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<citation_list><citation key="ref0"><journal_title>IEEE Communications surveys &amp; tutorials</journal_title><author>Buczak</author><volume>18</volume><issue>2</issue><first_page>1153</first_page><cYear>2015</cYear><doi>10.1109/COMST.2015.2494502</doi><article_title>A survey of data mining and machine learning methods for cyber security intrusion detection</article_title><unstructured_citation>Buczak, A. L., &amp; Guven, E. (2015). A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Communications surveys &amp; tutorials, 18(2), 1153-1176.</unstructured_citation></citation><citation key="ref1"><journal_title>computers &amp; security 28(1-2)</journal_title><author>Garcia-Teodoro</author><first_page>18</first_page><cYear>2009</cYear><doi>10.1016/j.cose.2008.08.003</doi><article_title>Anomaly-based network intrusion detection: Techniques, systems and challenges</article_title><unstructured_citation>Garcia-Teodoro, P., Diaz-Verdejo, J., Maciá-Fernández, G., &amp; Vázquez, E. (2009). Anomaly-based network intrusion detection: Techniques, systems and challenges. computers &amp; security, 28(1-2), 18-28.</unstructured_citation></citation><citation key="ref2"><journal_title>International Journal of Machine Learning and Cybernetics</journal_title><author>Torres</author><volume>10</volume><issue>10</issue><first_page>2823</first_page><cYear>2019</cYear><doi>10.1007/s13042-018-00906-1</doi><article_title>Machine learning techniques applied to cybersecurity</article_title><unstructured_citation>Torres, J. M., Comesaña, C. I., &amp; Garcia-Nieto, P. J. (2019). Machine learning techniques applied to cybersecurity. International Journal of Machine Learning and Cybernetics, 10(10), 2823-2836.</unstructured_citation></citation><citation key="ref3"><journal_title>Ieee access</journal_title><author>Xin</author><volume>6</volume><first_page>35365</first_page><cYear>2018</cYear><doi>10.1109/ACCESS.2018.2836950</doi><article_title>Machine learning and deep learning methods for cybersecurity</article_title><unstructured_citation>Xin, Y., Kong, L., Liu, Z., Chen, Y., Li, Y., Zhu, H., ... &amp; Wang, C. (2018). Machine learning and deep learning methods for cybersecurity. Ieee access, 6, 35365-35381.</unstructured_citation></citation><citation key="ref4"><journal_title>IEEE/ACM Transactions on Audio Speech and Language Processing</journal_title><author>Sarikaya</author><volume>22</volume><issue>4</issue><first_page>778</first_page><cYear>2014</cYear><doi>10.1109/TASLP.2014.2303296</doi><article_title>Application of deep belief networks for natural language understanding</article_title><unstructured_citation>Sarikaya, R., Hinton, G. E., &amp; Deoras, A. (2014). Application of deep belief networks for natural language understanding. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 22(4), 778-784.</unstructured_citation></citation><citation key="ref5"><journal_title>Sensors</journal_title><author>Larriva-Novo</author><volume>21</volume><issue>2</issue><first_page>656</first_page><cYear>2021</cYear><doi>10.3390/s21020656</doi><article_title>An IoT-Focused Intrusion Detection System Approach Based on Preprocessing Characterization for Cybersecurity Datasets</article_title><unstructured_citation>Larriva-Novo, X., Villagrá, V. A., Vega-Barbas, M., Rivera, D., &amp; Sanz Rodrigo, M. (2021). An IoT-Focused Intrusion Detection System Approach Based on Preprocessing Characterization for Cybersecurity Datasets. Sensors, 21(2), 656.</unstructured_citation></citation><citation key="ref6"><doi>10.1016/j.chaos.2021.111143</doi><unstructured_citation>Pérez, S. I., Moral-Rubio, S., &amp; Criado, R. (2021). A new approach to combine multiplex networks and time series attributes: Building intrusion detection systems (IDS) in cybersecurity. Chaos, Solitons &amp; Fractals, 150, 111143.</unstructured_citation></citation><citation key="ref7"><journal_title>Symmetry</journal_title><author>Ustun</author><volume>13</volume><issue>5</issue><first_page>826</first_page><cYear>2021</cYear><doi>10.3390/sym13050826</doi><article_title>Machine Learning-Based Intrusion Detection for Achieving Cybersecurity in Smart Grids Using IEC 61850 GOOSE Messages</article_title><unstructured_citation>Ustun, T. S., Hussain, S. M., Ulutas, A., Onen, A., Roomi, M. M., &amp; Mashima, D. (2021). Machine Learning-Based Intrusion Detection for Achieving Cybersecurity in Smart Grids Using IEC 61850 GOOSE Messages. Symmetry, 13(5), 826.</unstructured_citation></citation><citation key="ref8"><doi>10.1145/2619239.2631434</doi><unstructured_citation>Yuan, Z., Lu, Y., Wang, Z., &amp; Xue, Y. (2014, August). Droid-sec: deep learning in android malware detection. In Proceedings of the 2014 ACM conference on SIGCOMM (pp. 371-372).</unstructured_citation></citation><citation key="ref9"><journal_title>Tsinghua Science and Technology</journal_title><author>Yuan</author><volume>21</volume><issue>1</issue><first_page>114</first_page><cYear>2016</cYear><doi>10.1109/TST.2016.7399288</doi><article_title>Droiddetector: android malware characterization and detection using deep learning</article_title><unstructured_citation>Yuan, Z., Lu, Y., &amp; Xue, Y. (2016). Droiddetector: android malware characterization and detection using deep learning. Tsinghua Science and Technology, 21(1), 114-123.