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<h1><a href="index.html">Kleanthis Malialis</a></h1>
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<h2>Publications</h2>
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<section class="box">
<h3>Journal articles</h3>
<p><ol>
<li>P. Valianti, <strong>K. Malialis</strong>, P. Kolios, G. Ellinas. Cooperative multi-agent jamming of multiple rogue drones using reinforcement learning. IEEE Transactions on Mobile Computing, 2024. (To appear)</li>
<li>P. V. Pavlou, S. Filippou, S. Solonos, S. G. Vrachimis, <strong>K. Malialis</strong>, D. G. Eliades, T. Theocarides, M. M. Polycarpou. Monitoring domestic water consumption: a comparative study of model-based and data-driven end-use disaggregation methods. Journal of Hydroinformatics, 2024. <a href="https://iwaponline.com/jh/article/doi/10.2166/hydro.2024.120/101077">[pdf]</a></li>
<li><strong>K. Malialis</strong>, C. G. Panayiotou, M. M. Polycarpou. Nonstationary data stream classification with online active learning and siamese neural networks, Neurocomputing, Volume 512, Nov. 2022, Pages 235-252. <a href="https://arxiv.org/abs/2210.01090">[pdf]</a> <a href="https://github.com/kmalialis/actisiamese">[code]</a></li>
<li><strong>K. Malialis</strong>, C. G. Panayiotou and M. M. Polycarpou. Online Learning With Adaptive Rebalancing in Nonstationary Environments, in IEEE Transactions on Neural Networks and Learning Systems, 2020. <a href="https://ieeexplore.ieee.org/document/9203853">[pdf]</a> <a href="https://github.com/kmalialis/areba/">[code]</a></li>
<li><strong>K. Malialis</strong>, S. Devlin and D. Kudenko. Distributed Reinforcement Learning for Adaptive and Robust Network Intrusion Response. In Connection Science, Volume 27, Issue 3, July 2015, Pages 234-252. <a href="https://www.tandfonline.com/doi/full/10.1080/09540091.2015.1031082">[pdf]</a></li>
<li><strong>K. Malialis</strong>, D. Kudenko. Distributed Response to Network Intrusions Using Multiagent Reinforcement Learning. In Engineering Applications of Artificial Intelligence, Volume 41, May 2015, Pages 270-284. <a href="https://www.sciencedirect.com/science/article/abs/pii/S095219761500024X">[pdf]</a> (<span style="color:red">Department's Best Student Paper 2015 Award</span>)</li>
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<section class="box">
<h3>Refereed conference papers</h3>
<p><ol>
<li>P. Valianti, <strong>K. Malialis</strong>, P. Kolios, G. Ellinas. Cooperative Search and Track of Rogue Drones using Multiagent Reinforcement Learning. In IEEE International Conference on Systems, Man, and Cybernetics, 2024. (To appear)</li>
<li>V. Vaquet, J. Vaquet, F. Hinder, <strong>K. Malialis</strong>, C. Panayiotou, M. Polycarpou, B. Hammer. Self-supervised learning from gradually drifting data streams. In In European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), 2024. (To appear)</li>
<li><strong>K. Malialis</strong>, J. Li, C. G. Panayiotou, M. M. Polycarpou. Incremental learning with concept drift detection and prototype-based embeddings for graph stream classification. In IEEE International Joint Conference on Neural Networks (IJCNN), 2024. (To appear) <a href="https://arxiv.org/abs/2404.02572">[pdf]</a></li>
<li>J. Li, <strong>K. Malialis</strong>, C. G. Panayiotou, M. M. Polycarpou. Unsupervised incremental learning with dual concept drift detection for identifying anomalous sequences. In IEEE International Joint Conference on Neural Networks (IJCNN), 2024. (To appear) <a href="https://arxiv.org/abs/2403.03576">[pdf]</a></li>
<li>M. Karapitta, A. Kasis, C. Stylianides, <strong>K. Malialis</strong>, P. Kolios. Time-varying compartmental models with neural networks for pandemic infection forecasting. In IEEE Engineering in Medicine and Biology Society (EMBC), 2024. (To appear)</li>
<li>J. Li, <strong>K. Malialis</strong>, M. M. Polycarpou. Autoencoder-based anomaly detection in streaming data with incremental learning and concept drift adaptation. In IEEE International Joint Conference on Neural Networks (IJCNN), 2023. <a href="https://arxiv.org/abs/2305.08977">[pdf]</a></li>
<li>A. Artelt, <strong>K. Malialis</strong>, C. G. Panayiotou, M. M. Polycarpou, B. Hammer. Unsupervised unlearning of concept drift with autoencoders. In IEEE Symposium Series on Computational Intelligence, 2023. <a href="https://arxiv.org/abs/2211.12989">[pdf]</a></li>
<li>C. Stylianides, <strong>K. Malialis</strong>, P. Kolios. A study of data-driven methods for adaptive forecasting of
COVID-19 cases. In International Conference on Artificial Neural Networks (ICANN), 2023. <a href="https://arxiv.org/abs/2309.09698">[pdf]</a></li>
<li>S. Filippou, A. Achilleos, S. Z. Zukhraf, C. Laoudias, <strong>K. Malialis</strong>, M. K. Michael, G. Ellinas. A
machine learning approach for detecting GPS location spoofing attacks in autonomous vehicles. In
IEEE Vehicular Technology Conference (VTC), 2023. <a href="https://zenodo.org/records/8278591">[pdf]</a></li>
<li>S. Filippou, <strong>K. Malialis</strong>, C. G. Panayiotou. Improving customer experience in call centers with
