Anomaly Identification in IoT-Networks Using Artificial Intelligence-Based Data-Driven Techniques in Cloud Environmen
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V2I2P110Keywords:
Cybersecurity, Cloud, IOT, Anomaly Intrusion Detection, Machine Learning, Deep learningAbstract
In the dynamic and ever-evolving realm of computer networks, cloud computing has emerged as a major player. Cloud networks provide on-demand access to pooled resources, but anomalies can compromise their security and integrity. Recognizing anomalies in network traffic is crucial for data security in modern networked systems including the Internet of Things (IoT). This research introduces a novel anomaly detection technique that utilizes the CICIDS-2017 dataset, the Random Forest (RF) classifier, and machine learning (ML). Using the Synthetic Minority Over-Sampling Technique (SMOTE), class balancing is accomplished. Some of the data pre-processing procedures included in the suggested methodology are one-hot encoding, feature selection, handling missing data, and z-score normalization. Two datasets are created from the processed data: one for training and another for testing. Using the Random Forest model, both normal and harmful traffic patterns can be identified. The experimental findings demonstrate that the model achieves a high level of resilience and reliability in detecting anomalies. It obtains an AUC of 1.00, an F1-score of 99.65%, a recall of 99.31%, a precision of 99.99%, and an accuracy of 99.65%. The proposed system offers adequate management of imbalanced and high-dimensional network data, making it applicable in real-world IoT settings. It provides scalability, flexibility, and high detection rates
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