Anomaly Intrusion Detection based on Clustering a Data Stream

Jinsuk, Kang; Oh, Sang-Hyun

doi:10.7763/ijfcc.2012.v1.5

Cited by 2 publications

(1 citation statement)

References 2 publications

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“…First, for the unsupervised model [16][17][18][19][20][21][22], it includes DBSCAN, K-means, and spectral clustering. e DBSCAN algorithm [23] has a long convergence time when the sample data is too large and is not suitable for the big data network environment.…”

Section: Detection Methodologymentioning

confidence: 99%

WEB DDoS Attack Detection Method Based on Semisupervised Learning

et al. 2021

Security and Communication Networks

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Since the services on the Internet are becoming increasingly abundant, all walks of life are inextricably linked with the Internet. Simultaneously, the Internet’s WEB attacks have never stopped. Relative to other common WEB attacks, WEB DDoS (distributed denial of service) will cause serious damage to the availability of the target network or system resources in a short period of time. At present, most researches are centered around machine learning-related DDoS attack detection algorithms. According to previous studies, unsupervised methods generally have a high false positive rate, while supervisory methods cannot handle large amount of network traffic data, and the performance is often limited by noise and irrelevant data. Therefore, this paper proposes a semisupervised learning detection model combining spectral clustering and random forest to detect the DDoS attack of the WEB application layer and compares it with other existing detection schemes to verify the semisupervised learning model proposed in this paper. While ensuring a low false positive rate, there is a certain improvement in the detection rate, which is more suitable for the WEB application layer DDoS attack detection.

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