Analysis of the vulnerability of machine learning regression models to adversarial attacks using data from 5G wireless networks

This article describes the process of creating a script and conducting an analytical study of a dataset using the DeepMIMO emulator. An advertorial attack was carried out using the FGSM method to maximize the gradient. A comparison is made of the effectiveness of binary classifiers in the task of detecting distorted data. The dynamics of changes in the quality indicators of the regression model were analyzed in conditions without adversarial attacks, during an adversarial attack and when the distorted data was isolated. It is shown that an adversarial FGSM attack with gradient maximization leads to an increase in the value of the MSE metric by 33% and a decrease in the R2 indicator by 10% on average. The LightGBM binary classifier effectively identifies data with adversarial anomalies with 98% accuracy. Regression machine learning models are susceptible to adversarial attacks, but rapid analysis of network traffic and data transmitted over the network makes it possible to identify malicious activity

Investigating cybersecurity incidents using large language models in latest-generation wireless networks

The purpose of research: Detection of cybersecurity incidents and analysis of decision support and assessment of the effectiveness of measures to counter information security threats based on modern generative models. The methods of research: Emulation of signal propagation data in MIMO systems, synthesis of adversarial examples, execution of adversarial attacks on machine learning models, fine tuning of large language models for detecting adversarial attacks, explainability of decisions on detecting cybersecurity incidents based on the prompts technique. Scientific novelty: A binary classification of data poisoning attacks was performed using large language models, and the possibility of using large language models for investigating cybersecurity incidents in the latest generation wireless networks was investigated. The result of research: Fine-tuning of large language models was performed on the prepared data of the emulated wireless network segment. Six large language models were compared for detecting adversarial attacks, and the capabilities of explaining decisions made by a large language model were investigated. The Gemma-7b model showed the best results according to the metrics Precision = 0.89, Recall = 0.89 and F1-Score = 0.89. Based on various explainability prompts, the Gemma-7b model notes inconsistencies in the compromised data under study, performs feature importance analysis and provides various recommendations for mitigating the consequences of adversarial attacks. Large language models integrated with binary classifiers of network threats have significant potential for practical application in the field of cybersecurity incident investigation, decision support and assessing the effectiveness of measures to counter information security threats.

Algorithms for automatic intents extraction and utterances classification for goal-oriented dialogue systems

Modern machine learning techniques in the natural language processing domain can be used to automatically generate scripts for goal-oriented dialogue systems. The current article presents a general framework for studying the automatic generation of scripts for goal-oriented dialogue systems. A method for preprocessing dialog data sets in JSON format is described. A comparison is made of two methods for extracting user intent based on BERTopic and latent Dirichlet allocation. A comparison has been made of two implemented algorithms for classifying statements of users of a goal-oriented dialogue system based on logistic regression and BERT transformer models. The BERT transformer approach using the bert-base-uncased model showed better results for the three metrics Precision (0.80), F1-score (0.78) and Matthews correlation coefficient (0.74) in comparison with other methods.