کنفرانس مهندسی برق ایران

صفحه اصلی / سی و سومین کنفرانس بین المللی مهندسی برق

Intrusion Detection System for Securing Agriculture 4.0 against DDoS Attacks using Deep Learning and Machine Learning Models

نویسندگان :

Mohammad Mirmarghabi¹ Ahmad Afshar² Hajar Atriyanfar³

1- دانشگاه صنعتی امیرکبیر(پلی تکنیک تهران) 2- دانشگاه صنعتی امیرکبیر(پلی تکنیک تهران) 3- دانشگاه صنعتی امیرکبیر(پلی تکنیک تهران)

کلمات کلیدی :

Deep learning approaches،Agriculture 4.0،Intrusion Detection System،DDoS attack،Machine Learning

چکیده :

This paper focuses on smart agriculture, leveraging technologies like IoT and AI to improve agricultural productivity. The integration of Industry 4.0 into agriculture has led to the emergence of Agriculture 4.0. However, the deployment of IoT devices in open environments introduces security risks, including DDoS and false data injection attacks. We propose intrusion detection systems (IDS) based on deep learning and machine learning techniques to detect DDoS attacks. The performance of each model was evaluated using metrics such as Precision, Recall, F1-score, and Accuracy, leveraging the real-world CICIDS2017 dataset, which includes various types of attacks. Ultimately, our CNN model outperformed other models, demonstrating its superior performance.