prediction and feature recovery applied to intrusion detection in IoT. Sensors, 17(9), Article 1967.
Maayah, M. (2025). Framework for node detection in cloud computing: A multi-metric approach integrating security, availability, and latency factors. Journal of Cyber Security and Risk Auditing, 2025(4), 238–256. https://doi.org/10.63180/jcsra.thestap.2025.4.4
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., & Vladu, A. (2017). Towards deep learning models resistant to adversarial attacks. arXiv. https://arxiv.org/abs/1706.06083
Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I., & Frey, B. (2015). Adversarial autoencoders. arXiv. https://arxiv.org/abs/1511.05044
Mhammed, K. (2025). Cyber intelligence & Moroccan national security: What strategy for managing and mitigating cyber threats against future sporting events. Journal of Cyber Security and Risk Auditing, 2025(4), 366–383. https://doi.org/10.63180/jcsra.thestap.2025.4.12
Mughaid, A., Obaidat, I., Aljammal, A., AlZu'bi, S., Quiam, F., & Laila, D. A. (2023). Simulation and analysis performance of ad-hoc routing protocols under DDoS attack and proposed solution. International Journal of Data & Network Science, 7(2), [page numbers not provided].
Nascita, A., Montieri, A., Aceto, G., Ciuonzo, D., Persico, V., & Pescape, A. (2021). XAI meets mobile traffic classification: Understanding and improving multimodal deep learning architectures. IEEE Transactions on Network and Service Management, 18(4), 4225–4246.
Pouget, F., & Dacier, M. (2004). Honeypot-based forensics. In AusCERT Asia Pacific Information Technology Security Conference. AusCERT.
Rossi, E., Chamberlain, B., Frasca, F., Eynard, D., Monti, F., & Bronstein, M. (2020). Temporal graph networks for deep learning on dynamic graphs. arXiv. https://arxiv.org/abs/2006.10637
Torres, P., Catania, C., Garcia, S., & Garino, C. G. (2022). An analysis of recurrent neural networks for botnet detection behavior. In IEEE Biennial Congress of Argentina (ARGENCON) (pp. 1–8). IEEE.
Valeur, F., Vigna, G., Kruegel, C., & Kemmerer, R. A. (2004). Comprehensive approach to intrusion detection alert correlation. IEEE Transactions on Dependable and Secure Computing, 1(3), 146–169.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. In Advances in Neural Information Processing Systems (Vol. 30). Curran Associates.
Wang, K., Du, M., Yang, D., Zhu, C., Shen, J., & Zhang, Y. (2020). Game theory-based active defense for intrusion detection in cyber-physical systems. Computer Communications, 150, 42–51.
Wang, M., Zheng, K., Yang, Y., & Wang, X. (2021). An explainable machine learning framework for intrusion detection systems. IEEE Access, 8, 73127–73141.
Wang, W., Zhu, M., Wang, J., Zeng, X., & Yang, Z. (2017). End-to-end encrypted traffic classification with one-dimensional convolution neural networks. In 2017 IEEE International Conference on Intelligence and Security Informatics (ISI) (pp. 43–48). IEEE.
Yin, C., Zhu, Y., Fei, J., & He, X. (2017). A deep learning approach for intrusion detection using recurrent neural networks. IEEE Access, 5, 21954–21961.
Zhang, H., Chen, J., & Xu, G. (2021). Ensemble adversarial training for robust deep learning models. Pattern Recognition, 118, Article 108030.
Zhang, H., Yu, L., Wang, J., & Yang, W. (2022). Multiscale transformer for network intrusion detection. In IEEE International Conference on Communications (ICC) (pp. 1–6). IEEE.
Zhou, C., & Paffenroth, R. C. (2019). Anomaly detection with robust deep autoencoders. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 665–674). ACM.
Zhou, Y., Cheng, G., Jiang, S., & Dai, M. (2020). Building an efficient intrusion detection system based on feature selection and ensemble classifier. Computer Networks, 174, Article 107247.
Zimmermann, C. (2018). A categorization of real-time multimodal interaction errors and their recovery implications. In Proceedings of the 20th ACM International Conference on Multimodal Interaction (pp. 73–82). ACM.