Survey of Intrusion Detection Methods Based on Data Mining Algorithms

2019 ◽  
Author(s):  
Zichuan Jin ◽  
Yanpeng Cui ◽  
Zheng Yan
Keyword(s):  
Data Mining ◽  
Intrusion Detection ◽  
Detection Methods ◽  
Data Mining Algorithms ◽  
Mining Algorithms

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.

Problems of KDD Cup 99 Dataset Existed and Data Preprocessing

2014 ◽  
Vol 667 ◽  
pp. 218-225 ◽  
Author(s):  
Yan Wang ◽  
Kun Yang ◽  
Xiang Jing ◽  
Huang Long Jin
Keyword(s):  
Data Mining ◽  
Intrusion Detection ◽  
Detection System ◽  
Data Preprocessing ◽  
Vital Role ◽  
Data Mining Algorithms ◽  
Network Intrusion ◽  
Depth Analysis ◽  
Mining Algorithms ◽  
Kdd Cup 99

KDD Cup 99 dataset is not only the most widely used dataset in intrusion detection, but also the de facto benchmark on evaluating the performance merits of intrusion detection system. Nevertheless there are a lot of issues in this dataset which cannot be omitted. In order to establish good data mining models in intrusion detection and find the appropriate network intrusion attack types’ features, researchers should have a well-known understanding on this dataset. In this paper, first and foremost we have made an in-depth analysis on the problems which the dataset are existed, and given the related solutions. Secondly, we also have carried out plenty data preprocessing on the 10% subset of KDD Cup 99 dataset’s training set, giving better results to the following process. What’s more, by comparing 10 common kinds of data mining algorithms in our experiment, we have analyzed and summarized that data preprocessing plays a vital role on the performance and importance to data mining algorithms.

scholarly journals Pharmacovigilance, signal detection using statistical data mining methods

2018 ◽  
Vol 7 (2.31) ◽  
pp. 122
Author(s):  
G V. Sriramakrishnan ◽  
M Muthu Selvam ◽  
K Mariappan ◽  
G Suseendran
Keyword(s):  
Data Mining ◽  
Signal Detection ◽  
Detection Methods ◽  
Risk Minimization ◽  
Data Mining Algorithms ◽  
Use Of Data ◽  
Statistical Signal Detection ◽  
Statistical Data Mining ◽  
Mining Algorithms ◽  
Health Programmes

Pharmacovigilance programmes monitor and help safeguarding the use of medicines which is grave to the success of public health programmes. Identifying new possible risks and developing risk minimization action plans to prevent or ease these risks is at the heart of all pharmacovigilance activities throughout the product lifecycle.  In this paper we examine the use of data mining algorithms to identify signals from adverse events reported. The capabilities include screening, data mining and frequency tabulation for potential signals, including signal estimation using established statistical signal detection methods. We have standard processes, algorithms and follow current requirements for signal detection and risk management activities.

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