DFIR Stream 0x2
Channel: Assoc. of Cyber Forensics & Threat investigators
Date: 01/03/2024

"Securing the IoT: Harnessing Deep Learning for Robust and Interpretable Intrusion Detection" by  Dr. Antonio Montieri, University of Naples Federico II, and Dr. Domenico Ciuonzo, University of Naples Federico II.

Abstract: In recent years, IoT traffic proliferation has been exponential and is poised for further escalation. With IoT devices being vulnerable targets for cyber-attacks, there is an urgent need for advanced and robust security tools in this vertical. Deep Learning (DL) methods show promise for developing Network Intrusion Detection Systems (NIDSs) tailored to this
challenge.
However, traditional DL model training and evaluation relies on data from a single organization's network, limiting real-world performance assessment. Ensuring DL models work across diverse network environments is crucial.
This lecture explores crafting effective, reliable, and resilient attack traffic classifiers using DL within IoT networks. We delve into intricate design considerations encompassing various single-modal and multimodal DL approaches, fueled by meticulously curated and unbiased input data tailored explicitly for IoT attack classification, facilitating early traffic classification.
We also examine the interpretability of these methods, evaluating the impact of network layer fields on performance and potential biases. Additionally, we introduce a cross-evaluation methodology to assess models' ability to distinguish malicious from benign traffic across varied networks. We explore the influence of informative traffic fields on DL model generalization.
Furthermore, the lecture provides a practical segment guiding participants in designing, implementing, and evaluating NIDSs using Python frameworks.

More Info: https://www.acfti.org/news-events/dfir-stream-0x2