Industrial automation and control systems have gained increasing attention in the literature recently. Their integration with various systems has triggered considerable developments in critical infrastructure systems. With different network structures, these systems need to communicate with each other, work in an integrated manner, be controlled, and intervene effectively when necessary. Supervision Control and Data Acquisition (SCADA) systems are mostly utilized to achieve these aims. SCADA systems, which control and monitor the connected systems, have been the target of cyber attackers. These systems are subject to cyberattacks due to the openness to external networks, remote controllability, and SCADA-architecture-specific cyber vulnerabilities. Protecting SCADA systems on critical infrastructure systems against cyberattacks is an important issue that concerns governments in many aspects such as economics, politics, transport, communication, health, security, and reliability. In this study, we physically demonstrated a scaled-down version of a real water plant via a Testbed environment created including a SCADA system. In order to disrupt the functioning of the SCADA system in this environment, five attack scenarios were designed by performing various DDoS attacks, i.e., TCP, UDP, SYN, spoofing IP, and ICMP Flooding. Additionally, we evaluated a scenario with the baseline behavior of the SCADA system that contains no attack. During the implementation of the scenarios, the SCADA system network was monitored, and network data flow was collected and recorded. CNN models, LSTM models, hybrid deep learning models that amalgamate CNN and LSTM, and traditional machine learning models were applied to the obtained data. The test results of various DDoS attacks demonstrated that the hybrid model and the decision tree model are the most suitable for such environments, reaching the highest test accuracy of 95% and 99%, respectively. Moreover, we tested the hybrid model on a dataset that is used commonly in the literature which resulted in 98% accuracy. Thus, it is suggested that the security of the SCADA system can be effectively improved, and we demonstrated that the proposed models have a potential to work in harmony on real field systems.

Breast cancer continues to be one of the leading causes of women’s deaths around the world, and this has emphasized the necessity to have novel and interpretable diagnostic models. This work offers a clear learning deep learning model that integrates the mobility of MobileNet and two bio-driven optimization operators, the Firefly Algorithm (FLA) and Dingo Optimization Algorithm (DOA), in an effort to boost classification appreciation and the convergence of the model. The suggested model demonstrated excellent findings as the DOA-optimized MobileNet acquired the highest performance of 98.96 percent accuracy on the fusion test, and the FLA-optimized MobileNet scaled up to 98.06 percent and 95.44 percent accuracies on mammographic and ultrasound tests, respectively. Further to good quantitative results, Grad-CAM visualizations indeed showed clinically consistent localization of the lesions, which strengthened the interpretability and model diagnostic reliability of Grad-CAM. These results show that lightweight, compact CNNs can be used to do high-performance, multimodal breast cancer diagnosis.

Denetleyici Kontrol ve Veri Toplama Sistemleri veya Endüstriyel Kontrol Sistemleri, önemli görülen kritik altyapıların kontrolünü sağlayan sistemlerdir. Kritik altyapılara yönelik gerçekleştirilen ataklar, siber terör atakları olarak değerlendirilir. Bu kritik altyapıların siber terör ataklarına karşı güvenliğinin sağlanması ve işleyişinin devamlılığı büyük önem arz etmektedir. Bu çalışmada kritik altyapılardan gaz boru hattı kontrol sistemine ait bir veri kümesi kullanılmaktadır. Veri kümesinde, mevcut SCADA protokollerinden Modbus protokolüne yönelik Command Injection, Reconnaissance and DoS (Denial of Service) gibi kategorilerde çeşitli ataklar gerçekleştirilmiştir. Böylece atak uygulanan ve atak uygulanmayan durumların sahip olduğu davranışların incelenmesi, değerlendirilmesi ve atak tespitinin yapılabilmesi hedeflenmektedir. Bunun için veri kümesi üzerinde çeşitli algoritmalar ile veri madenciliği yöntemi kullanılmıştır. Elde edilen analiz sonuçlarına göre en doğru sınıflandırma oranının Random Tree algoritması ile sağlandığı görülmüştür. Bu algoritmaya ait analiz sonuçları incelenerek siber terör atak davranışları belirlenmiş ve böylece siber terör atak tespitinin gerçekleştirilmesi için ilgili alana önemli bir katkı sunulmuştur. Denetleyici Kontrol ve Veri Toplama Sistemlerinin veya Endüstriyel Kontrol Sistemlerinin siber güvenliğinin sağlanması için bu tür çalışmaların daha fazla yapılması ve yeni veri kümelerinin üretilerek kullanıma sunulması gerekmektedir.

