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Yayın A novel similarity based unsupervised technique for training convolutional filters(IEEE, 2023-05-17) Erkoç, Tuğba; Eskil, Mustata TanerAchieving satisfactory results with Convolutional Neural Networks (CNNs) depends on how effectively the filters are trained. Conventionally, an appropriate number of filters is carefully selected, the filters are initialized with a proper initialization method and trained with backpropagation over several epochs. This training scheme requires a large labeled dataset, which is costly and time-consuming to obtain. In this study, we propose an unsupervised approach that extracts convolutional filters from a given dataset in a self-organized manner by processing the training set only once without using backpropagation training. The proposed method allows for the extraction of filters from a given dataset in the absence of labels. In contrast to previous studies, we no longer need to select the best number of filters and a suitable filter weight initialization scheme. Applying this method to the MNIST, EMNIST-Digits, Kuzushiji-MNIST, and Fashion-MNIST datasets yields high test performances of 99.19%, 99.39%, 95.03%, and 90.11%, respectively, without applying backpropagation training or using any preprocessed and augmented data.Yayın TENET: a new hybrid network architecture for adversarial defense(Springer Science and Business Media Deutschland GmbH, 2023-08) Tuna, Ömer Faruk; Çatak, Ferhat Özgür; Eskil, Mustafa TanerDeep neural network (DNN) models are widely renowned for their resistance to random perturbations. However, researchers have found out that these models are indeed extremely vulnerable to deliberately crafted and seemingly imperceptible perturbations of the input, referred to as adversarial examples. Adversarial attacks have the potential to substantially compromise the security of DNN-powered systems and posing high risks especially in the areas where security is a top priority. Numerous studies have been conducted in recent years to defend against these attacks and to develop more robust architectures resistant to adversarial threats. In this study, we propose a new architecture and enhance a recently proposed technique by which we can restore adversarial samples back to their original class manifold. We leverage the use of several uncertainty metrics obtained from Monte Carlo dropout (MC Dropout) estimates of the model together with the model’s own loss function and combine them with the use of defensive distillation technique to defend against these attacks. We have experimentally evaluated and verified the efficacy of our approach on MNIST (Digit), MNIST (Fashion) and CIFAR10 datasets. In our experiments, we showed that our proposed method reduces the attack’s success rate lower than 5% without compromising clean accuracy.Yayın Distribution games: a new class of games with application to user provided networks(Institute of Electrical and Electronics Engineers Inc., 2022-11-29) Taşçı, Sinan Emre; Shalom, Mordechai; Korçak, ÖmerUser Provided Network (UPN) is a promising solution for sharing the limited network resources by utilizing user capabilities as a part of the communication infrastructure. In UPNs, it is an important problem to decide how to share the resources among multiple clients in decentralized manner. Motivated by this problem, we introduce a new class of games termed distribution games that can be used to distribute efficiently and fairly the bandwidth capacity among users. We show that every distribution game has at least one pure strategy Nash equilibrium (NE) and any best response dynamics always converges to such an equilibrium. We consider social welfare functions that are weighted sums of bandwidths allocated to clients. We present tight upper bounds for the price of anarchy and price of stability of these games provided that they satisfy some reasonable assumptions. We define two specific practical instances of distribution games that fit these assumptions. We conduct experiments on one of these instances and demonstrate that in most of the settings the social welfare obtained by the best response dynamics is very close to the optimum. Simulations show that this game also leads to a fair distribution of the bandwidth.Yayın ANN activation function estimators for homomorphic encrypted inference(Institute of Electrical and Electronics Engineers Inc., 2025-06-13) Harb, Mhd Raja Abou; Çeliktaş, BarışHomomorphic Encryption (HE) enables secure computations on encrypted data, facilitating machine learning inference in sensitive environments such as healthcare and finance. However, efficiently handling non-linear activation functions, specifically Sigmoid and Tanh, remains a significant computational challenge for encrypted inference using Artificial Neural Networks (ANNs). This study introduces a lightweight, ANN-based estimator designed to accurately approximate activation functions under homomorphic encryption. Unlike traditional polynomial and piecewise linear approximations, the proposed ANN estimators achieve