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The rapid expansion of Internet of Things (IoT) technologies has revolutionized connectivity but has also exposed users to increased cyber threats. This study aims to enhance Intrusion Detection Systems (IDSs) for IoT networks by employing advanced machine learning (ML) techniques and addressing the challenge of dataset selection, moving beyond outdated standards such as KDD99 and NSL-KDD99. We focus on evaluating the effectiveness of the Aegean Wi-Fi Intrusion Dataset (AWID) within the IEEE 802.11 standard for IoT security. Through classification algorithms, we assess AWID’s effectiveness by measuring accuracy, detection rate and false positives using the WEKA tool. We introduce a robust ML framework tailored for wireless intrusion detection, conducting evaluations across various scenarios, including nominal and numeric classes and employ feature selection techniques to enhance model performance. Our experiments demonstrate the efficacy of ML-based intrusion detection, with the boosted decision tree (DT) excelling in overlapping feature selection methods. Remarkably, logistic regression achieves a 98.90% accuracy rate in the initial two evaluation phases. This research contributes significantly to intrusion detection by providing a comprehensive framework for identifying attacks in IoT contexts through ML techniques. Unlike previous studies, our approach utilizes the AWID dataset, which is more aligned with the current threat landscape, addressing the limitations of outdated datasets. Additionally, we bridge the gap between research and practical implementation by conducting our experiments using the WEKA tool. In summary, this study offers a novel and practical solution to the pressing challenge of intrusion detection in IoT networks, promoting safer and more resilient IoT deployments.

Consent management has become an important issue with the increased usage of the Internet and also smart devices that collect personal data. Each country enacts its regulations and laws for consent management. These laws ensure that personal data is not collected without the individual’s consent and cannot be processed with a purpose other than the stated purpose. The General Data Protection Regulation (GDPR) has strict rules regarding collecting and processing personal data. This paper proposes a new approach for auditable hybrid consent management systems using blockchain technology and a purpose tree. The suggested approach includes (1) the implementation of a GDPR-compliant consent management system using blockchain and purpose tree; (2) the implementation of an audit mechanism that detects consent violations and corrects consents; and (3) the use of both on-chain and off-chain technologies. The audit mechanism proposed in this paper detects possible violations by performing inspections on every transaction in the system. Besides, it immediately informs the data subject and the competent authorities regarding the relevant violations. As part of this study, a prototype of the architecture is developed as a proof of concept to evaluate the performance of critical components. The obtained experimental results show that the proposed hybrid architecture that use purpose tree effectively supports consent sharing between the parties.

The development of technology accelerated the digital transformation of information systems. As a consequence of this digitization, data became available at any time and in any place. However, despite this ease of data accessibility, persons’ privacy concerns and threats to data privacy have emerged. Thus, serious privacy problems arise while collecting, storing, accessing, sharing, and archiving personal data. Consent management aims to prevent these problems by preserving privacy and protecting personal data. Hence, there are international treaties and legal regulations for personal data protection which state that consent is required to collect, store, manage and share personal data. In this study, a Semantic Web-based personal consent management model is proposed to protect personal data privacy. The proposed model is domain-independent and aims to control and manage the consent of a person. In order to provide the privacy protection of personal data, the proposed model allows individuals to establish their privacy preferences by determining who can access their personal information, for what purposes, and under what circumstances. For this purpose, a group of ontology is created to ensure the informed consent process. The proposed consent management model is generic. As similar to general personal information, personal health information is also sensitive and must be protected from data leakage. Therefore, the proposed generic model is implemented with Semantic Web technologies and demonstrated for the healthcare domain.

