Application of Association Rules Based on CF Gene in Intrusion Detection

2014 ◽  
Vol 556-562 ◽  
pp. 2603-2606
Author(s):  
Rong Fu ◽  
Li Yan Liu ◽  
Ying Qian Zhang ◽  
Yi He
Keyword(s):  
Intrusion Detection ◽  
Association Rules ◽  
Association Rule ◽  
Detection Technology ◽  
Mining Association Rule ◽  
Improved Algorithm

By analyzing and studying the most current algorithms about mining association rule, the rules evaluated by minimum confidence could not ensure the validity of the rules and will generate unrelated rules which will affect the intrusion detection work. This paper proposes CF measure based on the previous work and applies the association rule algorithm based on CF to intrusion detection technology to detect the intrusion behaviors in the network. Finally, experiments show that improved algorithm is more efficient.

An Enhanced Fuzzy ARM Approach for Intrusion Detection

2011 ◽  
Vol 3 (2) ◽  
pp. 41-61 ◽  
Author(s):  
Nasser S. Abouzakhar ◽  
Huankai Chen ◽  
Bruce Christianson
Keyword(s):  
Data Mining ◽  
Fuzzy Logic ◽  
Intrusion Detection ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Data Mining Technique ◽  
Detection Model ◽  
Improved Algorithm

The integration of fuzzy logic with data mining methods such as association rules has achieved interesting results in various digital forensics applications. As a data mining technique, the association rule mining (ARM) algorithm uses ranges to convert any quantitative features into categorical ones. Such features lead to the sudden boundary problem, which can be smoothed by incorporating fuzzy logic so as to develop interesting patterns for intrusion detection. This paper introduces a Fuzzy ARM-based intrusion detection model that is tested on the CAIDA 2007 backscatter network traffic dataset. Moreover, the authors present an improved algorithm named Matrix Fuzzy ARM algorithm for mining fuzzy association rules. The experiments and results that are presented in this paper demonstrate the effectiveness of integrating fuzzy logic with association rule mining in intrusion detection. The performance of the developed detection model is improved by using this integrated approach and improved algorithm.

Visual Data Mining for Discovering Association Rules

2008 ◽  
pp. 2105-2120
Author(s):  
Kesaraporn Techapichetvanich ◽  
Amitava Datta
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Large Data ◽  
Data Sets ◽  
Visual Data Mining ◽  
Useful Knowledge ◽  
Large Databases ◽  
A New Technique ◽  
Mining Association Rule

Both visualization and data mining have become important tools in discovering hidden relationships in large data sets, and in extracting useful knowledge and information from large databases. Even though many algorithms for mining association rules have been researched extensively in the past decade, they do not incorporate users in the association-rule mining process. Most of these algorithms generate a large number of association rules, some of which are not practically interesting. This chapter presents a new technique that integrates visualization into the mining association rule process. Users can apply their knowledge and be involved in finding interesting association rules through interactive visualization, after obtaining visual feedback as the algorithm generates association rules. In addition, the users gain insight and deeper understanding of their data sets, as well as control over mining meaningful association rules.

Visual Data Mining for Discovering Association Rules

2011 ◽  
pp. 209-226
Author(s):  
Kesaraporn Techapichetvanich ◽  
Amitava Datta
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Large Data ◽  
Data Sets ◽  
Visual Data Mining ◽  
Useful Knowledge ◽  
Large Databases ◽  
A New Technique ◽  
Mining Association Rule

Both visualization and data mining have become important tools in discovering hidden relationships in large data sets, and in extracting useful knowledge and information from large databases. Even though many algorithms for mining association rules have been researched extensively in the past decade, they do not incorporate users in the association-rule mining process. Most of these algorithms generate a large number of association rules, some of which are not practically interesting. This chapter presents a new technique that integrates visualization into the mining association rule process. Users can apply their knowledge and be involved in finding interesting association rules through interactive visualization, after obtaining visual feedback as the algorithm generates association rules. In addition, the users gain insight and deeper understanding of their data sets, as well as control over mining meaningful association rules.

Improved Algorithm for Mining Maximum Frequent Patterns Based on FP-Tree

2013 ◽  
Vol 756-759 ◽  
pp. 3692-3695 ◽  
Author(s):  
Nai Li Liu ◽  
Lei Ma
Keyword(s):  
Data Mining ◽  
Fast Algorithm ◽  
Association Rule ◽  
Frequent Patterns ◽  
Memory Space ◽  
The Cost ◽  
Mining Association Rule ◽  
Very High ◽  
Improved Algorithm

Mining association rule is an important matter in data mining, in which mining maximum frequent patterns is a key problem. Many of the previous algorithms mine maximum frequent patterns by producing candidate patterns firstly, then pruning. But the cost of producing candidate patterns is very high, especially when there exists long patterns. In this paper, the structure of a FP-tree is improved, we propose a fast algorithm based on FP-Tree for mining maximum frequent patterns, the algorithm does not produce maximum frequent candidate patterns and is more effectively than other improved algorithms. The new FP-Tree is a one-way tree and only retains pointers to point its father in each node, so at least one third of memory is saved. Experiment results show that the algorithm is efficient and saves memory space.

