Pub Date : 2021-09-15DOI: 10.1109/UBMK52708.2021.9558878
Halil Ibrahim Okur, A. Sertbas
In the text classification process, which is a sub-task of NLP, the preprocessing and indexing of the text has a direct determining effect on the performance for NLP models. When the studies on pre-trained models are examined, it is seen that the changes made on the models developed for world languages or training the same model with a Turkish text dataset. Word-embedding is considered to be the most critical point of the text processing problem. The two most popular word embedding methods today are Word2Vec and Glove, which embed words into a corpus using multidimensional vectors. BERT, Electra and Fastext models, which have a contextual word representation method and a deep neural network architecture, have been frequently used in the creation of pre-trained models recently. In this study, the use and performance results of pre-trained models on TTC-3600 and TRT-Haber text sets prepared for Turkish text classification NLP task are shown. By using pre-trained models obtained with large corpus, a certain time and hardware cost, the text classification process is performed with less effort and high performance.
{"title":"Pretrained Neural Models for Turkish Text Classification","authors":"Halil Ibrahim Okur, A. Sertbas","doi":"10.1109/UBMK52708.2021.9558878","DOIUrl":"https://doi.org/10.1109/UBMK52708.2021.9558878","url":null,"abstract":"In the text classification process, which is a sub-task of NLP, the preprocessing and indexing of the text has a direct determining effect on the performance for NLP models. When the studies on pre-trained models are examined, it is seen that the changes made on the models developed for world languages or training the same model with a Turkish text dataset. Word-embedding is considered to be the most critical point of the text processing problem. The two most popular word embedding methods today are Word2Vec and Glove, which embed words into a corpus using multidimensional vectors. BERT, Electra and Fastext models, which have a contextual word representation method and a deep neural network architecture, have been frequently used in the creation of pre-trained models recently. In this study, the use and performance results of pre-trained models on TTC-3600 and TRT-Haber text sets prepared for Turkish text classification NLP task are shown. By using pre-trained models obtained with large corpus, a certain time and hardware cost, the text classification process is performed with less effort and high performance.","PeriodicalId":106516,"journal":{"name":"2021 6th International Conference on Computer Science and Engineering (UBMK)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121909260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-15DOI: 10.1109/UBMK52708.2021.9558994
Selim Buyrukoğlu
Failure to early detection of Alzheimer’s disease (AD) can lead memory deterioration. Therefore, early detection of AD is essential affecting the points of the brain that control vital functions. Various early AD detection approaches have been employed using machine learning. In literature, most of the early detection of AD approaches has been developed using single machine learning methods. Due to the importance of early detection of AD, the goal of this study is to improve the classification performance of the previous studies for early detection of AD applying ensemble learning methods including bagging, boosting and stacking. ADNI clinical dataset was used in this study with three target classes: Normal (CN), Mild Cognitive Impairment (MCI) and Alzheimer’s disease (AD). The proposed ensemble learning methods provided better classification performance compared to single machine learning methods. Besides, the best classification performance from the ensemble methods is obtained through the boosting (AdaBoost) ensemble (92.7%). This study revealed that the classification rate increased up to between 3.2% and 7.2% compared to single based machine learning approaches through the AdaBoost ensemble method.
