Influence of Autoencoder Latent Space on Classifying IoT CoAP Attacks

TL;DR

This paper investigates how the latent space of autoencoders can enhance IoT CoAP attack detection, achieving high precision with minimal features in a resource-constrained environment.

Contribution

It introduces a novel approach combining autoencoder latent space with classification techniques for efficient IoT attack detection on CoAP.

Findings

Over 99% precision in attack detection

Effective data reduction with only 2 features

Autoencoder-based models outperform traditional methods

Abstract

The Internet of Things (IoT) presents a unique cybersecurity challenge due to its vast network of interconnected, resource-constrained devices. These vulnerabilities not only threaten data integrity but also the overall functionality of IoT systems. This study addresses these challenges by exploring efficient data reduction techniques within a model-based intrusion detection system (IDS) for IoT environments. Specifically, the study explores the efficacy of an autoencoder's latent space combined with three different classification techniques. Utilizing a validated IoT dataset, particularly focusing on the Constrained Application Protocol (CoAP), the study seeks to develop a robust model capable of identifying security breaches targeting this protocol. The research culminates in a comprehensive evaluation, presenting encouraging results that demonstrate the effectiveness of the proposed methodologies in strengthening IoT cybersecurity with more than a 99% of precision using only 2 learned features.