Analysis and Research of Intrusion Detection System Based on Association Rule

2013 ◽  
Vol 709 ◽  
pp. 628-631
Author(s):  
Ya Bing Jiao
Keyword(s):  
Data Mining ◽  
Intrusion Detection ◽  
Association Rule ◽  
Intrusion Detection System ◽  
Detection Efficiency ◽  
Detection System ◽  
Mining Association Rule

A model of intrusion detection system based on the technology data mining is presented on the basis of introduction on the concept and the technical method of the intrusion detection system. In this model, the two methods of the technology data mining association rule and the classified analysis cooperate with each other and the detection efficiency will be greatly enhanced.

scholarly journals PENERAPAN METODE ASSOCIATION RULE MENGGUNAKAN ALGORITMA APRIORI PADA SIMULASI PREDIKSI HUJAN WILAYAH KOTA BANDUNG

2016 ◽  
Vol 2 (3) ◽  
Author(s):  
Mohamad Fauzy ◽  
Kemas Rahmat Saleh W ◽  
Ibnu Asror
Keyword(s):  
Data Mining ◽  
Data Processing ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Weather Forecast ◽  
Weather Data ◽  
Rule Mining ◽  
Similarity Relationships ◽  
Mining Association Rule

[Id] Prakiraan cuaca saat ini telah menjadi satu hal yang dibutuhkan bagi banyak orang di dunia. Dalam memprediksi hujan pengolahan data cuaca merupakan hal yang penting. Namun permasalahannya, data cuaca yang semakin hari semakin bertambah menyebabkan penumpukan data sehingga pengolahan data tersebut perlu penanganan lebih lanjut. Oleh karena itu pemanfaatan data mining digunakan untuk menyelesaikan masalah ini. Association rule mining adalah salah satu metode data mining yang dapat mengidentifikasi hubungan kesamaan antar item. Penelitian ini dilakukan dengan tiga tahapan utama yaitu : 1) melakukan analisa pola frekuensi tinggi menggunakan algortima apriori; 2) pembentukan aturan asosiasi (association rule); 3) uji kekuatan rule yang terbentuk dengan menghitung lift ratio pada masing-masing rule. Dataset yang digunakan adalah data klimatologi yang diambil dari BMKG stasiun geofisika kelas 1 Bandung. Hasil akhir dari Penelitian ini berupa aturan-aturan asosiasi (association rules) dimana aturan-aturan ini dapat dijadikan sebagai acuan dalam memprediksi cuaca hujan atau tidak hujan untuk satu hari kedepan. Kata kunci : Data mining, association rule, apriori, prediksi hujan [En] Weather forecast today has become a necessary thing for many people in the world. In predicting rain weather data processing is essential. But the problem, weather data that is increasingly growing cause the accumulation of data so that the data processing needs further treatment. Therefore, the use of data mining is used to solve this problem. Association rule mining is one of data mining methods that can identify similarity relationships between items. This research is performed by three main stages, namely: 1) to analyze high frequency patterns using algorithms priori; 2) the establishment of an association rule (association rule); 3) test the strength of the rule which is formed by calculating the ratio elevator on each rule. The dataset used is the climatological data taken from BMKG station 1st class geophysical Bandung. The end result of this research in the form of rules of association (association rules) in which these rules can be used as a reference in predicting the weather is rain or not rain for the next day. Keywords : data mining, association rule, apriori, rain forecast

Research and Application in Supply Chain Management Based on Correlation Analyze of Association Rules Algorithm

2006 ◽  
Vol 532-533 ◽  
pp. 1024-1027 ◽  
Author(s):  
Shou Ning Qu ◽  
Qin Wang ◽  
Kui Liu ◽  
De Jun Xu
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Association Rules ◽  
Association Rule ◽  
Chain Management ◽  
Improved Algorithm

In this paper, the association rule algorithm and its defects were analyzed. An improved algorithm was put forward for applying it to analysis the association of products fittings in SCM. The application of improved algorithm can mine which kinds of fittings or sets of items being matched to get a salable product or gain the higher profit. So it can not only instruct customer’s consumption but also can help the entrepreneur make a detailed and efficient internal plan.

scholarly journals IMPLEMENTASI DATA MINING DALAM ANALISA POLA PEMINJAMAN BUKU DI PERPUSTAKAAN MENGGUNAKAN METODE ASSOCIATION RULE

JURTEKSI ◽  
2019 ◽  
Vol 5 (1) ◽  
pp. 89-96
Author(s):  
Edi Kurniawan
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Growth Data ◽  
Data Mining Technique ◽  
Transaction Data ◽  
Mining Technique ◽  
New Information ◽  
Important Means ◽  
Mining Association Rule

Abstract: The library is one of the most important means to add insight and knowledge to everyone. In general, borrowing transaction data books that exist in a library are only left to accumulate by the library in the database without any utilization or further processing of the data that has long been stored. By utilizing the Data Mining technique using association rules with FP-Growth, these data will be very useful. Because from the data lending books to the library, new information can be gleaned about what books are often borrowed and know the pattern of relationships between books that have been borrowed together so that later it can be used to compile books in accordance with the existing borrowing patterns so that they can facilitate library visitors in the process of finding books. Keywords: Data Mining, Association Rule, FP-Growth, Library Abstrak: Perpustakaan merupakan salah satu sarana yang sangat penting untuk menambah wawasan dan keilmuan setiap orang. Pada umumnya data transaksi peminjaman buku yang ada pada sebuah perpustakaan hanya dibiarkan saja menumpuk oleh pihak perpustakaan di dalam database tanpa ada pemanfaatan atau pengolahan lebih lanjut dari data-data yang telah lama tersimpan tersebut. Dengan melakukan pemanfaatan menggunakan Teknik Data Mining metode association rules dengan FP-Growth, data-data tersebut akan jadi sangat bermanfaat. Karena dari data peminjaman buku pada perpustakaan tersebut dapat diggali informasi baru tentang buku-buku apa yang sering dipinjam dan mengetahui pola hubungan antara buku yang telah dipinjam secara bersama-sama sehingga nantinya dapat dimanfaatkan untuk melakukan penyusunan buku sesuai dengan pola peminjaman buku yang ada sehingga dapat mempermudah para pengunjung perpustakaan dalam proses pencarian buku. Kata Kunci : Data Mining, Asociation Rule, FP-Growth, Perpustakaan

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