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<p>Network anomaly detection is one of the key aspects to be considered for the protection of modern digital infrastructures against changing cyber-attacks. As networks are becoming increasingly larger and complex, the processing of millions of flows in real time is a high-performance computing (HPC) issue that requires parallel and distributed computing. This paper aims to evaluate the performance of six anomaly detection models: four machine learning models (Random Forest, Isolation Forest, Local Outlier Factor, One-Class SVM) and two deep learning models (LSTM-CNN, Autoencoder). The performance evaluation is done using four public datasets (UNSW-NB15, CICIDS-2017, NSL-KDD, ToN-IoT) with over five million data records. The results showed that parallel preprocessing achieves a 10.8 times speedup on 16 CPU cores, while GPU achieves a 35 times speedup for deep learning models. Among the models, the performance of the Random Forest is found to be stable with F1 &gt; 0.95.</p>

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Assessing the strength of machine learning and deep learning models for network anomaly detection: insights from multi-dataset cybersecurity analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月6日

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  • Uras Panahi

摘要

Network anomaly detection is one of the key aspects to be considered for the protection of modern digital infrastructures against changing cyber-attacks. As networks are becoming increasingly larger and complex, the processing of millions of flows in real time is a high-performance computing (HPC) issue that requires parallel and distributed computing. This paper aims to evaluate the performance of six anomaly detection models: four machine learning models (Random Forest, Isolation Forest, Local Outlier Factor, One-Class SVM) and two deep learning models (LSTM-CNN, Autoencoder). The performance evaluation is done using four public datasets (UNSW-NB15, CICIDS-2017, NSL-KDD, ToN-IoT) with over five million data records. The results showed that parallel preprocessing achieves a 10.8 times speedup on 16 CPU cores, while GPU achieves a 35 times speedup for deep learning models. Among the models, the performance of the Random Forest is found to be stable with F1 > 0.95.