{"title":"Improvement of Machine Learning Models’ Performances based on Ensemble Learning for the detection of Alzheimer Disease","authors":"Selim Buyrukoğlu","doi":"10.1109/UBMK52708.2021.9558994","DOIUrl":"https://doi.org/10.1109/UBMK52708.2021.9558994","url":null,"abstract":"Failure to early detection of Alzheimer’s disease (AD) can lead memory deterioration. Therefore, early detection of AD is essential affecting the points of the brain that control vital functions. Various early AD detection approaches have been employed using machine learning. In literature, most of the early detection of AD approaches has been developed using single machine learning methods. Due to the importance of early detection of AD, the goal of this study is to improve the classification performance of the previous studies for early detection of AD applying ensemble learning methods including bagging, boosting and stacking. ADNI clinical dataset was used in this study with three target classes: Normal (CN), Mild Cognitive Impairment (MCI) and Alzheimer’s disease (AD). The proposed ensemble learning methods provided better classification performance compared to single machine learning methods. Besides, the best classification performance from the ensemble methods is obtained through the boosting (AdaBoost) ensemble (92.7%). This study revealed that the classification rate increased up to between 3.2% and 7.2% compared to single based machine learning approaches through the AdaBoost ensemble method.","PeriodicalId":106516,"journal":{"name":"2021 6th International Conference on Computer Science and Engineering (UBMK)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122674719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-15DOI: 10.1109/UBMK52708.2021.9558930
Hasan Dalmaz, Erdal Erdal, H. Ünver
Developing technology brings many risk in terms of data security. In this regard, it is an important issue to detect attacks for network security. Intrusion detection systems developed due to technological developlments and increasing attack diversity have revealed the necessity of being more succesful in detecting attacks. Today, many studies are carried out on this subject. When the literature is examined, there are various studies with varying success rates in detecting network attacks using machine learning approaches. In this study, the NSL-KDD dataset was explained in detail, the positive aspects of the KDD Cup 99 dataset were specified, the classifier used, performance criteria and the success results obtained were evaluated. In addition, the developed GWO-MFO hybrid algorithm is mentioned and the result is shared.
技术的发展带来了数据安全方面的诸多风险。因此，检测攻击对网络安全至关重要。随着技术的发展和攻击多样性的增加，入侵检测系统的发展表明需要更成功地检测攻击。今天，许多研究都在这个问题上进行。当检查文献时，有各种研究使用机器学习方法检测网络攻击的成功率不同。在本研究中，详细解释了NSL-KDD数据集，指定了KDD Cup 99数据集的积极方面，评估了使用的分类器，性能标准和获得的成功结果。此外，还介绍了所开发的GWO-MFO混合算法，并对其结果进行了共享。
{"title":"Machine Learning Approaches in Detecting Network Attacks","authors":"Hasan Dalmaz, Erdal Erdal, H. Ünver","doi":"10.1109/UBMK52708.2021.9558930","DOIUrl":"https://doi.org/10.1109/UBMK52708.2021.9558930","url":null,"abstract":"Developing technology brings many risk in terms of data security. In this regard, it is an important issue to detect attacks for network security. Intrusion detection systems developed due to technological developlments and increasing attack diversity have revealed the necessity of being more succesful in detecting attacks. Today, many studies are carried out on this subject. When the literature is examined, there are various studies with varying success rates in detecting network attacks using machine learning approaches. In this study, the NSL-KDD dataset was explained in detail, the positive aspects of the KDD Cup 99 dataset were specified, the classifier used, performance criteria and the success results obtained were evaluated. In addition, the developed GWO-MFO hybrid algorithm is mentioned and the result is shared.","PeriodicalId":106516,"journal":{"name":"2021 6th International Conference on Computer Science and Engineering (UBMK)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131604122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-15DOI: 10.1109/UBMK52708.2021.9558939
A. Karaoglu, Hasan Caglar, A. Değirmenci, Omer Karal
Cardiovascular diseases is a general term given to the group of diseases that includes heart failure, heart attack, stroke. They are quite dangerous for human health. Various studies have been conducted in the literature to predict the survival of patients with heart failure. In this study, user-defined parameters of three different machine learning methods (logistic regression-LR, K nearest neighbor-KNN, and decision tree-DT) used in existing studies are optimized to make predictions with higher accuracy. In terms of objectivity and reliability of the experimental results, k-fold cross validation technique is applied. As a result, the performance results of this study are observed to be 10% and 3% higher than the literature in the DT and KNN algorithms, respectively. In particular, the proposed KNN method has shown that it can guide physicians in the decision-making process.
