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Yayın Mixture of Gaussian models and bayes error under differential privacy(2011) Xi, Bowei; Kantarcıoğlu, Murat; İnan, AliGaussian mixture models are an important tool in Bayesian decision theory. In this study, we focus on building such models over statistical database protected under differential privacy. Our approach involves querying necessary statistics from a database and building a Bayesian classifier over the noise added responses generated according to differential privacy. We formally analyze the sensitivity of our query set. Since there are multiple methods to query a statistic, either directly or indirectly, we analyze the sensitivities for different querying methods. Furthermore we establish theoretical bounds for the Bayes error for the univariate (one dimensional) case. We study the Bayes error for the multivariate (high dimensional) case in experiments with both simulated data and real life data. We discover that adding Laplace noise to a statistic under certain constraint is problematic. For example variance-covariance matrix is no longer positive definite after noise addition. We propose a heuristic method to fix the noise added variance-covariance matrix.Yayın Cost-effective fault diagnosis of a multi-component dynamic system under corrective maintenance(Elsevier Ltd, 2021-04) Özgür Ünlüakın, Demet; Türkali, Busenur; Aksezer, Sezgin ÇağlarMaintenance planning and execution are challenging tasks for every system with complex structure. Interdependent nature of the components that builds up the system may have significant effect on system integrity. While preventive maintenance actions can be carried out in a more planned fashion, corrective actions are more time sensitive as they directly affect the availability of the system. This study proposes a cost-effective dynamic Bayesian network modeling scheme to be used in the planning of corrective maintenance actions on systems having hidden components which have stochastic and structural dependencies. In such context, the regenerative air heater system which is a key element of a power plant is taken into consideration. The proposed maintenance framework offers several methods, each aiming to balance the cost with the probability effect using a normalization procedure. The methodologies are extensively simulated for sensitivity analysis under various downtime cost values. Fault effect methods with worst state probability efficiency measures give the least total cost for all downtime cost values and their distinction becomes significant as this value increases. Further statistical analysis concludes that considerable gains on maintenance costs can be achieved by the proposed approach.Yayın A sequential Monte Carlo method for blind phase noise estimation and data detection(IEEE, 2005) Panayırcı, Erdal; Çırpan, Hakan Ali; Moeneclaey, MarcIn this paper, a computationally efficient algorithm is presented for blind phase noise estimation and data detection jointly, based on a sequential Monte Carlo method. The basic idea is to treat the transmitted symbols as " missing data" and draw samples sequentially of them based on the observed signal samples up to time t. This way, the Bayesian estimates of the phase noise and the incoming data are obtained through these samples, sequentially drawn, together with their importance weights. The proposed receiver structure is seen to be ideally suited for high-speed parallel implementation using VLSI technology.Yayın Parçacık süzgeçleme ile hedef izleme uygulamasında topak çizelgeleme(IEEE, 2007) Özfidan, Özgür; Bayazıt, Uluğ; Çırpan, Hakan AliBu çalışmada, uzaklık ölçer algılayıcılarla hedef takibi uygulamasında algılayıcı çizelgeleme problemi ele alınmıştır. Çok algılayıcılı uygulamalarda algılayıcıların yönetimi ürettikleri verilerin sınıflandırılması için olduğu kadar algılayıcıların verimli kullanımı için de gereklidir. Algılayıcı yönetimindeki önemli hususlardan biri algılayıcı çizelgelemesidir. Algılayıcıları çizelgeleyerek bant genişliği, güç, ve hesaplamada ciddi ölçüde kazanımlar sağlanabilir.Yayın Cluster based sensor scheduling in a target tracking application with particle filtering(IEEE, 2007) Özfidan, Özgür; Bayazıt, Uluğ; Çırpan, Hakan AliIn multi-sensor applications management of sensors is necessary for the classification of data they produce and for the efficient use of sensors as well. One of the important aspects in sensor management is the sensor scheduling. By scheduling the sensors, serious reductions can be achieved in the cost of bandwidth, power, and computation. In this work a simple solution for the problem of sensor scheduling in a multi-sensor target tracking application is presented. Due to non-linearity of the problem itself, proposed solution is presented in the framework of non-linear Bayesian estimation.Yayın Software defect prediction using Bayesian networks(Springer, 2014-02) Okutan, Ahmet; Yıldız, Olcay TanerThere are lots of different software metrics discovered and used for defect prediction in the literature. Instead of dealing with so many metrics, it would be practical and easy if we could determine the set of metrics that are most important and focus on them more to predict defectiveness. We use Bayesian networks to determine the probabilistic influential relationships among software metrics and defect proneness. In addition to the metrics used in Promise data repository, we define two more metrics, i.e. NOD for the number of developers and LOCQ for the source code quality. We extract these metrics by inspecting the source code repositories of the selected Promise data repository data sets. At the end of our modeling, we learn the marginal defect proneness probability of the whole software system, the set of most effective metrics, and the influential relationships among metrics and defectiveness. Our experiments on nine open source Promise data repository data sets show that response for class (RFC), lines of code (LOC), and lack of coding quality (LOCQ) are the most effective metrics whereas coupling between objects (CBO), weighted method per class (WMC), and lack of cohesion of methods (LCOM) are less effective metrics on defect proneness. Furthermore, number of children (NOC) and depth of inheritance tree (DIT) have very limited effect and are untrustworthy. On the other hand, based on the experiments on Poi, Tomcat, and Xalan data sets, we observe that there is a positive correlation between the number of developers (NOD) and the level of defectiveness. However, further investigation involving a greater number of projects is needed to confirm our findings.Yayın Evaluation of proactive maintenance policies on a stochastically dependent hidden multi-component system using DBNs(Elsevier Ltd, 2021-07) Özgür Ünlüakın, Demet; Türkali, BusenurIn complex systems with stochastically dependent components which are not observed directly, determining an effective maintenance policy is a difficult task. In this paper, a dynamic Bayesian network based maintenance decision framework is proposed to evaluate proactive maintenance policies for such systems. Two preventive and one predictive maintenance strategies from a cost perspective are designed for multi-component dependable systems which aim to reduce maintenance cost while increasing system reliability at the same time. Tabu procedure is employed to avoid repetitive similar actions. The performances of the policies are compared with a reactive maintenance strategy and also with each other using different strategy parameters on a real life system confronted in thermal power plants for six different scenarios. The scenarios are designed considering different structures of system dependability and reactive cost. The results show that the threshold based maintenance which is the predictive strategy gives the minimum cost and maintenance number in almost all scenarios.Yayın Cross-layer ransomware detection framework for SDN using HMM, LSTM, and Bayesian inference(Institute of Electrical and Electronics Engineers Inc., 2025-08-28) Serter, Cemal Emre; Çeliktaş, BarışRansomware continues to pose a serious threat to endpoint computers as well as network systems, especially in Software Defined Networks (SDN) environments where programmability and centralized control offer novel attack surfaces. In this paper, a cross-layer detection model for ransomware is introduced that integrates host-based behavioral modeling using Hidden Markov Models (HMM), anomaly detection at flow level using Long Short-Term Memory (LSTM) networks, and probabilistic fusion through Bayesian inference. By correlating host and SDN layer anomalies, the system enhances early-stage detection and reduces false positives. A variational Bayesian approximation technique is utilized for decision score stabilization under ambiguous conditions. The model is evaluated with new ransomware datasets and obtains a range between 97.5%-99.92% F1-score across three benchmark datasets with less than 50 ms latency for detection. The hybrid framework gives a promising direction for real-time threat detection in resilient programmable networks.
