Проблемы защиты от ложной информации в компьютерных сетях

This paper provides an analysis of the present state in the field of protection against false information in computer networks and formulates current problems related to this protection. An approach to assessing protection activities on the basis of the Markov chain of the disinformation process is proposed. The architecture of a future system of data analysis is described. It implies enhanced methods of text trustworthiness analysis. The proposed complex approach, based on the known and suggested methods, enables detecting false information in computer networks promptly. Furthermore, the proposed method can be used for countering terrorist activities and cybercrimes in order to search for network resources which may be involved in unlawful activities.Анализируется текущее состояние в области защиты от ложной информации в компьютерных сетях и формулируются актуальные проблемы, связанные с этой защитой. Предлагается подход к оценке мероприятий защиты от такой информации на основе использования марковской модели дезинформирования. Раскрывается архитектура перспективной системы анализа информации в компьютерных сетях по требованиям достоверности. В рамках этой архитектуры рассматриваются усовершенствованные методы анализа достоверности текстов. Предлагается комплексный подход к использованию известных и предложенных методов для оперативного выявления ложной информации в компьютерных сетях. Кроме того, метод может применяться в области борьбы с киберпреступностью и терроризмом для поиска сетевых ресурсов и коммуникационных площадок, которые могут быть использованы для организации противоправной деятельности

Comparative Analysis of XGBoost and Minirocket Algortihms for Human Activity Recognition

Human Activity Recognition (HAR) has been extensively studied, with recent emphasis on the implementation of advanced Machine Learning (ML) and Deep Learning (DL) algorithms for accurate classification. This study investigates the efficacy of two ML algorithms, eXtreme Gradient Boosting (XGBoost) and MiniRocket, in the realm of HAR using data collected from smartphone sensors. The experiments are conducted on a dataset obtained from the UCI repository, comprising accelerometer and gyroscope signals captured from 30 volunteers performing various activities while wearing a smartphone. The dataset undergoes preprocessing, including noise filtering and feature extraction, before being utilized for training and testing the classifiers. Monte Carlo cross-validation is employed to evaluate the models' robustness. The findings reveal that both XGBoost and MiniRocket attain accuracy, F1 score, and AUC values as high as 0.99 in activity classification. XGBoost exhibits a slightly superior performance compared to MiniRocket. Notably, both algorithms surpass the performance of other ML and DL algorithms reported in the literature for HAR tasks. Additionally, the study compares the computational efficiency of the two algorithms, revealing XGBoost's advantage in terms of training time. Furthermore, the performance of MiniRocket, which achieves accuracy and F1 values of 0.94, and an AUC value of 0.96 using raw data and utilizing only one channel from the sensors, highlights the potential of directly leveraging unprocessed signals. It also suggests potential advantages that could be gained by utilizing sensor fusion or channel fusion techniques. Overall, this research sheds light on the effectiveness and computational characteristics of XGBoost and MiniRocket in HAR tasks, providing insights for future studies in activity recognition using smartphone sensor data.Comment: 12 pages, 1 figure, 1st Bilsel International Sur Scientific Researches Congress, 10-11 February, 2024, Diyarbakir, T\"urk\.Iy

Advanced system engineering approaches to dynamic modelling of human factors and system safety in sociotechnical systems

Sociotechnical systems (STSs) indicate complex operational processes composed of interactive and dependent social elements, organizational and human activities. This research work seeks to fill some important knowledge gaps in system safety performance and human factors analysis using in STSs. First, an in-depth critical analysis is conducted to explore state-of-the-art findings, needs, gaps, key challenges, and research opportunities in human reliability and factors analysis (HR&FA). Accordingly, a risk model is developed to capture the dynamic nature of different systems failures and integrated them into system safety barriers under uncertainty as per Safety-I paradigm. This is followed by proposing a novel dynamic human-factor risk model tailored for assessing system safety in STSs based on Safety-II concepts. This work is extended to further explore system safety using Performance Shaping Factors (PSFs) by proposing a systematic approach to identify PSFs and quantify their importance level and influence on the performance of sociotechnical systems’ functions. Finally, a systematic review is conducted to provide a holistic profile of HR&FA in complex STSs with a deep focus on revealing the contribution of artificial intelligence and expert systems over HR&FA in complex systems. The findings reveal that proposed models can effectively address critical challenges associated with system safety and human factors quantification. It also trues about uncertainty characterization using the proposed models. Furthermore, the proposed advanced probabilistic model can better model evolving dependencies among system safety performance factors. It revealed the critical safety investment factors among different sociotechnical elements and contributing factors. This helps to effectively allocate safety countermeasures to improve resilience and system safety performance. This research work would help better understand, analyze, and improve the system safety and human factors performance in complex sociotechnical systems