In recent years, wireless sensor network (WSN) is employed in many application areas such as monitoring, tracking, and controlling. For many applications of WSN, security is an important requirement. However, security solutions in WSN differ from traditional networks due to resource limitation and computational constraints. This paper analyzes security solutions: TinySec, IEEE 802.15.4, SPINS, MiniSEC, LSec, LLSP, LISA, and LISP in WSN. The paper also presents characteristics, security requirements, attacks, encryption algorithms, and operation modes. This paper is considered to be useful for security designers in WSNs.

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It is the efficient use of resources expected from an exam scheduling application. There are various criteria for efficient use of resources and for all tests to be carried out at minimum cost in the shortest possible time. It is aimed that educational institutions with such criteria successfully carry out central examination organizations. In the study, a two-stage genetic algorithm was developed. In the first stage, the assignment of courses to sessions was carried out. In the second stage, the students who participated in the test session were assigned to examination rooms. Purposes of the study are increasing the number of joint students participating in sessions, using the minimum number of buildings in the same session, and reducing the number of supervisors using the minimum number of classrooms possible. In this study, a general purpose exam scheduling solution for educational institutions was presented. The developed system can be used in different central examinations to create originality. Given the results of the sample application, it is seen that the proposed genetic algorithm gives successful results. MERKEZİ SINAVLARDA YAŞANAN SINAV ÇİZELGELEME PROBLEMLERİNİN GENETİK ALGORİTMALAR İLE ÇÖZÜLMESİÖz Bir sınav çizelgeleme uygulamasından beklenen kaynakların verimli kullanımıdır. Kaynakları verimli kullanabilmek ve en kısa zamanda en az maliyetle bütün sınavların gerçekleştirilmesi için çeşitli kıstaslar vardır. Yapılan çalışma ile bu tür kıstaslara sahip eğitim kurumlarının, merkezi sınav organizasyonlarını başarıyla gerçekleştirmesi amaçlanmıştır. Çalışmada, iki aşamalı genetik algoritma geliştirilmiştir. Birinci aşamada derslerin oturumlara atanması işlemi, ikinci aşamada ise ilgili oturumda sınava katılacak öğrencilerin sınav salonlarına atanması işlemi gerçekleştirilmiştir. Oturumlara katılan ortak öğrenci sayısının arttırılması, aynı oturumda asgari bina kullanımı, mümkün olan en az sayıda sınıf-sıra kullanılarak gözetmen sayısının azaltılması yapılan çalışmanın amaçlarını oluşturmaktadır. Yapılan bu çalışmada eğitim kurumlarına yönelik genel amaçlı sınav çizelgeleme çözümü sunulmuştur. Geliştirilen sistem farklı merkezi sınavlar içinde kullanılabilmesiyle özgünlük oluşturmaktadır. Örnek uygulama sonuçlarına bakıldığında önerilen genetik algoritmanın başarılı sonuçlar verdiği görülmektedir.

In recent years, the studies in inter-devices communication (M2M) which is considered to be the Internet, ecosystem of the objects have accelerated in line with the developments in mobile communication technologies. This way, development of systems making human lives easier in smart environment, smart agriculture, smart grid fields with monitoring purposes which can be considered among M2M implementations is now possible. These systems can be developed with sensor node platform which can detect and communicate. Wireless Sensor Networks (WSN) consists of these sensor nodes containing simple processors, low power consuming antennas and various detectors. As the sensor networks do not require wired communication infrastructure, they can easily and inexpensively be formed with no harm to the environment. The sensor nodes have the ability to store and process data locally thanks to the software and hardware structure they have. The fact that they can get in contact with each other enables them to collaborate in performing complicated tasks along with exchanging information. As the communication means have low power consumption, it ensures that the life of the sensor nodes is long. The fact, too, that the nodes are programmable after being placed in the medium provides greater advantages. Because of the low cost and flexibility in use, the wireless sensor nodes are convenient to be used in many industrial and environmental applications. In this study, -how to develop smart environment, smart agriculture, smart grid with WSN-is analyzed.

Malware is a significant threat that has grown with the spread of technology. This makes detecting malware a critical issue. Static and dynamic methods are widely used in the detection of malware. However, traditional static and dynamic malware detection methods may fall short in advanced malware detection. Data obtained through memory analysis can provide important insights into the behavior and patterns of malware. This is because malwares leave various traces on memories. For this reason, the memory analysis method is one of the issues that should be studied in malware detection. In this study, the use of memory data in malware detection is suggested. Malware detection was carried out by using various deep learning and machine learning approaches in a big data environment with memory data. This study was carried out with Pyspark on Apache Spark big data platform in Google Colaboratory. Experiments were performed on the balanced CIC-MalMem-2022 dataset. Binary classification was made using Random Forest, Decision Tree, Gradient Boosted Tree, Logistic Regression, Naive Bayes, Linear Vector Support Machine, Multilayer Perceptron, Deep Feed Forward Neural Network, and Long Short-Term Memory algorithms. The performances of the algorithms used have been compared. The results were evaluated using the Accuracy, F1-score, Precision, Recall, and AUC performance metrics. As a result, the most successful malware detection was obtained with the Logistic Regression algorithm, with an accuracy level of 99.97% in malware detection by memory analysis. Gradient Boosted Tree follows the Logistic Regression algorithm with 99.94% accuracy. The Naive Bayes algorithm showed the lowest performance in malware analysis with memory data, with an accuracy of 98.41%. In addition, many of the algorithms used have achieved very successful results. According to the results obtained, the data obtained from memory analysis is very useful in detecting malware. In addition, deep learning and machine learning approaches were trained with memory datasets and achieved very successful results in malware detection.