Günümüzde iletişim teknolojilerinin gelişmesi birçok farklı alanda değişimlere neden olmaktadır. Bu alanlardan biri de VANET (Araç Alan Ağı) uygulama alanıdır. VANET alanında kullanım alanlarının artmasıyla birlikte VANET ağ güvenliğinin sağlanması daha kritik hale gelmiştir. VANET ağlarına yapılan saldırıları tespit etmek için birçok farklı sistem geliştirilmiştir. Makine öğrenimi tabanlı sistemler, bu saldırı tespit sistemlerinin (STS- Intrusion Detection Systems IDS) geliştirilmesinde en yaygın kullanılan yöntemlerden biridir. Bu makalede literatürde son dönemde yapılan makine öğrenmesi tabanlı VANET IDS üzerine araştırmalar yapılmıştır. Öncelikle VANET mimarisi ve güvenlik gereksinimleri sunulmuş, ardından kapsamlı bir literatür özeti verilmiş ve farklı parametreler üzerinden karşılaştırmalar yapılmıştır. Sonuç olarak, saldırı tespit sistemlerinde birçok farklı makine öğrenmesi modelinin kullanıldığı ve yüksek performanslı tespit gerçekleştirdiği tespit edilmiştir. STS'nin performansında kullanılan makine öğrenmesi algoritmasının yanı sıra birçok farklı parametrenin de performansta kritik rol oynadığı gösterilmiştir. Makale, literatür karşılaştırması (makine öğrenme modeli, simülasyon araçları, veri seti, makine öğrenme algoritması ve performans kriterleri gibi kriterler dikkate alınarak) sayesinde saldırı tespit sistemlerinin performansını artıracak kazanımlarla bu alanda yapılacak yeni çalışmalara rehberlik etmeyi amaçlamaktadır.

The increasing digitalization of energy transmission and distribution infrastructures has made industrial control systems (ICS), and especially IEC 61850-based communication structures, critical. IEC 61850 performs protection and control functions in substations in real time via GOOSE and MMS protocols. The fast and low-latency operation of these protocols is essential; however, their open structure leaves systems vulnerable to cyberattacks. Traditional signature-based solutions are insufficient for detecting such anomalies, and models capable of learning both time and state relationships are needed. This study develops a time-aware probabilistic NFA model to detect anomalous behavior in IEC 61850 traffic. The model analyzes GOOSE and MMS message sequences with both state transitions and time differences (Δt). Thus, not only the message sequence but also the timing variations between events are learned. The probability of each transition is dynamically updated, and deviations from normal behavior are marked as “anomalies”. The dataset used in this study was created based on normal and attack scenarios conducted in the Sakarya University Critical Infrastructure National Testbed Center Energy Laboratory (Center Energy). The experimental results obtained in the study show that the model detects time-based, structural, and behavioral anomalies with high accuracy. With a dual-model configuration, results of 91.7% accuracy, 88.9% precision, 100% recall, and a 94.1% F1-score were achieved; particularly in time-based attack scenarios, the model performance reached an accuracy level of up to 93%.

Objectives: Self-insertion of penile foreign bodies is a common phenomenon worldwide. In the literature, there are numerous reported cases of self-inflicted foreign objects in the urethra but in Turkey, the reported cases are relatively few and these are usually adult cases. Case: An 11 years-old male presented to the emergency department with inadvertently slipping a needle in his urethra after itching the urethral orifice with the needle. A plain x-ray of the pelvis showed a needle approximately about the level of the posterior urethra. By cystoscopy, the needle is completely removed from the bulbar urethra without any complication. Conclusion: Cases of self-inserted urethral bodies are not rare. Patients often present with voiding symptoms such as dysuria, pyuria, increased urinary frequency, hematuria, urinary retention, and penile or perineal swelling. Pelvic x-ray is usually sufficient for diagnosis; ultrasonography and computerized tomography are the next choices. Endoscopic retrieval is the initial management and, in most cases, it is successful; open surgery is rarely required.

The problems of traditional electricity grids have led to the emergence of smart grids. Unlike traditional energy systems, smart grids play an important role in the energy sector with their flexibility, programmability and reliability. However, the heterogeneous structure of smart grids consisting of different devices and protocols poses some problems in terms of complexity, service quality and security. In the literature, SDN (Software Defined Networks) paradigm is proposed as a solution to these problems. SDN and smart grid integration makes the energy sector more efficient, reliable and sustainable. On the other hand, smart meters used in the consumption area of smart grids provide instantaneous transmission of energy production and consumption data in homes to the center. With the support of IoT (Internet of Things) of these meters and components in the home area network (oven, IP camera, TV, etc.), the energy supply and demand balance can be managed more smoothly. In this study, a software-defined and IoT-based smart home architecture is proposed to obtain real energy consumption data. The proposed architecture is developed and implemented on the Mininet simulator with python code. As a result of simulations run under different process and attack scenarios, energy consumption data sets were created. A comparison of the anomaly detection performances of machine learning algorithms on the data sets that are considered to contribute to the literature has been made. As a result of this comparison, it was observed that the success rate of the random forest algorithm was higher than the other algorithms with 90-95 percent.

Time-series anomaly detection is a critical task in various domains, including industrial control systems, where the early detection of unusual patterns can prevent system failures and ensure operational reliability. This paper introduces ChaMTeC (CHAnnel Mixing and TEmporal Convolution Network), a novel deep learning framework designed for time-series anomaly detection. ChaMTeC integrates an inverted embedding strategy, multi-layer temporal encoding, and a Mean Squared Error (MSE)-based feedback mechanism with dynamic thresholding to enhance anomaly detection performance. The framework is particularly tailored for industrial environments, where anomalies are rare and often subtle, making detection challenging. We evaluate ChaMTeC on six publicly available datasets and a newly introduced dataset, WaterLog, which is specifically designed to reflect real-world industrial control system scenarios with reduced anomaly rates. The experimental results demonstrate that ChaMTeC outperforms state-of-the-art models, achieving superior performance in terms of F1-CPA (Coverage-based Point-Adjusted F1) scores. The WaterLog dataset, which has been made publicly available, provides a more realistic benchmark for evaluating anomaly detection systems in industrial settings, addressing the limitations of existing datasets that often contain frequent and densely packed anomalies. Our findings highlight the effectiveness of combining channel-mixing techniques with temporal convolutional networks and dynamic thresholding for detecting anomalies in complex industrial environments. The proposed framework offers a robust solution for real-time anomaly detection, contributing to the reliability and sustainability of critical infrastructure systems.