ArticleScientific reports2023
Support vector data description with kernel density estimation (SVDD-KDE) control chart for network intrusion monitoring.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- A novel multi-scale CNN and Bi-LSTM arbitration dense network model for low-rate DDoS attack detection.Scientific reports · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Multivariate control charts have been applied in many sectors. One of the sectors that employ this method is network intrusion detection. However, the issue arises when the conventional control chart faces difficulty monitoring the network-traffic data that do not follow a normal distribution as required. Consequently, more false alarms will be found when inspecting network traffic data. To settle this problem, support vector data description (SVDD) is suggested. The control chart based on the SVDD distance can be applied for the non-normal distribution, even the unknown distributions. Kernel density estimation (KDE) is the nonparametric approach that can be applied in estimating the control limit of the non-parametric control charts. Based on these facts, a multivariate chart based on the integrated SVDD and KDE (SVDD-KDE) is proposed to monitor the network's anomaly. Simulation using the synthetic dataset is performed to examine the performance of the SVDD-KDE chart in detecting multivariate data shifts and outliers. Based on the simulation results, the proposed method produces better performance in detecting shifts and higher accuracy in detecting outliers. Further, the proposed method is applied in the intrusion detection system (IDS) to monitor network attacks. The NSL-KDD data is analyzed as the benchmark dataset. A comparison between the SVDD-KDE chart with the other IDS-based-control chart and the machine learning algorithms is executed. Although the it has high computational cost, the results show that the IDS based on the SVDD-KDE chart produces a high accuracy at 0.917 and AUC at 0.915 with a low false positive rate compared to several algorithms.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.