A Design and Implementation of Intrusion Detection System by Using Data Mining

Sharma, Brijesh; Gupta, Huma

doi:10.1109/csnt.2014.147

Cited by 5 publications

(7 citation statements)

References 9 publications

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“…It is divided into three categories Apriori (normal) -large datasets, AprioriTID -datasets that can fit in memory and Apriori Hybrid -time consumption for switching. This is similar to the research of [1] who have used Apriori algorithm. Apriori will suffer from the cost of generating a huge number of candidate sets as per survey of (Wu et al 2008) and thus it's not a good choice for an IDS where memory will be a key factor.…”

Section: Related Research Worksupporting

confidence: 79%

“…In paper [1] have proposed a signature based Intrusion Detection system which is a combination of two popular data mining algorithms, Apriori (Association) and Kmeans (Cluster). With several frequent item sets, large item sets, or very l ow minimum support, Apriori will suffer from the cost of generating a huge number of candidate sets as per survey of [2].…”

Section: Related Research Workmentioning

confidence: 99%

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Ensemble Technique for Intruder Detection in Network Traffic

Agrawal¹,

Mohammed²,

Jinan³

2019

IJSIA

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Due to increasing incidents of cyber-attacks, building effective intrusion detection systems are essential for protecting information systems security, and yet it remains an elusive goal and a great challenge. However, most of the conducted studies rely on static and one-time dataset where all the changes monitored are based on the dataset used. As network behaviors and patterns change and intrusions evolve, thus it has very much become necessary to move away from static and one-time dataset toward more dynamically configurable classifiers. The Current researches show that different classifiers provide different results about the patterns to be classified. These different results combined together (aka ensemble) yields better performance than individual classifiers. In this paper we have used a hybrid ensemble intrusion detection system consisting of a Misuse Binary Tree of Classifiers as the first stage and an anomaly detection model based upon SVM Classifier as the second stage. The Binary Tree consists of several best known classifiers specialized in detecting specific attacks at a high level of accuracy. Combination of a Binary Tree and specialized classifiers will increase accuracy of the misuse detection model. The misuse detection model will detect only known attacks. In-order to detect unknown attacks, we have an anomaly detection model as the second stage. SVM has been used, since it's the best known classifier for anomaly detection which will detect patterns that deviate from normal behavior. The proposed hybrid intrusion detection has been tested and evaluated using KDD Cup '99, NSL-KDD and UNSW-NB15 datasets.

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Section: Related Research Worksupporting

confidence: 79%

Section: Related Research Workmentioning

confidence: 99%

Ensemble Technique for Intruder Detection in Network Traffic

Agrawal¹,

Mohammed²,

Jinan³

2019

IJSIA

View full text Add to dashboard Cite

show abstract

Section: Related Workmentioning

confidence: 70%

“…In paper [1] have proposed a signature based Intrusion Detection system which is a combination of two popular data mining algorithms, Apriori (Association) and K-means (Cluster). With several frequent item sets, large item sets, or very low minimum support, Apriori will suffer from the cost of generating a huge number of candidate sets as per survey of [2].…”

Section: Related Workmentioning

confidence: 99%

Developing Data Mining Techniques for Intruder Detection in Network Traffic

Agrawal¹,

Mohammed²,

Fiaidhi³

2016

IJSIA

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“…Beberapa penelitian yang mengidentifikasi pendonor darah dengan menggunakan pendekatan machine learning. Penelitian [3] mengklasifikasikan dan memprediksi jumlah pendonor darah menurut umur dan golongan darahnya. aplikasi Weka telah digunakan dan berhasil untuk menjalankan algoritma J48.…”

Section: Pendahuluanunclassified

Analisis perbandingan Algoritma Support Vector Machine, Naive Bayes dan Regresi Logistik untuk Memprediksi Donor Darah

Hendriyana¹,

Karo²,

Dewi³

2022

j. teknologi terpadu

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Suplai dan stok darah sangat dibutuhkan di saat genting. Satu-satunya cara untuk memenuhi ketersediaan darah adalah sumbangan rutin dari sukarelawan yang sehat. Tujuan dari penelitian ini adalah untuk mengembangkan dan mengevaluasi algoritma machine learning untuk memprediksi seorang sukarelawan akan berdonor atau tidak. Algoritma machine learning yang digunakan adalah Naïve Bayes, Regresi Logistik dan Support Vector Machine (SVM). Penelitian ini juga menerapkan proses normalisasi data dengan Z-score untuk menstandarkan skala dataset. Adapun dataset bersumber dari Layanan Transfusi Darah Kota Hsin-Chu, Taiwan yang tersimpan di UCI repository. Metode evaluasi yang digunakan ialah akurasi, precision, recall dan F-1 score. Hasil penelitian dengan algoritma Naïve Bayes 89.90%, Regresi Logistik 82.59% dan SVM 94.79%. Proses normalisasi menggunakan metode Z-Score berkontribusi positif dalam meningkatkan performansi model klasifikasi. Berdasarkan performansi tersebut memberikan hasil prediksi bagi sukarelawan yang akan kembali mendonor darah untuk memberikan cadangan darah bagi yang membutuhkan.

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A Design and Implementation of Intrusion Detection System by Using Data Mining

Cited by 5 publications

References 9 publications

Ensemble Technique for Intruder Detection in Network Traffic

Ensemble Technique for Intruder Detection in Network Traffic

Developing Data Mining Techniques for Intruder Detection in Network Traffic

Analisis perbandingan Algoritma Support Vector Machine, Naive Bayes dan Regresi Logistik untuk Memprediksi Donor Darah

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