{"title":"Performance Improvement with Decision Tree in Predicting Heart Failure","authors":"A. Karaoglu, Hasan Caglar, A. Değirmenci, Omer Karal","doi":"10.1109/UBMK52708.2021.9558939","DOIUrl":"https://doi.org/10.1109/UBMK52708.2021.9558939","url":null,"abstract":"Cardiovascular diseases is a general term given to the group of diseases that includes heart failure, heart attack, stroke. They are quite dangerous for human health. Various studies have been conducted in the literature to predict the survival of patients with heart failure. In this study, user-defined parameters of three different machine learning methods (logistic regression-LR, K nearest neighbor-KNN, and decision tree-DT) used in existing studies are optimized to make predictions with higher accuracy. In terms of objectivity and reliability of the experimental results, k-fold cross validation technique is applied. As a result, the performance results of this study are observed to be 10% and 3% higher than the literature in the DT and KNN algorithms, respectively. In particular, the proposed KNN method has shown that it can guide physicians in the decision-making process.","PeriodicalId":106516,"journal":{"name":"2021 6th International Conference on Computer Science and Engineering (UBMK)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126453358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-15DOI: 10.1109/UBMK52708.2021.9558933
Ercan Atagün, Ahmet Aalbayrak
Regression algorithms are included in the supervised learning techniques of machine learning. Regression covers the operations of estimating the variable with the class label (output variable) by using the numerical values in a data with regression algorithms. When the desired performances cannot be achieved with the existing regression algorithms for a problem, Ensemble Learning models are applied. In the Ensemble Learning model, multiple predictive algorithms come together and aim to achieve a higher success than the success of an algorithm alone. In this study, honey production problem was estimated with Support vector machines, Multi-layer Perceptron Regressor, KNeighborsRegressor, Voting Regressor, RandomForestRegressor, AdaBoostRegressor, BaggingRegressor, GradientBoostingRegressor and the results were compared. It was observed that the ensemble learning models increased the prediction success with the regression processes.
{"title":"Analysis of Honey Production with Environmental Variables","authors":"Ercan Atagün, Ahmet Aalbayrak","doi":"10.1109/UBMK52708.2021.9558933","DOIUrl":"https://doi.org/10.1109/UBMK52708.2021.9558933","url":null,"abstract":"Regression algorithms are included in the supervised learning techniques of machine learning. Regression covers the operations of estimating the variable with the class label (output variable) by using the numerical values in a data with regression algorithms. When the desired performances cannot be achieved with the existing regression algorithms for a problem, Ensemble Learning models are applied. In the Ensemble Learning model, multiple predictive algorithms come together and aim to achieve a higher success than the success of an algorithm alone. In this study, honey production problem was estimated with Support vector machines, Multi-layer Perceptron Regressor, KNeighborsRegressor, Voting Regressor, RandomForestRegressor, AdaBoostRegressor, BaggingRegressor, GradientBoostingRegressor and the results were compared. It was observed that the ensemble learning models increased the prediction success with the regression processes.","PeriodicalId":106516,"journal":{"name":"2021 6th International Conference on Computer Science and Engineering (UBMK)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125307230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-15DOI: 10.1109/UBMK52708.2021.9558993
Ilke Yurtseven, Selami Bagriyanik, S. Ayvaz
With significant usage of social media to socialize in virtual environments, bad actors are now able to use these platforms to spread their malicious activities such as hate speech, spam, and even phishing to very large crowds. Especially, Twitter is suitable for these types of activities because it is one of the most common social media platforms for microblogging with millions of active users. Moreover, since the end of 2019, Covid-19 has changed the lives of individuals in many ways. While it increased social media usage due to free time, the number of cyber-attacks soared too. To prevent these activities, detection is a very crucial phase. Thus, the main goal of this study is to review the state-of-art in the detection of malicious content and the contribution of AI algorithms for detecting spam and scams effectively in social media.
{"title":"A Review of Spam Detection in Social Media","authors":"Ilke Yurtseven, Selami Bagriyanik, S. Ayvaz","doi":"10.1109/UBMK52708.2021.9558993","DOIUrl":"https://doi.org/10.1109/UBMK52708.2021.9558993","url":null,"abstract":"With significant usage of social media to socialize in virtual environments, bad actors are now able to use these platforms to spread their malicious activities such as hate speech, spam, and even phishing to very large crowds. Especially, Twitter is suitable for these types of activities because it is one of the most common social media platforms for microblogging with millions of active users. Moreover, since the end of 2019, Covid-19 has changed the lives of individuals in many ways. While it increased social media usage due to free time, the number of cyber-attacks soared too. To prevent these activities, detection is a very crucial phase. Thus, the main goal of this study is to review the state-of-art in the detection of malicious content and the contribution of AI algorithms for detecting spam and scams effectively in social media.","PeriodicalId":106516,"journal":{"name":"2021 6th International Conference on Computer Science and Engineering (UBMK)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130482252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-15DOI: 10.1109/UBMK52708.2021.9558996
Ekin Ecem Tatar, Murat Dener
Wireless sensor networks (WSNs) from the IoT subset consist of small sensor nodes with limited energy. Such nodes are capable of monitoring physical conditions and transmitting information between nodes without the need for physical media. Due to the lack of central authority and the deployment of random nodes on the network, WSNs and IoTs are prone to security threats and there are many attacks against these networks. Wormhole attack is a serious type of attack that can be resolved smoothly in networks but is difficult to observe. In this study, wormhole attack was shown and interpreted simulation results with simulated graphics in the NS2 simulation program.