superior accuracy with lower computational overhead associated with bootstrapping or high-degree polynomial techniques. These estimators are trained on plaintext data and seamlessly integrated into encrypted inference pipelines, significantly outperforming conventional methods. Experimental evaluations demonstrate notable improvements, with ANN estimators enhancing accuracy by approximately 2% for Sigmoid and up to 73% for Tanh functions, improving F1-scores by approximately 2% for Sigmoid and up to 88% for Tanh, and markedly reducing Mean Square Error (MSE) by up to 96% compared to polynomial approximations. The ANN estimator achieves an accuracy of 97.70% and an AUC of 0.9997 when integrated into a CNN architecture on the MNIST dataset, and an accuracy of 85.25% with an AUC of 0.9459 on the UCI Heart Disease dataset during ciphertext inference. These results underscore the estimator’s practical effectiveness and computational feasibility, making it suitable for secure and efficient ANN inference in encrypted environments.Yayın Automated diagnosis of Alzheimer’s Disease using OCT and OCTA: a systematic review(Institute of Electrical and Electronics Engineers Inc., 2024-08-06) Turkan, Yasemin; Tek, Faik Boray; Arpacı, Fatih; Arslan, Ozan; Toslak, Devrim; Bulut, Mehmet; Yaman, AylinRetinal optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) have emerged as promising, non-invasive, and cost-effective modalities for the early diagnosis of Alzheimer's disease (AD). However, a comprehensive review of automated deep learning techniques for diagnosing AD or mild cognitive impairment (MCI) using OCT/OCTA data is lacking. We addressed this gap by conducting a systematic review using the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. We systematically searched databases, including Scopus, PubMed, and Web of Science, and identified 16 important studies from an initial set of 4006 references. We then analyzed these studies through a structured framework, focusing on the key aspects of deep learning workflows for AD/MCI diagnosis using OCT-OCTA. This included dataset curation, model training, and validation methodologies. Our findings indicate a shift towards employing end-to-end deep learning models to directly analyze OCT/OCTA images in diagnosing AD/MCI, moving away from traditional machine learning approaches. However, we identified inconsistencies in the data collection methods across studies, leading to varied outcomes. We emphasize the need for longitudinal studies on early AD and MCI diagnosis, along with further research on interpretability tools to enhance model accuracy and reliability for clinical translation.Yayın A hierarchical key assignment scheme: a unified approach for ccalability and efficiency(IEEE, 2024-05-24) Çelikbilek, İbrahim; Çeliktaş, Barış; Özdemir, EnverThis study introduces a hierarchical key assignment scheme (HKAS) based on the closest vector problem in an inner product space (CVP-IPS). The proposed scheme offers a comprehensive solution with scalability, flexibility, cost-effectiveness, and high performance. The key features of the scheme include CVP-IPS based construction, the utilization of two public keys by the scheme, a distinct basis set designated for each class, a direct access scheme to enhance user convenience, and a rigorous mathematical and algorithmic presentation of all processes. This scheme eliminates the need for top-down structures and offers a significant benefit in that the lengths of the basis sets defined for classes are the same and the costs associated with key derivation are the same for all classes, unlike top-down approaches, where the higher class in the hierarchy generally incurs much higher costs. The scheme excels in both vertical and horizontal scalability due to its utilization of the access graph and is formally proven to achieve strong key indistinguishability security (S-KI-security). This research represents a significant advancement in HKAS systems, providing tangible benefits and improved security for a wide range of use cases.Yayın Photogrammetry-The science of precise measurements from images: a themed issue in Honour of Professor Emeritus Armin Grün in anticipation of his 80th birthday(Multidisciplinary Digital Publishing Institute (MDPI), 2024-12) Qin, Rongjun; Akça, Devrim; Remondino, Fabio[No abstract available]Yayın Relationships among organizational-level maturities in artificial intelligence, cybersecurity, and digital transformation: a survey-based analysis(Institute of Electrical and Electronics Engineers Inc., 2025-05-19) Kubilay, Burak; Çeliktaş, BarışThe rapid development of digital technology across industries has highlighted the growing need for enhanced competencies in Artificial Intelligence (AI), Cyber security (CS), and Digital Transformation (DT). While there is extensive research on each of these domains in isolation, few studies have investigated their relationship and joint impact on organizational maturity. This study aims to address this gap by analyzing the relationships among the maturity levels of AI, CS, and DT at the