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OBJECTIVE:The aim of this study was to develop a Turkish-specific speech recognition test, considering phonemic balance, homogeneity, and familiarity criteria. MATERIALS and METHODS:The most frequently used Turkish monosyllabic words were selected from the corpus. Thirty-six young adults with normal hearing were divided into two groups and asked to listen to words from the word pool; the words were given twice at six different intensity levels. The least and most frequently known words were identified and eliminated in order to provide homogeneity. Three word lists, each composed of 50 phonemically balanced words, were developed to be used in the tests. These lists were divided into two according to the phonemic balance criteria. RESULTS:No statistically significant difference was found among the word lists. Furthermore, the internal reliability of each list was analyzed using KR-20 and was found to be above 98% for all lists. CONCLUSION:The lists derived from the Turkish language were ascertained to be appropriate for use. As a result of using the developed lists to test individuals with various auditory pathologies, it will be possible to assess the lists' capability to distinguish pathological cases according to the location of the pathology.

Information in patients’ medical histories is subject to various security and privacy concerns. Meanwhile, any modification or error in a patient’s medical data may cause serious or even fatal harm. To protect and transfer this valuable and sensitive information in a secure manner, radio-frequency identification (RFID) technology has been widely adopted in healthcare systems and is being deployed in many hospitals. In this paper, we propose a mutual authentication protocol for RFID tags based on elliptic curve cryptography and advanced encryption standard. Unlike existing authentication protocols, which only send the tag ID securely, the proposed protocol could also send the valuable data stored in the tag in an encrypted pattern. The proposed protocol is not simply a theoretical construct; it has been coded and tested on an experimental RFID tag. The proposed scheme achieves mutual authentication in just two steps and satisfies all the essential security requirements of RFID-based healthcare systems.

Reliable sources report that errors in drug administration are increasing the number of harmed or killed inpatients, during healthcare. This development is in contradiction to patient safety norms. A correctly designed hospital-wide ubiquitous system, using advanced inpatient identification and matching techniques, should provide correct medicine and dosage at the right time. Researchers are still making grouping proof protocol proposals based on the EPC Global Class 1 Generation 2 ver. 1.2 standard tags, for drug administration. Analyses show that such protocols make medication unsecure and hence fail to guarantee inpatient safety. Thus, the original goal of patient safety still remains. In this paper, a very recent proposal (EKATE) upgraded by a cryptographic function is shown to fall short of expectations. Then, an alternative proposal IMS-NFC which uses a more suitable and newer technology; namely Near Field Communication (NFC), is described. The proposed protocol has the additional support of stronger security primitives and it is compliant to ISO communication and security standards. Unlike previous works, the proposal is a complete ubiquitous system that guarantees full patient safety; and it is based on off-the-shelf, new technology products available in every corner of the world. To prove the claims the performance, cost, security and scope of IMS-NFC are compared with previous proposals. Evaluation shows that the proposed system has stronger security, increased patient safety and equal efficiency, at little extra cost.

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Drone technology is developing very rapidly. Flying devices accomplishing various applications are becoming an integral part of our daily life undoubtedly. Drones are characterized by extreme mobility, decent computing power, scalability, and a very short lifetime due to energy constraints. The rise of drones inevitably enabled swarms and drone networking applications. Drone networks is a pathbreaking subclass of flying ad-hoc networks with unique capabilities and specific requirements. One very important challenge with swarms is the device authentication problem, in other words, proving the identity of a single or a group of drones that request to join the swarm. In this paper, we tackle this emerging problem and propose a novel context-aware mutual authentication protocol. The proposed protocol provides authentication for groups of drones and supports recovering a swarm in case of network separation. Likewise, the protocol can handle drone joins and leaves. Moreover, the protocol is not dependent on network infrastructure, secure storage, and secure channels. We tested the protocol using an automated formal security protocol verification tool, called Scyther. The tests resulted in the complete verification of the authentication and secrecy claims for arbitrary network instances and all defined use-cases. The protocol is also shown to have proven performance advantages over the existing schemes.INDEX TERMS Authentication, drone networks, security, swarms, wireless ad-hoc networks.

The need for security in lightweight devices such as radio frequency identification tags is increasing and a pseudorandom number generator (PRNG) constitutes an essential part of the authentication protocols that provide security. The main aim of this research is to produce a lightweight PRNG for cryptographic applications in wireless identification and sensing platform family devices, and other related lightweight devices. This PRNG is produced with genetic programming methods using entropy calculation as the fitness function, and it is tested with the NIST statistical test suite. Moreover, it satisfies the requirements of the EPCGen2 standards.

Detecting intrusions in a network traffic has remained an issue for researchers over the years. Advances in the area of machine learning provide opportunities to researchers to detect network intrusion without using a signature database. We studied and analyzed the performance of a stacking technique, which is an ensemble method that is used to combine different classification models to create a better classifier, on the KDD'99 dataset. In this study, the stacking method is improved by modifying the model generation and selection techniques and by using different classifications algorithms as a combiner method. Model generation is performed using subsets of the dataset with randomly selected features and not all of these models are used as input for the combiner. Various metrics are used in model selection and only selected models are used as input for the combiner method. In our experiments, the stacking technique provided higher accuracy results all the time compared to pure machine learning techniques. The second important result in our experiments was obtaining the highest detection rate for user-to-root attacks compared to other studies.