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Yayın Security analysis of coap and dtls protocols for internet of things applications(Işık Üniversitesi, 2019-08-26) Gürkan, Ali Tunca; Tüysüz Erman, Ayşegül; Işık Üniversitesi, Fen Bilimleri Enstitüsü, Bilgisayar Mühendisliği Yüksek Lisans ProgramıInternet of Things is a very fast growing area. Its requirements and related technologies are changing from day to day. In Internet of Things, devices can communicate with each other with different messaging protocols. The latest messaging protocols are well developed, but they are too heavy to be run on devices developed with old technology. Therefore, these devices have to be operated with old-fashioned protocols. This makes devices vulnerable to security risks. CoAP is a newly developed messaging protocol for constrained devices used in Internet of Things applications. The protocol is a variant of HTTP, so it has similar speci cations. CoAP does not have an embedded security mechanism. Therefore, another protocol called DTLS is used on top of it to provide security. DTLS has powerful functions like handshaking and session processes; however, it is weak against DoS attacks. In this study, we develop a security extension for Internet of Things devices using CoAP with DTLS for secure messaging. DTLS applies handshaking process for every received request. The handshaking process is the most time and resource consuming part of the communication. We propose a security extension to prevent unnecessary messaging during handshaking process of an attacker device that sends a lot of unauthenticated requests. When a client sends requests to a server that has the proposed security extension, the server counts unsuccessful handshaking processes for each client. If the count passes a limit of suspicious requests, the security extension on server adds the client's IP address into a banned IPs list. Until the expiration time, the server does not accept any request from the banned IP address. Our proposed security extension is tested in different scenarios to examine the effects on the network. The results of the experiments show that the enhanced security extension decreases delays on the network and it is helpful for communication between authenticated devices.Yayın A new approach for named entity recognition(IEEE, 2017) Ertopçu, Burak; Kanburoğlu, Ali Buğra; Topsakal, Ozan; Açıkgöz, Onur; Gürkan, Ali Tunca; Özenç, Berke; Çam, İlker; Avar, Begüm; Ercan, Gökhan; Yıldız, Olcay TanerMany sentences create certain impressions on people. These impressions help the reader to have an insight about the sentence via some entities. In NLP, this process corresponds to Named Entity Recognition (NER). NLP algorithms can trace a lot of entities in the sentence like person, location, date, time or money. One of the major problems in these operations are confusions about whether the word denotes the name of a person, a location or an organisation, or whether an integer stands for a date, time or money. In this study, we design a new model for NER algorithms. We train this model in our predefined dataset and compare the results with other models. In the end we get considerable outcomes in a dataset containing 1400 sentences.Yayın Shallow parsing in Turkish(IEEE, 2017) Topsakal, Ozan; Açıkgöz, Onur; Gürkan, Ali Tunca; Kanburoğlu, Ali Buğra; Ertopçu, Burak; Özenç, Berke; Çam, İlker; Avar, Begüm; Ercan, Gökhan; Yıldız, Olcay TanerIn this study, shallow parsing is applied on Turkish sentences. These sentences are used to train and test the per-formances of various learning algorithms with various features specified for shallow parsing in Turkish.Yayın All-words word sense disambiguation for Turkish(IEEE, 2017) Açıkgöz, Onur; Gürkan, Ali Tunca; Ertopçu, Burak; Topsakal, Ozan; Özenç, Berke; Kanburoğlu, Ali Buğra; Çam, İlker; Avar, Begüm; Ercan, Gökhan; Yıldız, Olcay TanerIdentifying the sense of a word within a context is a challenging problem and has many applications in natural language processing. This assignment problem is called word sense disambiguation(WSD). Many papers in the literature focus on English language and data. Our dataset consists of 1400 sentences translated to Turkish from the Penn Treebank Corpus. This paper seeks to address and discuss 6 different feature extraction methods and its classification performances using C4.5, Random Forests, Rocchio, Naive Bayes, KNN, Linear and multilayer Perceptron. This paper calls into question how the described features perform on a morphologically rich language (Turkish) with several classifiers.