{"title":"Wormhole Attacks in IoT Based Networks","authors":"Ekin Ecem Tatar, Murat Dener","doi":"10.1109/UBMK52708.2021.9558996","DOIUrl":"https://doi.org/10.1109/UBMK52708.2021.9558996","url":null,"abstract":"Wireless sensor networks (WSNs) from the IoT subset consist of small sensor nodes with limited energy. Such nodes are capable of monitoring physical conditions and transmitting information between nodes without the need for physical media. Due to the lack of central authority and the deployment of random nodes on the network, WSNs and IoTs are prone to security threats and there are many attacks against these networks. Wormhole attack is a serious type of attack that can be resolved smoothly in networks but is difficult to observe. In this study, wormhole attack was shown and interpreted simulation results with simulated graphics in the NS2 simulation program.","PeriodicalId":106516,"journal":{"name":"2021 6th International Conference on Computer Science and Engineering (UBMK)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122334602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-15DOI: 10.1109/UBMK52708.2021.9558964
U. Ünal, Ceyda Nur Kahya, Yaprak Kurtlutepe, H. Dağ
Awareness, in the sense of security, builds the backbone of operations understanding the current and future cyber activities. Situation awareness has become the focal point of securing systems due to dynamic nature of cyber domain. Technological advancements cause the volatility to transform into upcoming challenges. Understanding those is the key to keep cyber Situation Awareness (SA) progression. Earlier studies define required steps to administer cyber SA. These steps (perceive, comprehend, project, and resolve) are also adapted to cyber domain. Rapid technological changes redefine the content of those and thus, it creates demands improving automated tools, which play as systematic factor in nurturing SA. As a system factor, SIEM tools can be basis for comprehending cyber domain. In this work, we investigate recent studies contributed mainly to SIEM (Security Information and Event Management) tool’s enhancement to evaluate current state and help predict upcoming challenges for maintaining awareness. We use various criteria in our investigation such as; architecture improvement, affected SIEM process, utilized CTI (Cyber Threat Intelligence) artefact, implementation area, and type of produced result. In doing so, we aim to impart upward trends on CSA (Cyber Situation Awareness) to academia and industry professionals.
{"title":"Investigation of Cyber Situation Awareness via SIEM tools: a constructive review","authors":"U. Ünal, Ceyda Nur Kahya, Yaprak Kurtlutepe, H. Dağ","doi":"10.1109/UBMK52708.2021.9558964","DOIUrl":"https://doi.org/10.1109/UBMK52708.2021.9558964","url":null,"abstract":"Awareness, in the sense of security, builds the backbone of operations understanding the current and future cyber activities. Situation awareness has become the focal point of securing systems due to dynamic nature of cyber domain. Technological advancements cause the volatility to transform into upcoming challenges. Understanding those is the key to keep cyber Situation Awareness (SA) progression. Earlier studies define required steps to administer cyber SA. These steps (perceive, comprehend, project, and resolve) are also adapted to cyber domain. Rapid technological changes redefine the content of those and thus, it creates demands improving automated tools, which play as systematic factor in nurturing SA. As a system factor, SIEM tools can be basis for comprehending cyber domain. In this work, we investigate recent studies contributed mainly to SIEM (Security Information and Event Management) tool’s enhancement to evaluate current state and help predict upcoming challenges for maintaining awareness. We use various criteria in our investigation such as; architecture improvement, affected SIEM process, utilized CTI (Cyber Threat Intelligence) artefact, implementation area, and type of produced result. In doing so, we aim to impart upward trends on CSA (Cyber Situation Awareness) to academia and industry professionals.","PeriodicalId":106516,"journal":{"name":"2021 6th International Conference on Computer Science and Engineering (UBMK)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123140030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-15DOI: 10.1109/UBMK52708.2021.9559022
Tolgahan Satici, Recep Bayindir
Cross and up-selling models are sales models designed to deepen customers’ spending habits, segments and spending trends, and to increase the life span of the customer. Within the scope of the study, a software was created that presents all of the actionable value-added information production from raw data supported by artificial intelligence and machine learning in modular and automation form. Our study has enabled the rule-based customer base created with business knowledge to produce much more successful output with advanced analytical models such as k-means and apriori. With the technological infrastructure created with the big data infrastructure, an expanding infrastructure where voluminous data can be processed has been provided. Successful results were obtained by testing the outputs in Turkey's leading institutions.
