scholarly journals Applications of Data Mining Algorithms for Network Security

2019 ◽  
Author(s):  
Kai Chain
Keyword(s):  
Data Mining ◽  
Network Security ◽  
Intrusion Detection ◽  
Pattern Mining ◽  
Academic Community ◽  
Network Attacks ◽  
Data Mining Algorithms ◽  
Human Effort ◽  
Modern Information ◽  
Mining Algorithms

Typical modern information systems are required to process copious data. Conventional manual approaches can no longer effectively analyze such massive amounts of data, and thus humans resort to smart techniques and tools to complement human effort. Currently, network security events occur frequently, and generate abundant log and alert files. Processing such vast quantities of data particularly requires smart techniques. This study reviewed several crucial developments of existent data mining algorithms, including those that compile alerts generated by heterogeneous IDSs into scenarios and employ various HMMs to detect complex network attacks. Moreover, sequential pattern mining algorithms were examined to develop multi-step intrusion detection. These studies can focus on applying these algorithms in practical settings to effectively reduce the occurrence of false alerts. This article researched the application of data mining algorithms in network security. The academic community has recently generated numerous studies on this topic.

scholarly journals Algorithms for detecting network attacks in an enterprise industrial network based on data mining algorithms

2021 ◽  
Vol 2001 (1) ◽  
pp. 012004
Author(s):  
A M Vulfin ◽  
V I Vasilyev ◽  
S N Kuharev ◽  
E V Homutov ◽  
A D Kirillova
Keyword(s):  
Data Mining ◽  
Network Attacks ◽  
Data Mining Algorithms ◽  
Industrial Network ◽  
Mining Algorithms

Clustering of Time Series Data

2011 ◽  
pp. 258-263
Author(s):  
Anne Denton
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Pattern Mining ◽  
Time Series Data ◽  
Frequent Pattern Mining ◽  
Frequent Pattern ◽  
Series Data ◽  
Science And Engineering ◽  
Data Mining Algorithms ◽  
Mining Algorithms

Time series data is of interest to most science and engineering disciplines and analysis techniques have been developed for hundreds of years. There have, however, in recent years been new developments in data mining techniques, such as frequent pattern mining, that take a different perspective of data. Traditional techniques were not meant for such pattern-oriented approaches. There is, as a result, a significant need for research that extends traditional time-series analysis, in particular clustering, to the requirements of the new data mining algorithms.

Trajectory Pattern Mining: Methods and Applications

2014 ◽  
Vol 490-491 ◽  
pp. 1361-1367
Author(s):  
Xin Huang ◽  
Hui Juan Chen ◽  
Mao Gong Zheng ◽  
Ping Liu ◽  
Jing Qian
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Pattern Mining ◽  
Data Sets ◽  
Trajectory Data ◽  
Data Mining Algorithms ◽  
Mining Methods ◽  
Trajectory Pattern ◽  
Mining Algorithms ◽  
Trajectory Pattern Mining

With the advent of location-based social media and locationacquisition technologies, trajectory data are becoming more and more ubiquitous in the real world. A lot of data mining algorithms have been successfully applied to trajectory data sets. Trajectory pattern mining has received a lot of attention in recent years. In this paper, we review the most inuential methods as well as typical applications within the context of trajectory pattern mining.

scholarly journals Data Mining Techniques for Providing Network Security through Intrusion Detection Systems: a Survey

2018 ◽  
Vol 7 (1) ◽  
pp. 7
Author(s):  
Prabhu Kavin B ◽  
Ganapathy S
Keyword(s):  
Data Mining ◽  
Network Security ◽  
Intrusion Detection ◽  
Detection System ◽  
Intrusion Detection Systems ◽  
Detection Accuracy ◽  
Data Mining Techniques ◽  
Detection Systems ◽  
Data Mining Algorithms ◽  
Soft Computing Techniques

Intrusion Detection Systems are playing major role in network security in this internet world. Many researchers have been introduced number of intrusion detection systems in the past. Even though, no system was detected all kind of attacks and achieved better detection accuracy. Most of the intrusion detection systems are used data mining techniques such as clustering, outlier detection, classification, classification through learning techniques. Most of the researchers have been applied soft computing techniques for making effective decision over the network dataset for enhancing the detection accuracy in Intrusion Detection System. Few researchers also applied artificial intelligence techniques along with data mining algorithms for making dynamic decision. This paper discusses about the number of intrusion detection systems that are proposed for providing network security. Finally, comparative analysis made between the existing systems and suggested some new ideas for enhancing the performance of the existing systems.

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