Passban IDS: An Intelligent Anomaly-Based Intrusion Detection System for IoT Edge Devices

By Mojtaba Eskandari, Z. H. Janjua, Massimo Vecchio, Fabio Antonelli. IEEE Internet of Things Journal, 2020

361 citations recorded by the source corpus.

Cyber-threat protection is today’s one of the most challenging research branches of information technology, while the exponentially increasing number of tiny, connected devices able to push personal data to the Internet is doing nothing but exacerbating the battle between the involved parties. Thus, this protection becomes crucial with a typical Internet-of-Things (IoT) setup, as it usually involves several IoT-based data sources interacting with the physical world within various application domains, such as agriculture, health care, home automation, critical industrial processes, etc. Unfortunately, contemporary IoT devices often offer very limited security features, laying themselves open to always new and more sophisticated attacks and also inhibiting the expected global adoption of IoT technologies, not to mention millions of IoT devices already deployed without any hardware security support. In this context, it is crucial to develop tools able to detect such cyber threats. In this article, we present Passban, an intelligent intrusion detection system (IDS) able to protect the IoT devices that are directly connected to it. The peculiarity of the proposed solution is that it can be deployed directly on very cheap IoT gateways (e.g., single-board PCs currently costing few tens of U.S. dollars), hence taking full advantage of the edge computing paradigm to detect cyber threats as close as possible to the corresponding data sources. We will demonstrate that Passban is able to detect various types of malicious traffic, including Port Scanning, HTTP and SSH Brute Force, and SYN Flood attacks with very low false positive rates and satisfactory accuracies.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/358a998f85c343cacfb695cae1d312de9fa8dcf2

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In Artificial Intelligence

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In Computer Networks and Communications

  1. Mohammad Ali Jabraeil Jamali
  2. Arash Heidari

In Signal Processing

  1. Mohammad Ali Jabraeil Jamali
  2. Arash Heidari

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