IoT Vulnerability Data Crawling and Analysis

TL;DR

This paper explores crawling various web layers to analyze IoT vulnerabilities, aiming to identify attack trends and enable proactive defense strategies for the IoT ecosystem.

Contribution

It introduces a method for extracting IoT attack data from web sources to predict vulnerabilities and improve security measures.

Findings

It is possible to identify IoT attack trends through web crawling.

Proactive measures can be developed based on trend analysis.

The approach enhances early detection of IoT security threats.

Abstract

Internet of Things (IoT) is a whole new ecosystem comprised of heterogeneous connected devices -i.e. computers, laptops, smart-phones and tablets as well as embedded devices and sensors-that communicate to deliver capabilities making our living, cities, transport, energy, and many other areas more intelligent. The main concerns raised from the IoT ecosystem are the devices poor support for patching/updating and the poor on-board computational power. A number of issues stem from this: inherent vulnerabilities and the inability to detect and defend against external attacks. Also, due to the nature of their operation, the devices tend to be rather open to communication, which makes attacks easy to spread once reaching a network. The aim of this research is to investigate if it is possible to extract useful results regarding attacks' trends and be able to predict them, before it is too late, by crawling Deep/Dark and Surface web. The results of this work show that is possible to find the trend and be able to act proactively in order to protect the IoT ecosystem.