{"title":"AI-Supported Cross and UP Sales Tendency Analysis System for Insurance Companies","authors":"Tolgahan Satici, Recep Bayindir","doi":"10.1109/UBMK52708.2021.9559022","DOIUrl":"https://doi.org/10.1109/UBMK52708.2021.9559022","url":null,"abstract":"Cross and up-selling models are sales models designed to deepen customers’ spending habits, segments and spending trends, and to increase the life span of the customer. Within the scope of the study, a software was created that presents all of the actionable value-added information production from raw data supported by artificial intelligence and machine learning in modular and automation form. Our study has enabled the rule-based customer base created with business knowledge to produce much more successful output with advanced analytical models such as k-means and apriori. With the technological infrastructure created with the big data infrastructure, an expanding infrastructure where voluminous data can be processed has been provided. Successful results were obtained by testing the outputs in Turkey's leading institutions.","PeriodicalId":106516,"journal":{"name":"2021 6th International Conference on Computer Science and Engineering (UBMK)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129669387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-15DOI: 10.1109/UBMK52708.2021.9559010
H. Dalkılıç, M. H. Özcanhan
The use of Internet of things (IoT) devices in smart cities has been increasing in recent years. The use of IoT devices, which facilitate the daily life of people living in the smart city, on wearable and mobile devices causes the vulnerability. Some countermeasures should be taken to prevent unauthorized access to IoT devices that contain personal data and to protect the data. In this study, the protocol created to ensure the security of the data communication of IoT devices in smart cities is explained. In the proposed design, IoT device-based secure data communication protocol with limited resources is presented. Data privacy methods that will work on IoT devices are designed to achieve high performance by consuming as few resources as possible. The proposed protocol provides secure data communication against 4 different attacks: Man-in-the-middle attack, malicious code injection attack, denial of service (DoS) attack, and replay attack. As a result of the formal analysis made with the Scyther tool, it is shown that data security is ensured.
{"title":"Strong Authentication Protocol for Identity Verification in Internet of Things (IoT)","authors":"H. Dalkılıç, M. H. Özcanhan","doi":"10.1109/UBMK52708.2021.9559010","DOIUrl":"https://doi.org/10.1109/UBMK52708.2021.9559010","url":null,"abstract":"The use of Internet of things (IoT) devices in smart cities has been increasing in recent years. The use of IoT devices, which facilitate the daily life of people living in the smart city, on wearable and mobile devices causes the vulnerability. Some countermeasures should be taken to prevent unauthorized access to IoT devices that contain personal data and to protect the data. In this study, the protocol created to ensure the security of the data communication of IoT devices in smart cities is explained. In the proposed design, IoT device-based secure data communication protocol with limited resources is presented. Data privacy methods that will work on IoT devices are designed to achieve high performance by consuming as few resources as possible. The proposed protocol provides secure data communication against 4 different attacks: Man-in-the-middle attack, malicious code injection attack, denial of service (DoS) attack, and replay attack. As a result of the formal analysis made with the Scyther tool, it is shown that data security is ensured.","PeriodicalId":106516,"journal":{"name":"2021 6th International Conference on Computer Science and Engineering (UBMK)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133723603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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