Authors:
Amine Hattak
1
;
2
;
Fabio Martinelli
1
;
Francesco Mercaldo
3
;
1
and
Antonella Santone
1
Affiliations:
1
Institute for Informatics and Telematics, National Research Council of Italy (CNR), Pisa, Italy
;
2
La Sapienza, University of Rome, Rome, Italy
;
3
University of Molise, Campobasso, Italy
Keyword(s):
Internet of Things, Network Traffic Classification, Deep Learning, Network Intrusion Detection, Security.
Abstract:
In an era marked by increasing reliance on digital technology, the security of interconnected devices and networks has become a paramount concern in the realm of information technology. Recognizing the pivotal role of network analysis in identifying and thwarting cyber threats, this paper delves into network security, specifically targeting the classification of network traffic using deep learning techniques within the Internet of Things (IoT) ecosystem. This paper introduces a deep learning-based approach tailored for traffic classification, beginning with raw traffic data in PCAP format. This data undergoes a transformation into visualized images, which serve as input for deep learning models designed to differentiate between benign and malicious activities. We evaluate the efficacy of our proposed method using the TON IoT dataset (Dr Nickolaos Koroniotis, 2021), comprising 10 network traces across two categories: nine related to diverse vulnerability scenarios and one associated w
ith a trusted application. Our results showcase an impressive accuracy of 99.1%, underscoring the potential of our approach in bolstering network security within IoT environments.
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