Research

  • Strategic Information Transmission
  • Strategic Adversarial Intervention Detection
  • Deception-as-Defense Framework – Dynamic Control Systems
  • Strategic Information Gathering – Autonomous Intersection Control
  • Sensor Fusion – Distributed Online Learning (and Optimization)
  • Adaptive Filtering (Prediction and Estimation) Theory

  • Strategic Information Transmission
    To induce intelligent decision makers to take certain actions, we can create incentives via external means, e.g., explicit payments, but we can also persuade them to take the actions by their own will without the need for any external means if we can craft the information available to them. In the following (selected) papers, we address this information design problem for general and Gaussian distributions:

  • M. O. Sayin and T. Başar, “Optimal Hierarchical Signaling for Quadratic Cost Measures and General Distributions: A Copositive Program Characterization,” IEEE Transactions on Automatic Control, submitted for publication, available at arXiv:1907.09070, 2019.
  • M. O. Sayin and T. Basar. Deception-As-Defense Framework for Cyber-Physical Systems. In A. Teixeira and R. Ferrari (Eds.). Safety, Security, and Privacy for Cyber-Physical Systems, Springer International Publishing, submitted for publication, available at arXiv:1902.01364.
  • M. O. Sayin, E. Akyol, and T. Başar, “Hierarchical Multi-stage Gaussian Signaling Games in Noncooperative Communication and Control Systems,” Automatica, vol. 107, pp. 9-20, 2019.
  • Go to top


    Strategic Adversarial Intervention Detection
    Information available to intelligent decision makers can be inferred to certain extent from the actions they take. Correspondingly, the decision makers (e.g., stealthy intruders) can control others’ (e.g., detectors’) perceptions gained through such inference by selecting their actions strategically. In the following (selected) papers, we address this problem under the solution concept of saddle point equilibrium:

  • M. O. Sayin, D. Sahabandu, M. A. Zaman, R. Poovendran, and T. Başar, “Actionable Game-Theoretic Adversarial Intervention Detection Against Advanced Persistent Threats,” Working Paper.
  • M. O. Sayin, C.-W. Lin, E. Kang, S. Shiraishi, and T. Başar, “Reliable Smart Road Signs,” IEEE Transactions on Intelligent Transportation Systems, submitted for publication, available at arXiv:1901.10622, 2019.
  • Go to top


    Deception-as-Defense Framework – Dynamic Control Systems
    By designing the information available to adversaries, we can induce them to take actions (or attack the system) inadvertently in-line with the systems objectives even though their malicious objective differs from it. In the following (selected) papers, we address this problem with a specific focus on dynamic control systems:

  • M. O. Sayin and T. Basar. Deception-As-Defense Framework for Cyber-Physical Systems. In A. Teixeira and R. Ferrari (Eds.). Safety, Security, and Privacy for Cyber-Physical Systems, Springer International Publishing, submitted for publication, available at arXiv:1902.01364.
  • M. O. Sayin and T. Başar, “Robust Sensor Design Against Multiple Attackers with Misaligned Control Objectives,” IEEE Transactions on Automatic Control, submitted for publication, available at arXiv:1901.10618, 2019.
  • Go to top


    Strategic Information Gathering – Autonomous Intersection Control
    Classical traffic lights, even the adaptive ones equipped with sensors, are not efficient for the quality of transportation. In that respect, communication based intersection control can be a novel alternative to the classical traffic lights. However, diversification in the drivers’ objectives and possibility of malicious (or selfish) ones result in non-cooperative multi-agent environments, where incentives play significant role in the agents’ actions. In the following (selected) papers, we address those issues by designing strategy-proof mechanisms in a game-theoretical perspective:

  • M. O. Sayin, C.-W. Lin, S. Shiraishi, J. Shen, and T. Başar, “Information-driven Autonomous Intersection Control via Incentive Compatible Mechanisms,” IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 3, pp. 912-924, 2019.
  • Go to top


    Sensor Fusion – Distributed Online Learning (and Optimization)
    A network of agents equipped with monitoring, processing, and communication modules, brings in a new dimension for information processing applications. As an example, in remote sensing applications, each sensor can monitor certain phenomena, process the measurements, and enhance the processing performance by communicating with other sensors. In energy harvesting sensor networks, distributed processing algorithms with limited computational complexity and communication load are desirable since computation and communication can consume substantial amount of power. In the following (selected) papers, we, specifically, address the issues related to the excessive communication load:

  • M. O. Sayin and S. S. Kozat, “Compressive Diffusion Strategies Over Distributed Networks for Reduced Communication Load,” IEEE Transactions on Signal Processing, vol. 62, no. 20, pp. 5308-5323, Oct. 2014.
  • M. O. Sayin, N. D. Vanli, S. S. Kozat, and T. Başar, “Stochastic Subgradient Algorithms for Strongly Convex Optimization over Distributed Networks,” IEEE Transactions on Network Science and Engineering, vol. 4, no. 4, pp. 248-260, 2017. [Runner-up of the 2019 IEEE TNSE Best Paper Award]
  • N. D. Vanli, M. O. Sayin, I. Delibalta, and S. S. Kozat, “Sequential Nonlinear Learning for Distributed Multi-Agent Systems via Extreme Learning Machines,” IEEE Transactions on Neural Networks and Learning, vol. 28, no. 3, pp. 546-558, 2017.
  • M. O. Sayin, S. S. Kozat,  and T. Başar, “Team-Optimal Distributed MMSE Estimation in General and Tree Networks,” Digital Signal Processing, vol. 64, pp. 83-95, 2017.
  • Go to top


    Adaptive Filtering (Prediction and Estimation) Theory
    Adaptive filtering (prediction and estimation) has been extensively studied in the literature and has been (and is going to be) used extensively in the industry due to its adaptability (or flexibility) to changes in the problem parameters and scalability for large-scale problems. There are various algorithms that can lead to superior/inferior performances depending on the specifics of the problem, e.g., stationary structure, computational complexity, or large-scale data. In the following (selected) papers, we, specifically, address the issues related to stability and robustness of the adaptive algorithms while seeking to achieve improved trade-off in terms of adaptability (i.e., convergence) rate and steady state performance:

  • M. O. Sayin, N. D. Vanli, and S. S. Kozat, “A Novel Family of Adaptive Filtering Algorithms Based on the Logarithmic Cost,” IEEE Transactions on Signal Processing, vol. 62, no. 17, pp. 4411-4424, Sept. 2014.
  • M. O. Sayin, Y. Yilmaz, A. Demir, and S. S. Kozat, “Krylov Proportionate Normalized Least Mean Fourth Approach: Formulation and Performance Analysis,” Signal Processing, vol. 109, pp. 1-13, Apr. 2015.
  • N. D. Vanli, K. Gokcesu, M. O. Sayin, H. Yilmaz, and S. S. Kozat, “Sequential Prediction Over Hierarchical Structures,” IEEE Transactions on Signal Processing, vol. 64, no. 23, pp.  6284-6298, 2016.
  • Go to top