intelligent customer-agent pairing. In International Conference on Artificial Intelligence Applications
and Innovations (AIAI), 2023. <a href="https://arxiv.org/abs/2305.08594">[pdf]</a></li>
<li>P. Valianti, <strong>K. Malialis</strong>, P. Kolios, G. Ellinas. Multi-agent reinforcement learning for multiple drone
interception. In International Conference on Unmanned Aerial Systems (ICUAS), 2023.</li>
<li><strong>K. Malialis</strong>, M. Roveri, C. Alippi, C. G. Panayiotou, M. M. Polycarpou, "A hybrid active-passive approach to imbalanced nonstationary data stream classification." In IEEE Symposium Series on Computational Intelligence (SSCI), 2022. <a href="https://arxiv.org/abs/2210.04949">[pdf]</a></li>
<li><strong>K. Malialis</strong>, D. Papatheodoulou, S. Filippou, C. G. Panayiotou, M. M. Polycarpou, "Data augmentation on-the-fly and active learning in data stream classification." In IEEE Symposium Series on Computational Intelligence (SSCI), 2022. <a href="https://arxiv.org/abs/2210.06873">[pdf]</a> <a href="https://github.com/kmalialis/augmented_queues/">[code]</a></li>
<li>D. Papatheodoulou, P. Pavlou, S. G. Vrachimis, <strong>K. Malialis</strong>, D. G. Eliades, Theocharides, T. (2022). A Multi-label Time Series Classification Approach for Non-intrusive Water End-Use Monitoring. In: Maglogiannis, I., Iliadis, L., Macintyre, J., Cortez, P. (eds) Artificial Intelligence Applications and Innovations. AIAI 2022. IFIP Advances in Information and Communication Technology, vol 647. Springer, Cham. <a href="https://arxiv.org/abs/2210.00089">[pdf]</a></li>
<li><strong>K. Malialis</strong>, C. G. Panayiotou and M. M. Polycarpou. Data-efficient online classification with Siamese networks and active learning. In IEEE International Joint Conference on Neural Networks (IJCNN), 2020. <a href="https://arxiv.org/abs/2010.01659">[pdf]</a>
<li><strong>K. Malialis</strong>, C. Panayiotou and M. M. Polycarpou. Queue-based resampling for online class imbalance learning. In Proceedings of the 27th International Conference on Artificial Neural Networks (ICANN), 2018. <a href="https://arxiv.org/abs/1809.10388">[pdf]</a> <a href="https://github.com/kmalialis/queue_based_resampling">[code]</a></li>
<li>H. Cai, K. Ren, W. Zhang, <strong>K. Malialis</strong>, J. Wang, Y. Yu and D. Guo. Real-Time Bidding with Reinforcement Learning in Display Advertising. In Proceedings of the 10th ACM International Conference on Web Search and Data Mining (WSDM), 2017. <a href="https://arxiv.org/abs/1701.02490">[pdf]</a> (Acceptance rate 15.8%)</li>
<li><strong>K. Malialis</strong>, S. Devlin and D. Kudenko. Resource Abstraction for Reinforcement Learning in Multiagent Congestion Problems. In Proceedings of the 15th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2016. <a href="https://arxiv.org/abs/1903.05431">[pdf]</a> <a href="https://github.com/kmalialis/resource_abstraction/">[code]</a> (Acceptance rate 24.9%)</li>
<li><strong>K. Malialis</strong>, S. Devlin and D. Kudenko. Coordinated Team Learning and Difference Rewards for Distributed Intrusion Response. In Proceedings of the 21st European Conference on Artificial Intelligence (ECAI), 2014. <a href="https://ebooks.iospress.nl/doi/10.3233/978-1-61499-419-0-1063">[pdf]</a></li>
<li><strong>K. Malialis</strong> and D. Kudenko. Multiagent Router Throttling: Decentralized Coordinated Response against DDoS Attacks. In Proceedings of the 25th Conference on Innovative Applications of Artificial Intelligence (AAAI / IAAI), 2013. <a href="https://cdn.aaai.org/ojs/19000/19000-13-22812-1-10-20211006.pdf">[pdf]</a></li>
</ol></p>
</section>
<section class="box">
<h3>Refereed workshop papers</h3>
<p><ol>
<li><strong>K. Malialis</strong>, J. Wang, G. Brooks, G. Frangou. Feature Selection as a Multiagent Coordination Problem. In AAMAS Workshop on Adaptive and Learning Agents (ALA), 2016. <a href="https://arxiv.org/abs/1603.05152">[pdf]</a></li>
<li><strong>K. Malialis</strong>, S. Devlin and D. Kudenko. Intrusion Response Using Difference Rewards for Scalability and Online Learning. In AAMAS Workshop on Adaptive and Learning Agents (ALA), 2014.</li>
<li><strong>K. Malialis</strong> and D. Kudenko. Large-Scale DDoS Response Using Cooperative Reinforcement Learning. In 11th European Workshop on Multi-Agent Systems (EUMAS), 2013. <a href="https://www.irit.fr/EUMAS2013/Papers/eumas2013_submission_21.pdf">[pdf]</a></li>
<li><strong>K. Malialis</strong> and D. Kudenko. Reinforcement Learning of Throttling for DDoS Attack Response. In AAMAS Workshop on Adaptive and Learning Agents (ALA), 2012.</li>
</ol></p>
</section>
<section class="box">
<h3>Theses</h3>
<p><ol>
<li><strong>K. Malialis</strong>. Distributed Reinforcement Learning for Network Intrusion Response. PhD thesis, Department of Computer Science, University of York, UK, 2014. <a href="http://etheses.whiterose.ac.uk/8109/">[pdf]</a></li>
<li><strong>K. Malialis</strong>. Genetic Algorithms Using Lamarckian Evolution. MEng dissertation, Department of Computer Science, University of York, UK, 2010.</li>
</ol></p>
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