This paper focuses on training machine learning models using the XGBoost and extremely randomized trees algorithms on two datasets obtained using static and dynamic analysis of real malicious and benign samples. We then compare their success rates—both mutually and with other algorithms, such as the random forest, the decision tree, the support vector machine, and the naïve Bayes algorithms, which we compared in our previous work on the same datasets. The best performing classification models, using the XGBoost algorithm, achieved 91.9% detection accuracy and 98.2% sensitivity, 0.853 AUC, and 0.949 F1 score on the static analysis dataset, and 96.4% accuracy and 98.5% sensitivity, 0.940 AUC, and 0.977 F1 score on the dynamic analysis dataset. Then, we exported the best performing machine learning models and used them in our proposed MLMD program, automating the process of static and dynamic analysis and allowing the trained models to be used for classification on new samples.

This paper focuses on the implementation of nfstream, an open source network data analysis tool and machine learning model using the TensorFlow library for HTTP attack detection. HTTP attacks are common and pose a significant security threat to networked systems. In this paper, we propose a machine learning-based approach to detect the aforementioned attacks, by exploiting the machine learning capabilities of TensorFlow. We also focused on the collection and analysis of network traffic data using nfstream, which provides a detailed analysis of network traffic flows. We pre-processed and transformed the collected data into vectors, which were used to train the machine learning model using the TensorFlow library. The proposed model using nfstream and TensorFlow is effective in detecting HTTP attacks. The machine learning model achieved high accuracy on the tested dataset, demonstrating its ability to correctly identify HTTP attacks while minimizing false positives.

Information security is one of the priorities in the context of communication technologies. The work is focused on intrusion detection in high-speed computer networks. Request-transition diagrams method describing the time-dependent phenomena provides an effective method to announce passages preceding the existence of disturbance. Modified hybrid detection reduces the responsiveness of the system and method provides real-time tracking. In a correct state of the system is used only one method of detection, which is provided in real-time. Definition of indefinite field brings extension to dual detection methods to ensure accuracy and recovery of erroneous detections. Distributed detection methods based on descriptive rules accelerate the detection process.

Stock price prediction is a challenging and dynamic task due to the high volatility and complexity of financial markets. In this study, we present a comparative analysis of several machine learning algorithms applied to the prediction of stock prices. The models explored include Gradient Boosting Regressor (GBR), Gated Recurrent Unit (GRU), Linear Regression (LR), Long Short-Term Memory (LSTM), Random Forest (RF), and Support Vector Regression (SVR). Using a dataset comprising historical stock prices from multiple companies, we evaluate the performance of each model based on accuracy and predictive stability. Daily predictions were generated for each model, and the results were assessed using metrics such as average deviation from real price. The analysis indicates that while deep learning models like GRU and LSTM exhibit a strong capacity to capture temporal patterns, ensemble methods such as GBR and RF also provide competitive accuracy for short-term forecasts. This research highlights the strengths and limitations of various approaches to stock price forecasting and offers insights into their potential applications for investment decision-making. Future work may involve extending this analysis to broader financial datasets and investigating the integration of additional market factors.

The most used asymmetric encryption algorithm nowadays is RSA. It may become insecure regarding advances in the field of quantum computers. It is the reason why the National Institute of Standards and Technologies introduces the challenges of choosing a new post-quantum encryption standard. Initially, NIST received 82 submissions comprising key encapsulation mechanisms and digital signature schemes. However, only 69 of them were formally accepted after an initial review. In July 2022, NIST selected some algorithms for standardization. For the key encapsulation mechanism, Kyber was selected, and for digital signatures, Dilithium Falcon and SPHINCS+. After the third round concluded, NIST indicated it would continue to evaluate some of the alternative algorithms that were not selected as finalists in the third round. This ongoing evaluation is informally referred to as a "fourth round." Initially, there were four participants - BIKE, Classic McEliece, SIKE, and HQC. However, the SIKE downfall with the Castryck-Decru attack was introduced in July 2022, and the HQC algorithm was chosen for standardization in March 2025. In our research, we examine all functions of BIKE, Classic McEliece, and the HQC from the point of view of time and memory consumption. The results obtained will help us during the implementation of the BIKE algorithm on ESP32.