Cloud computing is a technology in which the resources are delivered as services. Users can access them anywhere, anytime via the Internet without any need to know the infrastructure knowledge, experience, or even authority that provides such services. It has become an important medium for enterprises to develop their networks because of these resources. With the growing need for cloud computing, security is becoming important for both individuals and business needs. Many researchers have studied the security of cloud computing. Still, security gaps or threats are increasing as the demand for cloud computing connectivity increases. Because a third party mostly provides cloud platforms to the cloud client, data protection in the cloud is the primary issue. This paper presents a novel, effective lightweight homomorphic cryptographic algorithm which contains two layers of encryption. The first layer uses the new effective, light-weight cryptographic algorithm and the second layer multiplicative homomorphic schemes considered for improving security data in cloud computing. This approach offers both symmetric and asymmetric cryptography features. The proposed approach's performance is evaluated using a variety of metrics, including memory, computational time and (key sensitivity), statistical analysis, image histogram, and entropy change analysis. The proposed algorithm's experimental findings showed a high level of security and an apparent improvement in encryption execution time, memory usage, and throughput. When compared to the cryptographic systems widely used in cloud computing.

A secure concurrency control algorithms must be devoid of covert channels originating from data conflicts between transactions, in addition to ensuring database integrity. Current secure concurrency control algorithms mistreat transactions with higher access classes. Because it better solves the priority inversion problem. A widely used concurrency control algorithm is the two-phase locking with high priority (2 PL-HP). The inclusion of cyclic restart, deadlock, unnecessary abort, pseudo-priority inversion, and starvation, on the other hand, may reduce its performance. In this paper, a new enhanced of secure two-phase locking protocol with multilevel secure distributed database systems is presented, the idea of enhanced secure Two-Phase locking protocol by adding the security level to assign the priority to the transaction. The enhanced add a secure manager to decide the level of the security and give the instruction to process the transaction in real lock with data x/virtual lock with version data x0 or decided to Abort the transaction based on the security level. The suggested study intends to reduce transaction miss rate by addressing the following issues: deadlock by splitting the execution stage into locking and processing phases, it reduces unnecessary cycle restart, transaction aborts by checking the security priority level (high, medium, low), and generating virtual lock and versions data. Finally, simulation performance confirms that improvement in transactions miss's percent and reduction in transactions' rollbacks compared to other secure concurrency control.

Today’s technological improvements have made ubiquitous healthcare systems that converge into smart healthcare applications in order to solve patients’ problems, to communicate effectively with patients, and to improve healthcare service quality. The first step of building a smart healthcare information system is representing the healthcare data as connected, reachable, and sharable. In order to achieve this representation, ontologies are used to describe the healthcare data. Combining ontological healthcare data with the used and obtained data can be maintained by storing the entire health domain data inside big data stores that support both relational and graph-based ontological data. There are several big data stores and different types of big data sets in the healthcare domain. The goal of this paper is to determine the most applicable ontology data store for storing the big healthcare data. For this purpose, AllegroGraph and Oracle 12c data stores are compared based on their infrastructural capacity, loading time, and query response times. Hence, healthcare ontologies (GENE Ontology, Gene Expression Ontology (GEXO), Regulation of Transcription Ontology (RETO), Regulation of Gene Expression Ontology (REXO)) are used to measure the ontology loading time. Thereafter, various queries are constructed and executed for GENE ontology in order to measure the capacity and query response times for the performance comparison between AllegroGraph and Oracle 12c triple stores.