The Internet of Medical Things (IoMT) enables real-time monitoring and decision support systems in healthcare. However, due to their heterogeneous structure, limited resources, and high criticality, IoMT networks are vulnerable to cyberattacks. This situation increases the need for low-latency, high-accuracy, and generalizable attack detection systems. In this experimental study, the Multi-Scale Depthwise Channel Attention Network (MSCA-Net) model is proposed for multi-class attack detection in IoMT environments. The model consists of three core components: multi-scale depthwise separable convolutions to capture traffic patterns across different time scales, a squeeze-and-excitation-based channel attention mechanism that adaptively weights discriminative features, and a lightweight unidirectional LSTM layer that models temporal dependencies. This architecture enables effective representation learning with low parameter costs. The proposed model was evaluated on the WUSTL-EHMS-2020 and CICIoMT2024 datasets. On the CICIoMT2024 dataset, it achieved 99.75% accuracy and a weighted F1 score of 99.77% in a 6-class scenario. It has also demonstrated competitive results in 19-class fine-grained classification. Experimental comparisons show that MSCA-Net offers a better performance-to-cost trade-off compared to nine different baseline models. Furthermore, it demonstrates a speed advantage of up to two times in inference time. The results obtained at the conclusion of the experimental study demonstrate that the proposed approach effectively addresses the challenges of multi-scale feature extraction, class imbalance, and computational efficiency. Furthermore, the model appears to offer a viable solution for real-time attack detection in IoMT environments.

Distributed Denial of Service Attacks (DDoS) are threaten every device connected to the Internet. The fast progress and wide spreading DDoS attacks are among the most well-known features of them. Many studies have been conducted to reduce the impact of these fast-progressing and widespread attacks. However, because of the continuous development of attack types and the implementation of different techniques, the prevention of attacks has not been fully achieved. Therefore, within the scope of this study, a DDoS attack was examined first and applications for detecting it were investigated. A system has been proposed to detect DDoS attacks using data mining methods. For the proposed system, experiment mechanisms for Transmission Control Protocol (TCP) Flooding, Spoofing Internet Protocol (IP), SYN Flood with Spoofed IP, and User Datagram Protocol (UDP) Flooding, which are among the DDoS attack types, were established and the attacks were performed to obtain network flow data. The classification was made with appropriate data mining methods according to the specified features and ZeroR, OneR, Naive Bayes, Bayes Net, Decision Stump, and J48 algorithms were used. According to these algorithms, the best classification rate has been reached with J48 algorithm. The results have shown that the proposed system plays an important role in determining the DDoS attack type. The proposed system will ensure that appropriate detection mechanisms are applied more quickly, effectively and efficiently in real attacks.

This study aims to classify the “specific result” output, a rarely explored aspect in the literature, using a widely adopted dataset in SCADA system security. While most previous studies have concentrated on binary or categorized result outputs, this research uniquely focuses on specific result classification. To mitigate class imbalance and enhance model performance, the Synthetic Minority Oversampling Technique (SMOTE) was applied. Subsequently, multiple machine learning and deep learning algorithms, including Random Forest, Logistic Regression, Decision Tree, Naive Bayes, Support Vector Machine, Convolutional Neural Network, Long Short-Term Memory, Extended Long Short-Term Memory, Multilayer Perceptron, Deep Neural Network, and Gated Recurrent Unit, were implemented. Experimental results demonstrated that the Random Forest model achieved an accuracy of 99%, while the MLP model reached 95%. These results outperform previous studies on specific result classification and confirm the effectiveness of the proposed approach for attack detection in Industrial Control Systems.