article · The Journal of Supercomputing
Rapid growth in Internet of Things networks requires effective security against cyber-attacks, highlighting the need for efficient anomaly intrusion detection systems. High data complexity often challenges these systems, requiring balanced solutions that maintain high accuracy alongside low detection times. A hybrid feature selection method combines filter and wrapper techniques, integrating them into a two-level intrusion detection architecture. The first level classifies network packets as normal or malicious, while the second level categorises the specific attack type. Synthetic Minority Over-sampling addresses class imbalance within network datasets. Testing on benchmark datasets including BoT-IoT, TON-IoT, and CIC-DDoS2019 across several machine learning algorithms reveals strong performance. In particular, a decision tree model achieved detection accuracy between 99.82 and 100 per cent, alongside rapid detection times between 0.02 and 0.15 seconds, outperforming existing detection architectures.
As connected devices proliferate, protecting network traffic from diverse cyber threats becomes essential. Practical security systems must rapidly identify attacks without causing network delays or high computational strain. By simultaneously achieving near-perfect detection accuracy and split-second response times on standard benchmarks, this approach helps demonstrate that machine learning models can effectively safeguard resource-constrained Internet of Things environments against emerging threats.
This research applies to network security software and intrusion detection tools designed for Internet of Things infrastructure. Network administrators and security developers could implement the two-stage classification framework to monitor network traffic. Because the methodology has been validated on benchmark datasets rather than deployed in live production networks, the technology appears to be applied and tested research requiring further operational testing before commercial software deployment.
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Abstract The exponential growth of Internet of Things (IoT) devices underscores the need for robust security measures against cyber-attacks. Extensive research in the IoT security community has centered on effective traffic detection models, with a particular focus on anomaly intrusion detection systems (AIDS). This paper specifically addresses the preprocessing stage for IoT datasets and feature selection approaches to reduce the complexity of the data. The goal is to develop an efficient AIDS that strikes a balance between high accuracy and low detection time. To achieve this goal, we propose a hybrid feature selection approach that combines filter and wrapper methods. This approach is integrated into a two-level anomaly intrusion detection system. At level 1, our approach classifies network packets into normal or attack, with level 2 further classifying the attack to determine its specific category. One critical aspect we consider is the imbalance in these datasets, which is addressed using the Synthetic Minority Over-sampling Technique (SMOTE). To evaluate how the selected features affect the performance of the machine learning model across different algorithms, namely Decision Tree, Random Forest, Gaussian Naive Bayes, and k-Nearest Neighbor, we employ benchmark datasets: BoT-IoT, TON-IoT, and CIC-DDoS2019. Evaluation metrics encompass detection accuracy, precision, recall, and F1-score. Results indicate that the decision tree achieves high detection accuracy, ranging between 99.82 and 100%, with short detection times ranging between 0.02 and 0.15 s, outperforming existing AIDS architectures for IoT networks and establishing its superiority in achieving both accuracy and efficient detection times.
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DOI: 10.1007/s11227-024-06409-x
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