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    Establishment of interdisciplinary child protection teams in Turkey 2002-2006: Identifying the strongest link can make a difference!
    (PERGAMON-ELSEVIER SCIENCE LTD, 2009) Agirtan, Canan A.; Akar, Taner; Akbas, Seher; Akdur, Recep; Aydin, Cahide; Aytar, Gulsen; Ayyildiz, Suat; Baskan, Sevgi; Belgemen, Tugba; Bezirci, Ozdecan; Beyazova, Ufuk; Beyaztas, Fatma Yucel; Buken, Bora; Buken, Erhan; Camurdan, Aysu D.; Can, Demet; Canbaz, Sevgi; Canturk, Gurol; Ceyhan, Meltem; Coskun, Abdulhakim; Celik, Ahmet; Cetin, Fusun C.; Coskun, Ayse Gul; Dagcinar, Adnan; Dallar, Yildiz; Demirel, Birol; Demirogullari, Billur; Derman, Orhan; Dilli, Dilek; Ersahin, Yusuf; Esiyok, Burcu; Evinc, Gulin; Gencer, Ozlem; Gokler, Bahar; Hanci, Hamit; Iseri, Elvan; Isir, Aysun Baransel; Isiten, Nukhet; Kale, Gulsev; Karadag, Ferda; Kanbur, Nuray; Kilic, Birim; Kultur, Ebru; Kurtay, Derya; Kuruoglu, Asli; Miral, Suha; Odabasi, Aysun B.; Oral, Resmiye; Orhon, Filiz Simsek; Ozbesler, Cengiz; Ozdemir, Dilsad Foto; Ozkok, M. Selim; Ozmert, Elif; Oztop, Didem B.; Ozyurek, Hamit; Pasli, Figen; Peksen, Yildiz; Polat, Onur; Sabin, Figen; Sabin, Ahmet Rifat; Salacin, Serpil; Suskan, Emine; Tander, Burak; Tekin, Deniz; Teksam, Ozlern; Tiras, Ulku; Tomak, Yilmaz; Tumer, Ali Riza; Turla, Ahmet; Ulukol, Betul; Uslu, Runa; Tas, Fatma V.; Vatandas, Nilgun; Velipasaoglu, Sevtap; Yagmur, Fatih; Yagmurlu, Aydin; Yalcin, Songul; Yavuz, Sukruye; Yurdakok, Kadriye
    Objectives: The University of Iowa Child Protection Program collaborated with Turkish professionals to develop a training program on child abuse and neglect during 2002-2006 with the goals of increasing professional awareness and number of multidisciplinary teams (MDT), regional collaborations, and assessed cases. This paper summarizes the 5-year outcome. Methods: A team of instructors evaluated needs and held training activities in Turkey annually, and provided consultation when needed. Descriptive analysis was done via Excel and SPSS software. Results: Eighteen training activities were held with 3,570 attendees. Over the study period, the number of MDTs increased from 4 to 14. The MDTs got involved in organizing training activities in their institutions and communities. The number of medical curriculum lectures taught by MDTs to medical students/residents, conferences organized by the MDTs, and lectures to non-medical professional audiences increased significantly (R-2 = 91.4%, 83.8%, and 69.2%, respectively). The number of abuse cases assessed by the MDTs increased by five times compared to pre-training period. Conclusions: A culturally competent training program had a positive impact on professional attitudes and behaviors toward recognition and management of child abuse and neglect in Turkey. The need to partner with policy makers to revise current law in favor of a greater human services orientation became clear. Practice implications: Pioneers in developing countries may benefit from collaborating with culturally competent instructors from countries with more developed child protection systems to develop training programs so that professional development can improve recognition and management of child abuse and neglect. Published by Elsevier Ltd.
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    Hybrid AI-Powered Real-Time Distributed Denial of Service Detection and Traffic Monitoring for Software-Defined-Based Vehicular Ad Hoc Networks: A New Paradigm for Securing Intelligent Transportation Networks
    (MDPI, 2024) Polat, Onur; Oyucu, Saadin; Turkoglu, Muammer; Polat, Hueseyin; Aksoz, Ahmet; Yardimci, Fahri
    Vehicular Ad Hoc Networks (VANETs) are wireless networks that improve traffic efficiency, safety, and comfort for smart vehicle users. However, with the rise of smart and electric vehicles, traditional VANETs struggle with issues like scalability, management, energy efficiency, and dynamic pricing. Software Defined Networking (SDN) can help address these challenges by centralizing network control. The integration of SDN with VANETs, forming Software Defined-based VANETs (SD-VANETs), shows promise for intelligent transportation, particularly with autonomous vehicles. Nevertheless, SD-VANETs are susceptible to cyberattacks, especially Distributed Denial of Service (DDoS) attacks, making cybersecurity a crucial consideration for their future development. This study proposes a security system that incorporates a hybrid artificial intelligence model to detect DDoS attacks targeting the SDN controller in SD-VANET architecture. The proposed system is designed to operate as a module within the SDN controller, enabling the detection of DDoS attacks. The proposed attack detection methodology involves the collection of network traffic data, data processing, and the classification of these data. This methodology is based on a hybrid artificial intelligence model that combines a one-dimensional Convolutional Neural Network (1D-CNN) and Decision Tree models. According to experimental results, the proposed attack detection system identified that approximately 90% of the traffic in the SD-VANET network under DDoS attack consisted of malicious DDoS traffic flows. These results demonstrate that the proposed security system provides a promising solution for detecting DDoS attacks targeting the SD-VANET architecture.
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    Multi-Stage Learning Framework Using Convolutional Neural Network and Decision Tree-Based Classification for Detection of DDoS Pandemic Attacks in SDN-Based SCADA Systems
    (Mdpi, 2024) Polat, Onur; Turkoglu, Muammer; Polat, Huseyin; Oyucu, Saadin; Uzen, Huseyin; Yardimci, Fahri; Aksoz, Ahmet
    Supervisory Control and Data Acquisition (SCADA) systems, which play a critical role in monitoring, managing, and controlling industrial processes, face flexibility, scalability, and management difficulties arising from traditional network structures. Software-defined networking (SDN) offers a new opportunity to overcome the challenges traditional SCADA networks face, based on the concept of separating the control and data plane. Although integrating the SDN architecture into SCADA systems offers many advantages, it cannot address security concerns against cyber-attacks such as a distributed denial of service (DDoS). The fact that SDN has centralized management and programmability features causes attackers to carry out attacks that specifically target the SDN controller and data plane. If DDoS attacks against the SDN-based SCADA network are not detected and precautions are not taken, they can cause chaos and have terrible consequences. By detecting a possible DDoS attack at an early stage, security measures that can reduce the impact of the attack can be taken immediately, and the likelihood of being a direct victim of the attack decreases. This study proposes a multi-stage learning model using a 1-dimensional convolutional neural network (1D-CNN) and decision tree-based classification to detect DDoS attacks in SDN-based SCADA systems effectively. A new dataset containing various attack scenarios on a specific experimental network topology was created to be used in the training and testing phases of this model. According to the experimental results of this study, the proposed model achieved a 97.8% accuracy rate in DDoS-attack detection. The proposed multi-stage learning model shows that high-performance results can be achieved in detecting DDoS attacks against SDN-based SCADA systems.