Sağlık alanı, farklı kurum ve kişilerin hizmet aldığı aynı zamanda da hizmet sunduğu dağıtık ve karmaşık bir alandır. Bir hasta ile ilgili sağlık veriler de bunun sonucunda; doktorlar, klinikler, eczaneler, sigorta şirketleri ve hastaneler arasında tamamen dağılmış durumdadır. Anlamsal olarak zengin ve yüksek seviyede yapılandırılmış bu dağıtık klinik verinin sağlık alanında her yerden uygun izinler ile paylaşılması ve yeniden kullanılabilmesi, son yıllarda bilgi sistemlerinin odaklandığı önemli çalışma alanlarından biridir. Anlamsal Web'in sunduğu verinin anlamını sunarak bilginin gösterilebilmesi ve bilgilerden yeni bilgiler çıkarılması, sağlık alanı için uygulanabilecek teknolojik bir alt yapı sunmaktadır. Yaşam sıvısı olan kan, içeriğindeki maddeler ile kişilerin hastalıkları hakkında doktorlara ipuçları vermektedir. Sağlık alanında kan testlerinin sonuçları birçok farklı klinik tarafından kullanılabilecek bilgiyi içermektedir. Hastalığın teşhis aşamasında, aynı kan testlerinin yeniden yapılması teşhis süresini uzatıp tedaviyi geciktirirken, maliyeti arttıran bir süreçtir. Kan Testi Ontolojisi, kan ile ilgili sağlık alanında yapılan tüm tıbbı testleri anlamsal olarak modellemek, kan ve kan testlerine ilişkin bilgileri ve bunlar arasındaki ilişkileri tanımlamak üzere geliştirilmiştir. Hastaların kişiselleştirilmiş kan test sonuçlarının sorgulanmasını, paylaşılmasını ve yeniden kullanılmasını sağlayacak olan bir sağlık bilgi sisteminin bilgi tabanı olarak kullanılması amacıyla geliştirilen Kan Testi Ontolojisi, sağlık alanında farklı tıbbi ontolojiler ile birlikte çalışabilir olabilmesi için tıbbi bilişim standartları ile de desteklenmektedir.

Health domain is a complex and distributed research area, where different institutions and people take and provide service, at the same time. Therefore, the health data about a patient is completely distributed among doctors, clinics, hospitals, pharmacies and insurance companies. To share and reuse the distributed, well-structured and semantically rich clinical data with the appropriate permissions from anywhere is one of the major areas that the research of information systems focused in healthcare domain in recent years. The semantic web provides a technological infrastructure with representing the meaning of data and reasoning new information from the existing knowledge for the healthcare domain. The blood, as the life fluid, gives hints to the clinicians about a patient's general health status by analyzing the ingredients in. The results of blood tests contain lots of information that can be used by different clinics. In the diagnostic phase, analyzing the blood for the same tests repeatedly delays to start the treatment process and increases the cost. The Blood Test Ontology is developed to model the blood tests semantically that is done in the health field and also to define information related with the blood and the blood tests as well as the relationships between them. The ontology in this work is developed with the aim to be used in the health information system, which should provide the querying, sharing and reusing the personalized the blood test result of the patients, as a knowledge base. The Blood Test Ontology is supported by the medical information standards to be able to interoperable with the other medical ontologies that are developed in the health.

Nowadays, the information that can be accessed online is increasing exponentially. However, this increase in the amount of information brings difficulties to users to access information relevant with their preferences in an effective way. These difficulties could be overcomed with providing customized information or service to an individual by using personalization approach. Profiling is the basis of personalization approach and the representation of person specific information. In a user-adaptive system, personalized results will be reached according to the user’s needs after a profile specific search. In this work, semantically rich profiles are created to present personal context and developed user profiles are integrated with policies to provide a rule-based personalization. Thus, personalized information will be achieved in an effective way through profile based constraints.

Günümüzde, çevrimiçi olarak erişilen bilgi üstsel olarak artmaktadır. Bilgi miktarındaki bu artış, kullanıcıların tercihleri ile uyumlu bilgiye etkin bir şekilde erişmesini zorlaştırmaktadır. Bu zorluklar, kişiselleştirme yaklaşımı kullanılarak, bireye özel bilginin ya da servisin sunulması ile aşılabilir. Kişiselleştirme yaklaşımının temeli, kişiye özel bilgiyi temsil eden profil yapısıdır. Kullanıcı-uyarlanabilir bir sistemde, profil tipine özgü bir aramanın sonucunda, kullanıcı ihtiyaçlarına göre kişiselleştirilmiş sonuçlara ulaşılacaktır. Bu çalışmada, kişisel içeriğin sunulması amacı ile anlamsal olarak zengin profiller oluşturulmakta ve geliştirilen kullanıcı profilleri, politikalar ile entegre edilerek kural-tabanlı bir kişiselleştirme sağlanmaktadır. Böylelikle, profili temel alan kısıtlar yardımı ile kişiselleştirilmiş bilgiye erişim etkin bir şekilde gerçekleştirilecektir.

