ANALYSIS OF NETWORK TRAFFIC THREATS ACROSS OSI MODEL LAYERS FOR DYNAMIC RTO CALCULATION IN THE CONTEXT OF COMBATING DDoS ATTACKS

Abstract

This document provides an examination of current threats to network security, viewed through the lens of network traffic analysis at various OSI model layers. It delves into the different forms of Distributed Denial of Service (DDoS) attacks and their ramifications on the Transmission Control Protocol (TCP), with a specific focus on a critical parameter - the Retransmission Timeout (RTO). The text also divulges fundamental algorithms and techniques for calculating RTO, encompassing adaptive methodologies that harness machine learning and artificial intelligence for optimizing the TCP/IP stack. In particular, it offers insights into the functioning of the RTO calculation algorithm, a pivotal element ensuring the reliability of data transmission via TCP. The document elaborates on how this algorithm dynamically adjusts the RTO value based on network conditions and measured Round Trip Time (RTT) values. Furthermore, it furnishes formulas for computing RTO with diverse parameters. Moreover, the document explores the potential of employing machine learning and data analysis methodologies to detect and preempt DDoS attacks. It elucidates how contemporary technologies empower the use of these approaches to minimize false positives in identifying malicious traffic packets, thereby enhancing the effectiveness of safeguarding information systems. Additionally, it provides an illustration of software and hardware tools employed for the practical implementation of these algorithms in devices facilitating data transmission via Ethernet connections. In summary, this work offers insights into contemporary challenges and issues in the realm of network security, especially in the context of the escalating frequency of DDoS attacks. This information proves valuable for students and professionals engaged in the study of network security and the development of measures to fortify networks and systems.