Work

Novel Methods for Detecting Anomalies in Information Communication Systems

Year: 2025

Type: article

Abstract: As the complexity of ICS increases, security concerns also grow. Anomalies may indicate failures or cyberattacks, making early detection crucial. Traditional statistical and threshold-based methods are ineffective in dynamic ICS environments. ML- and DL-based methods, including support vector machines, autoencoders, and recurrent neural networks, improve accuracy and adaptability. However, they often require high computational resources. Transfer learning, federated learning, and XAI help overcome these limitations. This study analyzes and compares traditional and emerging approaches.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.15071145

Authors: Sherzod G'ulomov, Sodikjon Jumayev

Institutions: Tashkent University of Information Technology, Denau Institute of Entrepreneurship and Pedagogy

Language: English

Topic: Network Security and Intrusion Detection

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W6893322784

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