Determination of Multipath Security Using Efficient Pattern Matching

TL;DR

This paper proposes an efficient pattern matching method to assess multipath security by analyzing attack signatures and threat levels, enhancing secure data transmission in networks.

Contribution

It introduces a novel approach combining compressed sensing and machine learning to evaluate and improve security levels across multiple network paths.

Findings

Effective detection of attack signatures along network paths

Enhanced security assessment accuracy using machine learning

Improved secure multipath routing strategies

Abstract

Multipath routing is the use of multiple potential paths through a network in order to enhance fault tolerance, optimize bandwidth use, and improve security. Selecting data flow paths based on cost addresses performance issues but ignores security threats. Attackers can disrupt the data flows by attacking the links along the paths. Denial-of-service, remote exploitation, and other such attacks launched on any single link can severely limit throughput. Networks can be secured using a secure quality of service approach in which a sender disperses data along multiple secure paths. In this secure multi-path approach, a portion of the data from the sender is transmitted over each path and the receiver assembles the data fragments that arrive. One of the largest challenges in secure multipath routing is determining the security threat level along each path and providing a commensurate level of encryption along that path. The research presented explores the effects of real-world attack scenarios in systems, and gauges the threat levels along each path. Optimal sampling and compression of network data is provided via compressed sensing. The probability of the presence of specific attack signatures along a network path is determined using machine learning techniques. Using these probabilities, information assurance levels are derived such that security measures along vulnerable paths are increased.