Anomaly detection in IOT edge computing using deep learning and instance-level horizontal reduction.

Autor:	Abbasi, Negar, Soltanaghaei, Mohammadreza, Zamani Boroujeni, Farsad
Předmět:	DEEP learning ANOMALY detection (Computer security) EDGE computing RECURRENT neural networks TRAFFIC flow INTERNET of things
Zdroj:	Journal of Supercomputing; May2024, Vol. 80 Issue 7, p8988-9018, 31p
Abstrakt:	The increasing number of network attacks has led to the development of intrusion detection systems. However, these methods often face limitations such as high traffic flow data dimensions, which can reduce attack detection rates and noise sensitivity, affecting anomaly detection performance. This paper introduces a new model based on recurrent deep learning and instance-level horizontal reduction to detect anomalies and network attacks. The model uses nested sliding windows, which move with a specific step in the data and generate a different number of histogram outputs based on the type of anomaly in the data. Evaluation results on five databases show that the proposed model achieves a high accuracy of 99% in detecting different attacks, demonstrating the success of this new approach combined with deep recurrent neural networks in detecting anomalies. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
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