February 2025
New approach for enhancement of critical entities resilience against terrorist threats
2025, vol.17 , no.1, pp. 125-136
Article [2025-01-12]
In critical infrastructure security research, multiple solutions are proposed based on different approaches used. An attempt will be made here to present an integrated new approach in cases where organizations take actions to increase the entities resilience. The new approach is mainly based on the existing standardization base that affects the relevant elements of security systems. The main results of the research are the proposed resilience levels that are the main elements of the structure of the business organization. The identification of resilience levels is the basis of the assessment of the degree of trust in the organization which the security systems form in the users of its services, as well as in the society.
Kiril Stoichev. New approach for enhancement of critical entities resilience against terrorist threats. International Journal on Information Technologies and Security, vol.17 , no.1, 2025, pp. 125-136.
The role of dual-use technologies in hybrid warfare: A double-edged sword?
2025, vol.17 , no.1, pp. 112-124
Article [2025-01-11]
The concepts of "hybrid warfare" and "dual-use technologies" have two striking similarities. Both link the military with the civilian domains; both are also heavily used in Western security architecture. It is thus surprising that very little academic literature explores their interrelationship. To fill this gap, the present article asks how dual-use technologies influence hybrid warfare. It is argued that dual-use technologies are a double-edged sword as they enable hybrid aggressors but also help counter hybrid warfare. On the one hand, the versatility, lower acquisition costs, and adaptability of dual-use technologies increase the risk of hybrid campaigns. On the other, they can improve allied interoperability and resilience against hybrid threats. It is thus recommended that researchers, analysts and decision-makers consider dual-use technologies when investigating or mounting a defence against hybrid adversaries.
Borislav Bankov , Stefan Bojilov . The role of dual-use technologies in hybrid warfare: A double-edged sword?. International Journal on Information Technologies and Security, vol.17 , no.1, 2025, pp. 112-124.
Resource analysis of methods for assessing the dependability of power supply systems in conditions of negative impacts
2025, vol.17 , no.1, pp. 103-112
Article [2025-01-10]
The article aims to analyse existing methods for assessing the reliability and dependability of power supply systems in the event of partial or complete failure of such systems. The basis for the analysis is the resources that must be spent to perform dependability assessments, such as the complexity of calculations, the amount of initial data required to assess the reliability of complex electrical systems and time costs. Resource analysis was performed for the deterministic methods of system modelling, the Markov analysis method, and the Monte Carlo method. Based on the analysis, a conclusion was drawn about the feasibility of using Markov analysis for assessing the dependability of complex power supply systems. A list of possible events from external negative impacts on power supply systems' generating and distribution capacities was developed and substantiated for use in Markov analysis.
Dmitry Maevsky, Elena Maevskaya, Viktoriia Kryvda, Dmytro Stetsiuk. Resource analysis of methods for assessing the dependability of power supply systems in conditions of negative impacts. International Journal on Information Technologies and Security, vol.17 , no.1, 2025, pp. 103-112.
Application of algorithms for forecasting and optimizing the production capacity of wind energy
2025, vol.17 , no.1, pp. 91-102
Article [2025-01-09]
With advances in technology, particularly where environmental implications are a concern, equipment has become increasingly complex and metered. Human activities like emissions and urbanization significantly impact the atmosphere and wind energy generation. This study predicts wind energy generation capacity for 2025–2029 based on data from 2000–2024. Predictive models (e.g., Long Short-Term Memory (LSTM), Facebook Prophet, and SARIMA) are applied to analyze energy trends. Performance results show LSTM provides the best accuracy. The findings enhance reliable wind energy forecasting and improved energy management methods in response to shifting climate dynamics.
Naim Baftiu, Tatjana Atasanova-Pachemska. Application of algorithms for forecasting and optimizing the production capacity of wind energy. International Journal on Information Technologies and Security, vol.17 , no.1, 2025, pp. 91-102.
MOD-XGBOOST: AN ADAPTIVE MACHINE LEARNING MODEL FOR INTERNET OF THINGS ENVIRONMENT TO DETECT SPOOFING AND DOS ATTACKS
2025, vol.17 , no.1, pp. 79-90
Article [2025-01-08]
The rising number of Internet of Things (IoT) devices necessitates robust security to counter threats like Denial-of-Service (DoS) and spoofing attacks. Many Intrusion Detection Systems (IDS) leverage Machine Learning (ML), but challenges like concept drift and class imbalance persist. This study presents an adaptive ML-based IDS framework using a Modified XGBoost (Mod-XGB) model. Mod-XGB incorporates a weighted loss function for class imbalance, adaptive instance weighting for concept drift, and a security penalty term to enhance feature selection. Evaluated on the CICIoV2024 dataset, Mod-XGB achieved 97.96% accuracy, outperforming models like Random Forest, Logistic Regression, and AdaBoost. This innovative approach effectively addresses IoT security challenges, offering significant advancements in IDS performance for improved attack detection and resilience in dynamic environments.
Sujit N. Deshpande, Deipali Vikram Gore, Amrapali Shivajirao Chavan, Arundhati Nelli. MOD-XGBOOST: AN ADAPTIVE MACHINE LEARNING MODEL FOR INTERNET OF THINGS ENVIRONMENT TO DETECT SPOOFING AND DOS ATTACKS. International Journal on Information Technologies and Security, vol.17 , no.1, 2025, pp. 79-90.
