Attacks based on malicious perturbations on image processing systems and defense methods against them

Systems implementing artificial intelligence technologies have become widespread due to their effectiveness in solving various applied tasks including computer vision. Image processing through neural networks is also used in securitycritical systems. At the same time, the use of artificial intelligence is associated with characteristic threats including disruption of machine learning models. The phenomenon of triggering an incorrect neural network response by introducing perturbations that are visually imperceptible to a person was first described and attracted the attention of researchers in 2013. Methods of attacks on neural networks based on malicious perturbations have been continuously improved, ways of disrupting the operation of neural networks in processing various types of data and tasks of the target model have been proposed. The threat of disrupting the functioning of neural networks through these attacks has become a significant problem for systems implementing artificial intelligence technologies. Thus, research in the field of countering attacks based on malicious perturbations is very relevant. This article describes current attacks, provides an overview and comparative analysis of such attacks on image processing systems based on artificial intelligence. Approaches to the classification of attacks based on malicious perturbations are formulated. Defense methods against such attacks are considered, their shortcomings are revealed. The limitations of the applied defense methods that reduce the effectiveness of counteraction to attacks are shown. Approaches and practical measures to detect and eliminate harmful disturbances are proposed

Darknet Traffic Analysis A Systematic Literature Review

The primary objective of an anonymity tool is to protect the anonymity of its users through the implementation of strong encryption and obfuscation techniques. As a result, it becomes very difficult to monitor and identify users activities on these networks. Moreover, such systems have strong defensive mechanisms to protect users against potential risks, including the extraction of traffic characteristics and website fingerprinting. However, the strong anonymity feature also functions as a refuge for those involved in illicit activities who aim to avoid being traced on the network. As a result, a substantial body of research has been undertaken to examine and classify encrypted traffic using machine learning techniques. This paper presents a comprehensive examination of the existing approaches utilized for the categorization of anonymous traffic as well as encrypted network traffic inside the darknet. Also, this paper presents a comprehensive analysis of methods of darknet traffic using machine learning techniques to monitor and identify the traffic attacks inside the darknet.Comment: 35 Pages, 13 Figure