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A Two-Stage Agent-based Framework for Network Attack Detection And Categorization in IoT

Nguyễn Huy Trung

This paper proposes a two-stage, lightweight agent-based framework for detecting and classifying network attacks on resource-constrained IoT devices. The system collects both system-level and network-level data directly from IoT devices and applies Transformer-based deep learning models for anomaly detection and attack classification. Experimental results demonstrate high accuracy in detecting common attacks such as DDoS, port scanning, brute-force, and malware, highlighting the effectiveness and practical applicability of the proposed approach for IoT security.

Xuất bản trên:

A Two-Stage Agent-based Framework for Network Attack Detection And Categorization in IoT

Ngày đăng:

DOI:

Nhà xuất bản:

2025 the 5th International Conference on Information Communication and Software Engineering (ICICSE 2025)

Địa điểm:

Từ khoá:

IoT Security; Network Attack Detection; Lightweight Agent; Anomaly Detection

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