Intrusion detection system by improved preprocessing methods and Na&amp;#x00EF;ve Bayes classifier using NSL-KDD 99 Dataset

Deshmukh, Datta H.; Ghorpade, Tushar; Padiya, Puja

doi:10.1109/ecs.2014.6892542

Cited by 32 publications

(12 citation statements)

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“…Using machine learning algorithms in Intrusion Detection System (IDS) is an attracting research area for cyber security researchers around the world [5][6][7][8][9][10][11][12][13][14]. In this research, experiments were made based on classifying NSL-KDD dataset to either normal or attack.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Machine Learning in Network Security Using KNIME Analytics

Abualkibash¹

2019

IJNSA

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Machine learning has more and more effect on our every day's life. This field keeps growing and expanding into new areas. Machine learning is based on the implementation of artificial intelligence that gives systems the capability to automatically learn and enhance from experiments without being explicitly programmed. Machine Learning algorithms apply mathematical equations to analyze datasets and predict values based on the dataset. In the field of cybersecurity, machine learning algorithms can be utilized to train and analyze the Intrusion Detection Systems (IDSs) on security-related datasets. In this paper, we tested different machine learning algorithms to analyze NSL-KDD dataset using KNIME analytics.

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Section: Experiments and Resultsmentioning

confidence: 99%

Machine Learning in Network Security Using KNIME Analytics

Abualkibash¹

2019

IJNSA

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“…In 2014, Deshmukh et al [22] presents a Data Mining method in which various preprocessing methods are involved such as Normalization, Discretization and Feature selection. With the help of these methods the data is preprocessed and required features are selected.…”

Section: Literature Surveymentioning

confidence: 99%

An approach for efficient intrusion detection for KDD dataset: A survey

Sharma¹,

Gaur²

2016

IJATEE

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“…While most papers will try to emphasize the âAIJaccuracyâAI of their algorithm, this word could mean different things depending on the method. Some papers describe the accuracy on a small test set, or the average accuracy across many test sets and on specific attacks, and some even describe the accuracy as the percentage correct when classifying the training set itself [7,8,9,10]. Along with these issues, a singular accuracy score is not enough to determine the strength of an algorithm, particularly of this nature.…”

Section: Introductionmentioning

confidence: 99%

Benchmarks for Evaluating Anomaly Based Intrusion Detection Solutions

Miller¹,

Aliasgari²

2018

IJNSA

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Anomaly-based Intrusion Detection Systems (IDS) have gained increased popularity over time. There are many proposed anomaly-based systems using different Machine Learning (ML) algorithms and techniques, however there is no standard benchmark to compare them based on quantifiable measures. In this paper, we propose a benchmark that measures both accuracy and performance to produce objective metrics that can be used in the evaluation of each algorithm implementation. We then use this benchmark to compare accuracy as well as the performance of four different Anomaly-based IDS solutions based on various ML algorithms. The algorithms include Naive Bayes, Support Vector Machines, Neural Networks, and K-means Clustering. The benchmark evaluation is performed on the popular NSL-KDD dataset. The experimental results show the differences in accuracy and performance between these Anomaly-based IDS solutions on the dataset. The results also demonstrate how this benchmark can be used to create useful metrics for such comparisons.

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Intrusion detection system by improved preprocessing methods and Naïve Bayes classifier using NSL-KDD 99 Dataset

Cited by 32 publications

References 10 publications

Machine Learning in Network Security Using KNIME Analytics

Machine Learning in Network Security Using KNIME Analytics

An approach for efficient intrusion detection for KDD dataset: A survey

Benchmarks for Evaluating Anomaly Based Intrusion Detection Solutions

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