The article analyzes various approaches to the generation and detection of audio deepfakes. Particular attention is paid to the preprocessing of acoustic signals, extraction of voice signal parameters, and data classification. The study examines three groups of classifiers: Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and neural networks. For each group, effective methods were identified, and the most successful approaches were determined based on a comprehensive analysis. The study revealed two approaches demonstrating high accuracy and reliability: a detector based on temporal convolutional networks analyzing MFCC-cepstrogram achieved an EER metric of 0.07%, while the Support Vector Machine with a radial basis function kernel reached an EER of 0.5%. Additionally, the latter method demonstrated the following metrics on the ASVspoof 2021 dataset: Accuracy = 99.6%, F1-score = 0.997, Precision = 0.998, and Recall = 0.994.
Keywords: audio deepfakes, preprocessing of acoustic signals, support vector machine, k-nearest neighbors, neural networks, temporal convolutional networks, deepfake detection
The purpose of this work is to analyze the concept of the threat of ransomware, methods of their detection, as well as to consider methods of intelligent analysis in solving the problem of detection, which are a popular tool among researchers of ransomware and malicious software (malware) in general. Data mining helps to improve the accuracy and speed up the malware detection process by processing large amounts of information. Specialists can identify new, previously unknown malware. And with the help of generative adversarial networks, zero-day malware can be detected. Despite the fact that a direct and objective comparison of all the studies presented in the work is impossible, due to different data sets, it can be assumed that using the architecture of generative-adversarial networks is the most promising way to solve the problem of detection.
Keywords: malware, ransomware, intelligent analysis, machine learning, neural network, generative adversarial network