Anomaly detection in network traffic using K-mean clustering

Kumari, Rashmi; Sheetanshu,; Singh, Manish Kumar; Jha, Ritesh; Singh, Niraj Kumar

doi:10.1109/rait.2016.7507933

Cited by 55 publications

(21 citation statements)

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“…The various partitioning methods are distance-based. In a system, if k is the given number of partitions for construction, the partitioning method helps in creating an initial partitioning [3]. Further, the iterative relocation method is used for improving the partitioning technique with the help of moving the objects from one group to another.…”

Section: Partitioning Methodsmentioning

confidence: 99%

Two-step Technique for Prediction Analysis using K-Means Clustering Algorithm

Saxena¹,

Kumar²,

Tewari³

2017

IJCA

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The technique that is utilized for analyzing the complex data is known as data mining technique. As per the input dataset provided, the predictions are made for the data with the help of prediction analysis method. There are various new techniques proposed for the execution of prediction analysis technique. In this paper, the k-mean algorithm is utilized for categorizing the data. Further, for the classification of this data, the SVM classifier is applied. For improving the performance of prediction analysis in terms of accuracy the back propagation algorithm is used along with the k-mean clustering algorithm. For executing this proposed technique, the MATLAB tool is used. As per the experimental results it is concluded that the accuracy of the clustering algorithm is improved as well as the execution time utilized for prediction analysis is decreased.

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Section: Partitioning Methodsmentioning

confidence: 99%

Two-step Technique for Prediction Analysis using K-Means Clustering Algorithm

Saxena¹,

Kumar²,

Tewari³

2017

IJCA

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“…Initially, the k-means clustering algorithm is applied to detect DoS attacks [12]. The data set X = { 1 , … , } consists of traffic sessions .…”

Section: ) Ping Of Death (Pod) Dosmentioning

confidence: 99%

Application of Clustering Methods Network Traffic for Detecting Ddos Attacks

Бабек¹

2018

JPIT

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“…Limitations are present with these techniques however. Kumari et al [15] looks at a clustering technique for the use of anomaly detection over a network, setting a distance based threshold as the 100th farthest data point from the obtained cluster centroids. This threshold is a common theme across multiple anomaly detection solutions [16][17][18].…”

Section: Related Workmentioning

confidence: 99%

NetFlow Anomaly Detection Though Parallel Cluster Density Analysis in Continuous Time-Series

Flanagan

Fallon

Connolly

et al. 2017

Lecture Notes in Computer Science

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The increase in malicious network based attacks has resulted in a growing interest in network anomaly detection. The ability to detect unauthorized or malicious activity on a network is of importance to any organization. With the increase in novel attacks, anomaly detection techniques can be more successful in detecting unknown malicious activity in comparison to traditional signature based methods. However, in a real-world environment, there are many variables that cannot be simulated. This paper proposes an architecture where parallel clustering algorithms work concurrently in order to detect abnormalities that may be lost while traversing over time-series windows. The presented results describe the NetFlow activity of the NPD Group, Inc. over a 24-hour period. The presented results contain real-world anomalies that were detected.

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Anomaly detection in network traffic using K-mean clustering

Cited by 55 publications

References 1 publication

Two-step Technique for Prediction Analysis using K-Means Clustering Algorithm

Two-step Technique for Prediction Analysis using K-Means Clustering Algorithm

Application of Clustering Methods Network Traffic for Detecting Ddos Attacks

NetFlow Anomaly Detection Though Parallel Cluster Density Analysis in Continuous Time-Series

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