DNA-based cryptosystem integrating Wichmann-Hill and mixed congruence algorithms for enhanced data security
2025, vol.17 , no.1, pp. 69-78
Article [2025-01-07]
This paper proposes a DNA-based cryptosystem integrating the Wichmann-Hill Algorithm (WHA) and Mixed Congruence Algorithm (MCA) to enhance encryption security. By employing WHA and MCA as hybrid pseudo-random number generators (PRNGs), the system dynamically generates encryption keys, improving randomness and attack resistance. Binary data is encoded into DNA sequences and encrypted using complementary rules, XOR, and permutations. The combined entropy from WHA and MCA ensures highly random outputs, validated by NIST tests, with a key size of ~76 bits for brute-force resistance. Performance evaluations confirm real-time efficiency, while multi-layered encryption enhances cryptanalytic defence. This framework demonstrates the potential of DNA-based cryptography integrated with advanced PRNGs to address modern security challenges and enhance bio-inspired encryption methodologies.
Ahmed S. Alshammari. DNA-based cryptosystem integrating Wichmann-Hill and mixed congruence algorithms for enhanced data security. International Journal on Information Technologies and Security, vol.17 , no.1, 2025, pp. 69-78.
Whale optimization algorithm for production scheduling problems in small bakery enterprises
2025, vol.17 , no.1, pp. 57-68
Article [2025-01-06]
The no-wait flexible flowshop scheduling problem (NWFFSP) is a significant challenge in industries such as bakeries, where strict no-wait constraints and resource limitations, like oven availability, complicate scheduling. This research aims to evaluate the performance of the Whale Optimization Algorithm (WOA) in solving NWFFSP as a multi-objective problem, focusing on minimizing makespan and total oven idle time (ToIT). The study employs the Pareto principle to address trade-offs between these conflicting objectives. Experimental results demonstrate that WOA effectively balances both objectives, maintaining comparable efficiency to mono-objective optimization. The findings confirm WOA's potential as a robust tool for solving complex multi-objective scheduling problems in practical settings.
Cecilia E. Nugraheni, Luciana Abednego, Maria Widyarini. Whale optimization algorithm for production scheduling problems in small bakery enterprises. International Journal on Information Technologies and Security, vol.17 , no.1, 2025, pp. 57-68.
Machine generated tools for short-term forecasting of price of electrical energy
2025, vol.17 , no.1, pp. 47-56
Article [2025-01-05]
In the contemporary context of energy markets, short-term electricity price forecasting plays a critical role for the efficient management of resources and optimization of energy systems. This paper explores the application of artificial intelligence and machine learning as tools for generating accurate short-term electricity price forecasts. We consider different algorithms, including neural networks, support vector machine learning, and time series, and analyze their accuracy and effectiveness in different real-world market conditions. The aim of the study is to provide a scientifically based view on the potential of machine-generated tools to improve the accuracy and reliability of short-term energy forecasts, which can contribute to greater stability and optimization of energy markets.
Plamen Stanchev, Gergana Vacheva, Dardan Klimenta, Nikolay Hinov. Machine generated tools for short-term forecasting of price of electrical energy. International Journal on Information Technologies and Security, vol.17 , no.1, 2025, pp. 47-56.
Predictive modeling of student success using machine learning
2025, vol.17 , no.1, pp. 37-45
Article [2025-01-04]
This paper presents a comprehensive study of predictive models for student success, leveraging machine learning techniques to analyze diverse factors affecting academic performance in higher education. Using a dataset of students from various institutions in Kosovo, encompassing demographic, socioeconomic, and academic attributes, the research employs and evaluates several machine learning algorithms to predict the dropout rate. To address the issue of unbalanced data, SMOTE and ADASYN techniques were implemented, enhancing the predictive accuracy. The study identifies significant predictors of student success, and the results indicate that machine learning can accurately predict student outcomes, providing educational institutions with actionable insights to mitigate dropout rates and enhance student support.
Arbër H. Hoti, Xhemal Zenuni, Jaumin Ajdari, Florije Ismaili. Predictive modeling of student success using machine learning. International Journal on Information Technologies and Security, vol.17 , no.1, 2025, pp. 37-45.
Development of AIOT-based health kiosks for patient classification in e-health
2025, vol.17 , no.1, pp. 25-34
Article [2025-01-03]
This study aimed to develop health kiosks that collect demographic data, body indices, and vital signs of patients. Additionally, this paper proposed the integration of Artificial Intelligence (AI) to predict and classify patients based on various respiratory infectious diseases, using the data collected by the device. Furthermore, the paper discussed formatting the device's output packages according to HL-7 standards to facilitate seamless integration with hospital management system (HMS). It also proposed a solution for deploying these devices on e-Health platforms utilizing Internet of Things (IoT). During routine operations, the kiosks collect demographic data, body indices, and vital signs from patients for medical examination and treatment. In the event of an epidemic, these kiosks can be utilized to detect and manage positive cases, thereby contributing to the reduction of the spread of infectious respiratory diseases.
Pham Trung Kien . Development of AIOT-based health kiosks for patient classification in e-health. International Journal on Information Technologies and Security, vol.17 , no.1, 2025, pp. 25-34.