Bilgisayar teknolojileri yaygınlaştıkça erişim denetimi düzeneklerine olan ihtiyaç da artmaktadır. Erişim denetiminin amacı, bir bilgisayar sistemi kullanıcısının gerçekleştirebileceği işlemleri sınırlandırmaktır. Böylelikle, erişim denetimi, güvenlik ihlaline neden olacak bir etkinliğin önlenmesini sağlamaktadır. Bilginin paylaşılmasını ve yeniden kullanımını sağlamak için, biçimsel anlambilimini kullanarak makinelerin diğer makineler ile iletişimine izin veren Anlamsal Web'in başarısı için erişim denetimi düzeneğine ihtiyaç duyulmaktadır. Erişim denetimi düzeneği, güvenilir bir Anlamsal Web'in sağlanması için, kullanıcının bir işlemi gerçekleştirmeden önce yerine getirmesi gereken belirli kısıtları belirtmektedir. Bu çalışmada, geleneksel erişim denetimi düzeneklerinden farklı olarak Anlamsal Web tabanlı politikaların kullanıldığı bir "Ontoloji Tabanlı Erişim Denetimi" düzeneği geliştirilmektedir. Bu düzenekte, erişim denetimi ile ilgili bilginin modellenmesi için ontolojiler kullanılmakta ve politika ontolojileri yaratılırken etki alanı bilgisi temel alınmaktadır.

As computer technologies become pervasive, the need for access control mechanisms grow. The purpose of an access control is to limit the operations that a computer system user can perform. Thus, access control ensures to prevent an activity which can lead to a security breach. For the success of Semantic Web, that allows machines to share and reuse the information by using formal semantics for machines to communicate with other machines, access control mechanisms are needed. Access control mechanism indicates certain constraints which must be achieved by the user before performing an operation to provide a secure Semantic Web. In this work, unlike traditional access control mechanisms, an "Ontology Based Access Control" mechanism has been developed by using Semantic Web based policies. In this mechanism, ontologies are used to model the access control knowledge and domain knowledge is used to create policy ontologies.

Bilgisayar teknolojileri yaygınlaştıkça erişim denetimi düzeneklerine olan ihtiyaç da artmaktadır. Erişim denetiminin amacı, bir bilgisayar sistemi kullanıcısının gerçekleştirebileceği işlemleri sınırlandırmaktır. Böylelikle, erişim denetimi, güvenlik ihlaline neden olacak bir etkinliğin önlenmesini sağlamaktadır. Bilginin paylaşılmasını ve yeniden kullanımını sağlamak için, biçimsel anlambilimini kullanarak makinelerin diğer makineler ile iletişimine izin veren Anlamsal Web'in başarısı için erişim denetimi düzeneğine ihtiyaç duyulmaktadır. Erişim denetimi düzeneği, güvenilir bir Anlamsal Web'in sağlanması için, kullanıcının bir işlemi gerçekleştirmeden önce yerine getirmesi gereken belirli kısıtları belirtmektedir. Bu çalışmada, geleneksel erişim denetimi düzeneklerinden farklı olarak Anlamsal Web tabanlı politikaların kullanıldığı bir "Ontoloji Tabanlı Erişim Denetimi" düzeneği geliştirilmektedir. Bu düzenekte, erişim denetimi ile ilgili bilginin modellenmesi için ontolojiler kullanılmakta ve politika ontolojileri yaratılırken etki alanı bilgisi temel alınmaktadır.