Application of an Improved Data Stream Clustering Algorithm in Intrusion Detection System

2017 ◽  
pp. 626-632 ◽  
Author(s):  
Chunyong Yin ◽  
Lian Xia ◽  
Jin Wang
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Data Stream ◽  
Clustering Algorithm ◽  
Detection System ◽  
Stream Clustering ◽  
Data Stream Clustering

A New Data Stream Clustering Approach about Intrusion Detection

2014 ◽  
Vol 926-930 ◽  
pp. 2898-2901 ◽  
Author(s):  
Zhuo Qun Li
Keyword(s):  
Intrusion Detection ◽  
Real Time ◽  
Data Stream ◽  
Clustering Algorithm ◽  
Detection System ◽  
Practical Significance ◽  
Time Pattern ◽  
Stream Clustering ◽  
Network Intrusion ◽  
Data Stream Clustering

Intrusion detection is one of the most important techniques for protecting network security. In addition, intrusion detection model can be used to recognize real-time pattern, which has important practical significance for real-time intrusion detection. However, due to the sheer speed and scale of the data, data points must often be analyzed in real time. The one-pass-through requirement and the lack of efficient clustering algorithms to identify intrusion patterns limit the power and scalability of this approach. A data stream clustering algorithm is proposed for real-time network intrusion detection. By introducing the new hashing mechanism, the method can quickly find the clustering patterns in the data stream. The method significantly reduces the false alarm rate of intrusion detection, and improves the performance of intrusion detection system.

Study on Intrusion Detection System Based on Data Mining

2015 ◽  
Vol 713-715 ◽  
pp. 2499-2502
Author(s):  
Jiang Kun Mao ◽  
Fan Zhan
Keyword(s):  
Data Mining ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Clustering Algorithm ◽  
Detection Efficiency ◽  
Detection System ◽  
Machine Learning Techniques ◽  
Cluster Center ◽  
Security Technology ◽  
Learning Techniques

Intrusion detection system as a proactive network security technology, is necessary and reasonable to add a static defense. However, the traditional exceptions and errors detecting exist issues of leakage police, the false alarm rate or maintenance difficult. In this paper, The intrusion detection system based on data mining with statistics, machine learning techniques in the detection performance, robustness, self-adaptability has a great advantage. The system improves the K-means clustering algorithm, focus on solving two questions of the cluster center node selection and discriminating of clustering properties, the test shows that the system further enhance the detection efficiency of the system.

scholarly journals EvolveCluster: an evolutionary clustering algorithm for streaming data

2021 ◽  
Author(s):  
Christian Nordahl ◽  
Veselka Boeva ◽  
Håkan Grahn ◽  
Marie Persson Netz
Keyword(s):  
Data Streams ◽  
Data Stream ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Streaming Data ◽  
Evolutionary Clustering ◽  
Stream Clustering ◽  
The Past ◽  
Data Stream Clustering ◽  
Evolving Data

AbstractData has become an integral part of our society in the past years, arriving faster and in larger quantities than before. Traditional clustering algorithms rely on the availability of entire datasets to model them correctly and efficiently. Such requirements are not possible in the data stream clustering scenario, where data arrives and needs to be analyzed continuously. This paper proposes a novel evolutionary clustering algorithm, entitled EvolveCluster, capable of modeling evolving data streams. We compare EvolveCluster against two other evolutionary clustering algorithms, PivotBiCluster and Split-Merge Evolutionary Clustering, by conducting experiments on three different datasets. Furthermore, we perform additional experiments on EvolveCluster to further evaluate its capabilities on clustering evolving data streams. Our results show that EvolveCluster manages to capture evolving data stream behaviors and adapts accordingly.

scholarly journals Intrusion Detection System Using Data Stream Classification

2021 ◽  
pp. 319-328
Author(s):  
Amer Abdulmajeed Abdualrahman ◽  
Mahmood Khalel Ibrahem
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Data Stream ◽  
Detection System ◽  
Data Communication ◽  
Computation Time ◽  
Stream Classification ◽  
Data Stream Classification ◽  
Network Intrusion ◽  
Learning Machine

Secure data communication across networks is always threatened with intrusion and abuse. Network Intrusion Detection System (IDS) is a valuable tool for in-depth defense of computer networks. Most research and applications in the field of intrusion detection systems was built based on analysing the several datasets that contain the attacks types using the classification of batch learning machine. The present study presents the intrusion detection system based on Data Stream Classification. Several data stream algorithms were applied on CICIDS2017 datasets which contain several new types of attacks. The results were evaluated to choose the best algorithm that satisfies high accuracy and low computation time.

A Cloud Intrusion Detection System Using Novel PRFCM Clustering and KNN Based Dempster-Shafer Rule

2016 ◽  
Vol 6 (4) ◽  
pp. 18-35 ◽  
Author(s):  
Partha Ghosh ◽  
Shivam Shakti ◽  
Santanu Phadikar
Keyword(s):  
Cloud Computing ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Clustering Algorithm ◽  
Detection System ◽  
Cloud Environment ◽  
Dempster Shafer Theory ◽  
Computing Paradigm ◽  
Rule Set ◽  
Shafer Theory

Cloud computing has established a new horizon in the field of Information Technology. Due to the large number of users and extensive utilization, the Cloud computing paradigm attracts intruders who exploit its vulnerabilities. To secure the Cloud environment from such intruders an Intrusion Detection System (IDS) is required. In this paper the authors have proposed an anomaly based IDS which classifies an incoming connection by taking the deviation of it from the normal behaviors. The proposed method uses a novel Penalty Reward based Fuzzy C-Means (PRFCM) clustering algorithm to generate a rule set and the best rule set is extracted from it using a modified approach for KNN algorithm. This best rule set is used in evidential reasoning of Dempster Shafer Theory for classification. The IDS has been trained and tested with NSL-KDD dataset for performance evaluation. The results prove the proposed IDS to be highly efficient and reliable.

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