• International Journal of Computer Science & Network Security
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• v.23 no.2
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• pp.193-198
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• 2023

The article focuses on the areas of education activities in using techniques for teaching and learning with information and communication technologies (ICTs), researching and analyzing the available ICTs, gearing the technologies to the specific psychological and pedagogical conditions, independently building and modeling ICTs, enlarging and developing their use in the learning environment. The visualization of scientific research has been determined to be part of the educational support for building students' ICT competence during teaching and learning and is essential to the methodology culture. There have been specified main tasks for pedagogy technologies (PTs) to develop the skills of adaptability to the global digital space in students, their effective database operation and using the data bases as necessary elements for learning and as part of professional training for research. We provided rationalization for implementing the latest ICTs into the Ukrainian universities' curricula, as well as creating modern methods for using the technologies in the learning / teaching process and scientific activities.

• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.229-238
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• 2022

Due to digitization, data has become a tsunami in almost every data-driven business sector. The information wave has been greatly boosted by man-to-machine (M2M) digital data management. An explosion in the use of ICT for farm management has pushed technical solutions into rural areas and benefited farmers and customers alike. This study discusses the benefits and possible pitfalls of using information and communication technology (ICT) in conventional farming. Information technology (IT), the Internet of Things (IoT), and robotics are discussed, along with the roles of Machine learning (ML), Artificial intelligence (AI), and sensors in farming. Drones are also being studied for crop surveillance and yield optimization management. Global and state-of-the-art Internet of Things (IoT) agricultural platforms are emphasized when relevant. This article analyse the most current publications pertaining to precision agriculture using ML and AI techniques. This study further details about current and future developments in AI and identify existing and prospective research concerns in AI for agriculture based on this thorough extensive literature evaluation.

• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.10-16
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• 2023

The ever-increasing amount of data generated by various industries and systems has led to the development of data mining techniques as a means to extract valuable insights and knowledge from such data. The electrical energy industry is no exception, with the large amounts of data generated by SCADA systems. This study focuses on the analysis of historical data recorded in the SCADA database of the Libyan Electricity Company. The database, spanned from January 1st, 2013, to December 31st, 2022, contains records of daily date and hour, energy production, temperature, humidity, wind speed, and energy consumption levels. The data was pre-processed and analyzed using the WEKA tool and the Apriori algorithm, a supervised machine learning technique. The aim of the study was to extract association rules that would assist decision-makers in making informed decisions with greater efficiency and reduced costs. The results obtained from the study were evaluated in terms of accuracy and production time, and the conclusion of the study shows that the results are promising and encouraging for future use in the Libyan Electricity Company. The study highlights the importance of data mining and the benefits of utilizing machine learning technology in decision-making processes.

• Journal of IKEEE
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• v.27 no.1
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• pp.93-102
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• 2023

As one of the techniques for analyzing malicious code, techniques creating a sequence or a graph of function call relationships in an executable program and then analyzing the result are proposed. Such methods generally study function calling in the executable program code through static analysis and organize function call relationships into a sequence or a graph. However, in the case of an obfuscated executable program, it is difficult to analyze the function call relationship only with static analysis because the structure/content of the executable program file is different from the standard structure/content. In this paper, we propose a dynamic analysis method to analyze the function call relationship of an obfuscated execution program. We suggest constructing a function call relationship as a graph using the proposed technique.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1657-1673
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• 2023

With the advancement of information technology, criminals employ multiple cyberspaces to promote cybercrime. To combat cybercrime and cyber dangers, banks and financial institutions use artificial intelligence (AI). AI technologies assist the banking sector to develop and grow in many ways. Transparency and explanation of AI's ability are required to preserve trust. Deep learning protects client behavior and interest data. Deep learning techniques may anticipate cyber-attack behavior, allowing for secure banking transactions. This proposed approach is based on a user-centric design that safeguards people's private data over banking. Here, initially, the attack data can be generated over banking transactions. Routing is done for the configuration of the nodes. Then, the obtained data can be preprocessed for removing the errors. Followed by hierarchical network feature extraction can be used to identify the abnormal features related to the attack. Finally, the user data can be protected and the malicious attack in the transmission route can be identified by using the Wrapper stepwise ResNet classifier. The proposed work outperforms other techniques in terms of attack detection and accuracy, and the findings are depicted in the graphical format by employing the Python tool.

• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.89-96
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• 2023

Intrusion detection has been widely studied in both industry and academia, but cybersecurity analysts always want more accuracy and global threat analysis to secure their systems in cyberspace. Big data represent the great challenge of intrusion detection systems, making it hard to monitor and analyze this large volume of data using traditional techniques. Recently, deep learning has been emerged as a new approach which enables the use of Big Data with a low training time and high accuracy rate. In this paper, we propose an approach of an IDS based on cloud computing and the integration of big data and deep learning techniques to detect different attacks as early as possible. To demonstrate the efficacy of this system, we implement the proposed system within Microsoft Azure Cloud, as it provides both processing power and storage capabilities, using a convolutional neural network (CNN-IDS) with the distributed computing environment Apache Spark, integrated with Keras Deep Learning Library. We study the performance of the model in two categories of classification (binary and multiclass) using CSE-CIC-IDS2018 dataset. Our system showed a great performance due to the integration of deep learning technique and Apache Spark engine.

• International Journal of Computer Science & Network Security
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• v.23 no.12
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• pp.204-212
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• 2023

Diabetes is a condition that can be brought on by a variety of different factors, some of which include, but are not limited to, the following: age, a lack of physical activity, a sedentary lifestyle, a family history of diabetes, high blood pressure, depression and stress, inappropriate eating habits, and so on. Diabetes is a disorder that can be brought on by a number of different factors. A chronic disorder that may lead to a wide range of complications. Diabetes mellitus is synonymous with diabetes. There is a correlation between diabetes and an increased chance of having a variety of various ailments, some of which include, but are not limited to, cardiovascular disease, nerve damage, and eye difficulties. There are a number of illnesses that are connected to kidney dysfunction, including stroke. According to the figures provided by the International Diabetes Federation, there are more than 382 million people all over the world who are afflicted with diabetes. This number will have risen during the years in order to reach 592 million by the year 2035. There are a substantial number of people who become victims on a regular basis, and a significant percentage of those people are uninformed of whether or not they have it. The individuals who are most adversely impacted by it are those who are between the ages of 25 and 74 years old. This paper reviews about various machine learning techniques used to detect diabetes mellitus.

• Advances in concrete construction
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• v.17 no.2
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• pp.53-66
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• 2024

Domain Name Systems (DNS) provide critical performance in directing Internet traffic. It is a significant duty of DNS service providers to protect DNS servers from bandwidth attacks. Data mining techniques may identify different trends in detecting anomalies, but these approaches are insufficient to provide adequate methods for querying traffic data in significant network environments. The patterns can enable the providers of DNS services to find anomalies. Accordingly, this research has used a new approach to find the anomalies using the Neural Network (NN) because intrusion detection techniques or conventional rule-based anomaly are insufficient to detect general DNS anomalies using multi-enterprise network traffic data obtained from network traffic data (from different organizations). NN was developed, and its results were measured to determine the best performance in anomaly detection using DNS query data. Going through the R² results, it was found that NN could satisfactorily perform the DNS anomaly detection process. Based on the results, the security weaknesses and problems related to unpredictable matters could be practically distinguished, and many could be avoided in advance. Based on the R² results, the NN could perform remarkably well in general DNS anomaly detection processing in this study.

• Journal of Internet of Things and Convergence
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• v.6 no.1
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• pp.39-43
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• 2020

The Internet of Things plays a role as an important element technology of the 4th Industrial Revolution. This study is currently developing intelligent cars with IT technology, and is at a time when the development of intelligent cars is active and network data communication is possible. However, security solutions are needed as security is still at a weak stage, which can be threatened by intrusions into the network from outside. In this paper, in order to improve security of intelligent cars without causing security problems, we will apply blockchain technology, propose biometric authentication techniques using users' biometric information, and continue to study them in the future.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.6
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• pp.1327-1339
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• 2015

This paper surveys structures, features and programming techniques of CNG that is substitution of CAPI in Microsoft, and implements hash provider for support HAS-160 that is one of the Korean hash algorithm. After that, we analysis agility from different perspective based on implemented results, and propose customizing stratagem. Analyzed results of basic concepts and implemented HAS-160 hash provider are expected applying measure for Korean cryptography algorithm in Vista environment. Consequently, we will research secure distribution way due to it is not apply on CNG.