Intrusion detection with machine learning: a two-step federated approach.using the CIC IoT 2023 dataset

• Autorzy: Jakotiya Komal , Shirsath Vishal , Inamdar SharanBasava

Identyfikatory

DOI

10.7494/csci.2025.26.2.6450

• Języki publikacji: EN

Abstrakty

EN

The main objective of the planned effort is to provide analytical analyses of current intrusion detection systems grounded on ML algorithms. Furthermore, examined in this work are the useful data sets and several techniques already in use to develop an effective IDS using single, hybrid, and ensemble machine learning algorithms. The approaches in the literature have then been investi-gated under several criteria to provide a clear road and direction for the next projects that will be successful. Nowadays, companies of all kinds include an intrusion detection system (IDS), which inhibits cybercrime to protect the network, resources, and private data. Many strategies have been suggested and implemented up till now to prevent uncivil behaviour. Since machine learning (ML) approaches are successful, the proposed approach applied several ML models for the intrusion detection system. The CIC IoT 2023 Dataset is the one applied in this paper, and a two-step process for Intrusion detection was proposed. Tested with several techniques including random forest, XGBoost, logistic regression, MLP model, and RNN. Following fine-tuning, the federated learning model using neural networks had the best accuracy—99.84%.

Słowa kluczowe

EN

LSTM; MLP; XG Boost; federated learning; logistic regression; RNN

• Wydawca: Wydawnictwa AGH
• Czasopismo: Computer Science
• Rocznik: 2025
• Tom: T. 26 (2)
• Strony: 77--95
• Opis fizyczny: Bibliogr. 29 poz., rys., tab., wykr.

Twórcy

autor

Jakotiya Komal

• School of Engineering, ADYPU, Pune India

autor

Shirsath Vishal

• School of Engineering, ADYPU, Pune India

autor

Inamdar SharanBasava

• School of Engineering, ADYPU, Pune India

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).