Vesper: Using Echo-Analysis to Detect Man-in-the-Middle Attacks in LANs

TL;DR

Vesper is a portable, high-accuracy MitM detection system for LANs that uses echo-analysis inspired by acoustic signal processing, leveraging neural autoencoders to identify environment changes with minimal overhead.

Contribution

Vesper introduces a novel echo-analysis technique combined with neural autoencoders for effective MitM attack detection in LANs, addressing portability and false positive issues.

Findings

High detection accuracy for MitM attacks

Minimal network overhead during operation

Effective against various adversarial attacks

Abstract

The Man-in-the-Middle (MitM) attack is a cyber-attack in which an attacker intercepts traffic, thus harming the confidentiality, integrity, and availability of the network. It remains a popular attack vector due to its simplicity. However, existing solutions are either not portable, suffer from a high false positive rate, or are simply not generic. In this paper, we propose Vesper: a novel plug-and-play MitM detector for local area networks. Vesper uses a technique inspired from impulse response analysis used in the domain of acoustic signal processing. Analogous to how echoes in a cave capture the shape and construction of the environment, so to can a short and intense pulse of ICMP echo requests model the link between two network hosts. Vesper uses neural networks called autoencoders to model the normal patterns of the echoed pulses, and detect when the environment changes. Using this technique, Vesper is able to detect MitM attacks with high accuracy while incurring minimal network overhead. We evaluate Vesper on LANs consisting of video surveillance cameras, servers, and PC workstations. We also investigate several possible adversarial attacks against Vesper, and demonstrate how Vesper mitigates these attacks.