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PatchesNet: PatchTST-based multi-scale network security situation prediction
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AI
DOI：10.1016/j.knosys.2024.112037.png)
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Abstract
En 中文
Internet of Things (IoT) technology increases connectivity between devices; however, IoT device networks are less secure, making them highly susceptible to attack. Most of the network security defense techniques are passive defense with inevitable delays. For instance, in the case of intrusion detection systems, during the time interval between detecting an attack and implementing defenses, the attacker may inflict damage on the target. Therefore, the adoption of active defense techniques becomes particularly important. Network Security Situation Prediction (NSSP) is an active defense technology capable of predicting future states of network security. Currently, most methods in the NSSP field focus on single-step prediction, while research related to multi -step prediction is scarce. Hence, this paper presents an NSSP method based on the PatchTST framework(PatchesNet), which exhibits good multi -step predictive performance. The method uses Variational Mode Decomposition (VMD) to deal with sequence instability. Multi -scale Patch delivery (MSP), Multi -scale Second Decomposition (MSD), and Period Embedding (PeE) are used to obtain more sequence features. Correlation Fusion (CF) can reduce the noise effect on prediction. Experiments are conducted on three datasets. The results demonstrate that PatchesNet exhibits favorable performance, relatively reducing the MSE and MAE by 39.243% and 26.033%, respectively, compared to the previous state-of-the-art method.
Keywords:
Network security situation prediction
Multi-scale prediction
Period embedding
Variational mode decomposition
PatchTST
Journal
K
IF:
7.6
Papers:
1.2W
Citations:
4.5W