organizational level using Structural Equation Modeling (SEM) and descriptive statistical methods. A mixed-methods design combines quantitative survey data with synthetic modeling techniques to assess organizational preparedness. The findings demonstrate significant bidirectional correlations among AI, CS, and DT, with technology and finance being more advanced than government and education. The research highlights the necessity of an integrated AI-CS strategy and provides actionable recommendations to increase investments in these domains. In contrast to the preceding fragmented evaluations, the current research establishes a comprehensive, empirically grounded framework that acts as a strategic reference point for digital resilience. Follow-up studies will involve collecting real-world industry data in support of empirical validation and predictive ability in measuring AI and CS maturity. This research adds to the existing literature by filling the gaps among fragmented digital maturity models and providing a consistent empirical base for organizations to thrive in an evolving technological environment.Yayın From policy to practice: a sector-agnostic operational framework for post-quantum cryptography transition(Institute of Electrical and Electronics Engineers Inc., 2026-03-02) Birgin, Berat; Çeliktaş, BarışThe pace of quantum computing development necessitates not only the adoption of post-quantum cryptographic algorithms, but also the establishment of an executable and auditable institutional transition process. Although guidance documents published by the National Institute of Standards and Technology (NIST) and roadmaps proposed by the Post-Quantum Cryptography Coalition (PQCC) articulate strategic objectives, they largely remain procedural constructs lacking a concrete operational execution model. This paper presents an industry-neutral operational framework that translates policy-level post-quantum cryptography (PQC) guidance into deterministic, proof-producing process flows encompassing cryptographic asset discovery, classification, risk modeling, algorithm selection, deployment, monitoring, and governance enforcement. Central to the framework is a deterministic Quantum Risk Scoring (QRS) function, calibrated using the Analytical Hierarchy Process (AHP), which enables reproducible asset prioritization and policy-driven enforcement decisions. Framework executability is further strengthened through cryptography-aware continuous integration/continuous deployment (CI/CD) validation gates and downgrade protection mechanisms, ensuring the generation of verifiable and immutable audit artifacts. A scenario-based operational validation, implemented using open-source toolchains, demonstrates the framework’s operability, auditability, and governance alignment without relying on empirical cryptographic performance benchmarks, confirming that PQC transition can be operationalized as a verifiable lifecycle process bridging policy guidance with enforceable technical actions. Rather than introducing new cryptographic primitives, this work formalizes PQC transition as an operational systems-engineering problem centered on governance-enforced execution and lifecycle verifiability.Yayın Hierarchical secure key assignment scheme(Public Library of Science, 2026-02-18) Çeliktaş, Barış; Çelikbilek, İbrahim; Güzey, Süeda; Özdemir, EnverThis work presents a novel hierarchical key assignment mechanism for access control, designed to be computationally lightweight and optimized for digital environments with structured access policies. By leveraging orthogonal projection and distributing a basis to each group, it enables flexible and efficient left-to-right and top-down access structures. The scheme ensures that parent groups can derive the secret keys of their child groups while preventing unauthorized reverse access. It is resilient against collusion attacks and privilege escalation, offering robust key recovery and indistinguishability properties. Moreover, it guarantees strong key indistinguishability under adversarial models and facilitates a secure rekeying process without reliance on a trusted third party. To demonstrate practical efficiency, we provide a full analytical complexity evaluation showing that key derivation requires at most ∂(n2i ) operations, where ni is the dimension of the assigned subspace. For typical deployment parameters used in the experiments, the total key material per user remains compact (≈ 3,072 bits), significantly smaller than well-known post-quantum schemes such as Dilithium-5 (38,912 bits). The storage requirement scales linearly with the number of groups (ck+1 bases for c groups with at most k members), ensuring that even large hierarchies remain lightweight. Our evaluation further shows that selective rekeying affects only the descendants of the modified group, resulting in communication overhead of ∂(m′λ) bits, where m′ is the number of affected users and λ is the key length. These results collectively highlight the scheme’s scalability, low storage footprint, and suitability for large access hierarchies.