</unstructured_citation></citation><citation key="ref10"><doi>10.1109/ICASSP.2015.7178304</doi><unstructured_citation>Pascanu, R., Stokes, J. W., Sanossian, H., Marinescu, M., &amp; Thomas, A. (2015, April). Malware classification with recurrent networks. In 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1916-1920). IEEE.</unstructured_citation></citation><citation key="ref11"><doi>10.1007/978-3-319-50127-7_11</doi><unstructured_citation>Kolosnjaji, B., Zarras, A., Webster, G., &amp; Eckert, C. (2016, December). Deep learning for classification of malware system call sequences. In Australasian joint conference on artificial intelligence (pp. 137-149). Springer, Cham.</unstructured_citation></citation><citation key="ref12"><doi>10.1109/COMPSAC.2016.151</doi><unstructured_citation>Tobiyama, S., Yamaguchi, Y., Shimada, H., Ikuse, T., &amp; Yagi, T. (2016, June). Malware detection with deep neural network using process behavior. In 2016 IEEE 40th annual computer software and applications conference (COMPSAC) (Vol. 2, pp. 577-582). IEEE.</unstructured_citation></citation><citation key="ref13"><doi>10.1109/IJCNN.2016.7727705</doi><unstructured_citation>Ding, Y., Chen, S., &amp; Xu, J. (2016, July). Application of deep belief networks for opcode based malware detection. In 2016 International Joint Conference on Neural Networks (IJCNN) (pp. 3901-3908). IEEE.</unstructured_citation></citation><citation key="ref14"><doi>10.1145/3029806.3029823</doi><unstructured_citation>McLaughlin, N., Martinez del Rincon, J., Kang, B., Yerima, S., Miller, P., Sezer, S., ... &amp; Joon Ahn, G. (2017, March). Deep android malware detection. In Proceedings of the seventh ACM on conference on data and application security and privacy (pp. 301-308).</unstructured_citation></citation><citation key="ref15"><doi>10.1109/MALWARE.2015.7413680</doi><unstructured_citation>Saxe, J., &amp; Berlin, K. (2015, October). Deep neural network based malware detection using two dimensional binary program features. In 2015 10th International Conference on Malicious and Unwanted Software (MALWARE) (pp. 11-20). IEEE.</unstructured_citation></citation><citation key="ref16"><doi>10.1109/GLOCOM.2016.7841778</doi><unstructured_citation>Shibahara, T., Yagi, T., Akiyama, M., Chiba, D., &amp; Yada, T. (2016, December). Efficient dynamic malware analysis based on network behavior using deep learning. In 2016 IEEE Global Communications Conference (GLOBECOM) (pp. 1-7). IEEE.</unstructured_citation></citation><citation key="ref17"><journal_title>IEEE Network</journal_title><author>Chen</author><volume>33</volume><issue>4</issue><first_page>36</first_page><cYear>2019</cYear><doi>10.1109/MNET.2019.1800458</doi><article_title>Deep learning for secure mobile edge computing in cyber-physical transportation systems</article_title><unstructured_citation>Chen, Y., Zhang, Y., Maharjan, S., Alam, M., &amp; Wu, T. (2019). Deep learning for secure mobile edge computing in cyber-physical transportation systems. IEEE Network, 33(4), 36-41.</unstructured_citation></citation><citation key="ref18"><journal_title>Intelligent Data Analytics for Terror Threat Prediction</journal_title><author>Raja</author><first_page>119</first_page><cYear>2021</cYear><doi>10.1002/9781119711629.ch6</doi><article_title>Analyses on Artificial Intelligence Framework to Detect Crime Pattern</article_title><unstructured_citation>Raja, R. A., Yuvaraj, N., &amp; Kousik, N. V. (2021). Analyses on Artificial Intelligence Framework to Detect Crime Pattern. Intelligent Data Analytics for Terror Threat Prediction: Architectures, Methodologies, Techniques and Applications, 119-132.</unstructured_citation></citation><citation key="ref19"><doi>10.1016/j.compeleceng.2021.107186</doi><unstructured_citation>Chang, V., Gobinathan, B., Pinagapani, A., Kannan, S., Dhiman, G., &amp; Rajan, A. R. (2021). Automatic detection of cyberbullying using multi-feature based artificial intelligence with deep decision tree classification. Computers &amp; Electrical Engineering, 92, 107186.</unstructured_citation></citation><citation key="ref20"><unstructured_citation>Karthikeyan, T., Praghash, K., &amp; Reddy, K. H. (2021). Binary Flower Pollination (BFP) Approach to Handle the Dynamic Networking Conditions to Deliver Uninterrupted Connectivity. Wireless Personal Communications, 1-20.</unstructured_citation></citation><citation key="ref21"><doi>10.1016/j.matpr.2021.04.310</doi><unstructured_citation>Sara, S. B. V., Anand, M., Priscila, S. S., Manikandan, R., &amp; Ramkumar, M. (2021). Design of autonomous production using deep neural network for complex job. Materials Today: Proceedings.</unstructured_citation></citation><citation key="ref22"><journal_title>In Inventive Computation and Information Technologies (pp</journal_title><author>Kousik</author><cYear>2021</cYear><doi>10.1007/978-981-33-4305-4_59</doi><article_title>Improved Density-Based Learning to Cluster for User Web Log in Data Mining</article_title><unstructured_citation>Kousik, N. V., Sivaram, M., Yuvaraj, N., &amp; Mahaveerakannan, R. (2021). Improved Density-Based Learning to Cluster for User Web Log in Data Mining. In Inventive Computation and Information Technologies (pp. 813-830). Springer, Singapore.</unstructured_citation></citation></citation_list>
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