Feature Selection Using Euclidean Distance and Cosine Similarity for Intrusion Detection Model

Suebsing, Anirut; Hiransakolwong, Nualsawat

doi:10.1109/aciids.2009.23

Cited by 16 publications

(3 citation statements)

References 7 publications

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“…Alguns dos algoritmos que podem ser utilizados para o papel de função de similaridade são a distância Euclideana [45], a função de Levenshtein [45], a similaridade de cosseno [47] e a baseada em compressão [45]. Estes algoritmos são compatíveis com o formato de pares de atributo-valor utilizado na representação de casos, deste modo, a função de similaridade pode ser aplicada para identificar se os atributos de ambos são semelhantes ou não [46,38].…”

Section: Recuperação De Casosunclassified

“…Desta forma, identificou-se a necessidade de uma alternativa com maior eficácia para o cálculo de similaridade entre dados numéricos. A função de similaridade de cosseno apresenta características que se encaixam neste requisito [47] e acabou por ser utilizada. O algoritmo de similaridade de cossenoé aplicado para avaliar a proximidade de dois vetores de dados numéricos.…”

Section: Implementação Do Protótipounclassified

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Vulnerability Identification on GNU/Linux Operating Systems through Case-Based Reasoning

Santos

Nobre

2019

RITA

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Operating system security has been steadily evolving over the years. Several mechanisms, softwares and guides of best practices of configuration have been developed to contribute with the security of such systems. The process that makes an operating system safer by considering the default level obtained at the installation is known as hardening. Experience and technical knowledge are important attributes for the professional performing this process. In this context, automated rule-based tools are often used to assist professionals with little experience in vulnerability identification activities. However, the use of rules establishes a dependency on developers for the development of new rules as well as to keep them updated. Failure to update rules can significantly compromise the integrity of vulnerability identification results. In this paper, the Case-Based Reasoning (CBR) technique is used to improve tools that assist inexperienced professionals in conducting vulnerability identification activities. The purpose of using CBR is to make inexperienced professionals obtain similar results as experienced professionals. In addition, the dependence on rule developers is diminished. A prototype was developed considering the GNU/Linux system in order to carry out an experimental evaluation. This evaluation demonstrated that the application of CBR improves the performance of inexperienced professionals in terms of the number of identified vulnerabilities.

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Section: Recuperação De Casosunclassified

Section: Implementação Do Protótipounclassified

Vulnerability Identification on GNU/Linux Operating Systems through Case-Based Reasoning

Santos

Nobre

2019

RITA

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“…Euclidean Based Feature Selection for Network Intrusion Detection (Suebsing and Hiransakolwong, 2009) applies Euclidean distance for selecting a subset of robust features using smaller storage space and getting higher intrusion detection performance. Three different test data sets are used to weigh up the management of the proposed technique.…”

Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Feature Subset Selection using Metrics and Forward Selection

Bibi¹,

Banu²

2015

RJASET

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The aim of this study is to design a Feature Subset Selection Technique that speeds up the Feature Selection (FS) process in high dimensional datasets with reduced computational cost and great efficiency. FS has become the focus of much research on decision support system areas for which data with tremendous number of variables are analyzed. Filters and wrappers are proposed techniques for the feature subset selection process. Filters make use of association based approach but wrappers adopt classification algorithms to identify important features. Filter method lacks the ability of minimization of simplification error while wrapper method burden weighty computational resource. To pull through these difficulties, a hybrid approach is proposed combining both filters and wrappers. Filter approach uses a permutation of ranker search methods and a wrapper which improves the learning accurateness and obtains a lessening in the memory requirements and finishing time. The UCI machine learning repository was chosen to experiment the approach. The classification accuracy resulted from our approach proves to be higher.

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Context adaptive ensemble classification mechanism with multi‐criteria decision making for network intrusion detection

Devprasad¹,

Ramanujam

Rajendran³

2022

Concurrency and Computation

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Nowadays, intrusion detection systems (IDSs) enabled with computational intelligence for electing the suitable classifiers to learn the patterns of various types of network attacks are under study. Selection mechanism of single or ensemble classifiers based on performance and ranking is to be enabled in the IDS framework to make accurate pattern predictions. A new anomaly based network intrusion detection system framework is proposed using context adaptive classification through the technique for order of preference by similarity to ideal solution (TOPSIS) ranking mechanism with hierarchy based chi-square and bat algorithms for feature selection.Parameters like accuracy, false positive rate (FPR), and classification model building time are explored for choosing the best and situation aware classifiers using TOPSIS.For experimentation, NSL-KDD and UNSW-NB15 datasets are used. The proposed system opts decision tree (DT) algorithm for NSL-KDD and produces 98.77% accuracy, 0.03% FPR with 8 features. Ensemble learner is selected for the UNSW-NB15 dataset using DT and support vector machine classifiers with 9 features. Combined classifier predicts 89.43% accuracy and 3.215% FPR. Experimentation on the proposed methodology with the state of the art approaches produces promising results.

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Feature Selection Using Euclidean Distance and Cosine Similarity for Intrusion Detection Model

Cited by 16 publications

References 7 publications

Vulnerability Identification on GNU/Linux Operating Systems through Case-Based Reasoning

Vulnerability Identification on GNU/Linux Operating Systems through Case-Based Reasoning

A Hybrid Feature Subset Selection using Metrics and Forward Selection

Context adaptive ensemble classification mechanism with multi‐criteria decision making for network intrusion detection

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