Feature Selection Strategy for Network Intrusion Detection System (NIDS) Using Meerkat Clan  Algorithm

Muhsen, Atheer R.; Jumaa, Ghazwh G.; Bakri, Nadia F. Al; Sadiq, Ahmed T.

doi:10.3991/ijim.v15i16.24173

Cited by 9 publications

(5 citation statements)

References 23 publications

(31 reference statements)

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“…• The phenomenon of communal decision-making may be observed in meerkats, as they actively participate in the exchange of experiences and ideas in order to achieve a consensus. In a similar vein, the algorithm incorporates a mechanism for collaborative decision-making through the aggregation of inputs from many actors [16]. • There is no information in the user's text that has to be rewritten in an academic way.…”

Section: Meerkat Clan Algorithm (Mca)mentioning

confidence: 99%

Honeywords Generation Technique based on Meerkat Clan Algorithm and WordNet

A.Ahmed

2023

wjps

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The efficiency of the Honeywords approach has been proven to be a significant tool for boosting password security. The suggested system utilizes the Meerkat Clan Algorithm (MCA) in conjunction with WordNet to produce honeywords, thereby enhancing the level of password security. The technique of generating honeywords involves data sources from WordNet, which contributes to the improvement of authenticity and diversity in the honeywords. The method encompasses a series of consecutive stages, which include the tokenization of passwords, the formation of alphabet tokens using the Meerkat Clan Algorithm (MCA), the handling of digit tokens, the creation of unique character tokens, and the consolidation of honeywords. The optimization of the performance of the Meerkat Clan Algorithm (MCA) involves the careful selection of parameters. The experimental findings have exhibited noteworthy levels of precision and optimum efficacy, particularly in tasks such as proposing words with similar meanings, forecasting numerical values, and producing distinctive symbols. The attainment of this achievement is facilitated by a confluence of factors, encompassing the caliber of data, the judicious use of algorithms or models, and the ongoing process of iterative improvement to consistently enhance outcomes. In order to achieve the appropriate levels of accuracy and functionality, it is crucial to engage in the process of conducting experiments, thoroughly testing the system, and making necessary improvements. The empirical findings provide confirmation of the effectiveness of the MCA in producing a varied and protected collection of honeywords. This is especially evident in the case of alphabet tokens, which are distinguished by their autonomous creation and strong security characteristics. The analysis of correction rates, specifically in relation to the password "Lion1999*," demonstrates the aforementioned results. This study reveals an average accuracy of honeyword production up to 0.729847632111541. In the same manner, the accuracy of the password "house2000" is determined to be 0.761325846711256. Additionally, when considering a sample of 100 passwords, the mean accuracy of honeyword creation is calculated to be 0.7073897168887518. The findings collectively highlight the effectiveness of the MCA in generating honeywords that possess improved security characteristics.

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Section: Meerkat Clan Algorithm (Mca)mentioning

confidence: 99%

Honeywords Generation Technique based on Meerkat Clan Algorithm and WordNet

A.Ahmed

2023

wjps

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“…By removing redundant, irrelevant, or noisy data, it improves data efficiency and thus learning technique performance. It also improves the correctness of the output model and aids in understanding the underlying operations that generated the data [12,13].…”

Section: Related Workmentioning

confidence: 99%

Optimizing Intrusion Detection Systems in Three Phases on the CSE-CIC-IDS-2018 Dataset

Songma,

Sathuphan,

Pamutha

2023

Computers

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This article examines intrusion detection systems in depth using the CSE-CIC-IDS-2018 dataset. The investigation is divided into three stages: to begin, data cleaning, exploratory data analysis, and data normalization procedures (min-max and Z-score) are used to prepare data for use with various classifiers; second, in order to improve processing speed and reduce model complexity, a combination of principal component analysis (PCA) and random forest (RF) is used to reduce non-significant features by comparing them to the full dataset; finally, machine learning methods (XGBoost, CART, DT, KNN, MLP, RF, LR, and Bayes) are applied to specific features and preprocessing procedures, with the XGBoost, DT, and RF models outperforming the others in terms of both ROC values and CPU runtime. The evaluation concludes with the discovery of an optimal set, which includes PCA and RF feature selection.

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“…Using the entire possible set of predictors and gradually excluding less significant predictors, or starting from significant features and gradually adding new predictors. The successful application of feature selection must not only reflect the important information for prediction but also must reduce the computational and analytical work for the analysis of high-dimensional data [31]. Variety techniques for finding an optimal subset from features were introduced.…”

Section: Two-level Prediction Modelmentioning

confidence: 99%

Object-oriented Model to Predict Crop Yield Using Satellite-based Vegetation Index

Khalil

Abbas

2022

Int. J. Interact. Mob. Technol.

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Different models had been developed to predict crop yields based on remotely sensed data. Most approaches were based on developing empirical relationships between the Satellite based Normalized difference vegetation index NDVI data and the crop yield. This article proposes to introduce methodological framework for constructing an object-oriented yield prediction model using satellite data based on the two-level regression models; with taken into consideration the trend caused by the influence of technological improvements. Regression models for the wheat and barley crop yield predictions have been developed for twenty-seven typical districts in the Voronezh region. The two-level regression model, including FSR technique firstly to select the set of features that reflect the spatial variations in crops, soil, agriculture management within districts, after carry out the steps of exploratory data analysis EDA, objects creation and zonal average of each object. Whereas the second level consist of yield prediction with multiple linear regression MLR, Least Absolute Shrinkage and Selection Operator Lasso, Support vector machines SVM techniques. The absolute error between the predicted and observed yields of 2011-2014 years was not exceed 19(16) centner/ha for wheat(barley) at worst case. In spite of that the SVM technique outperforms rest techniques by average RMSE of 5.59(4.51) for wheat(barley).

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Feature Selection Strategy for Network Intrusion Detection System (NIDS) Using Meerkat Clan Algorithm

Cited by 9 publications

References 23 publications

Honeywords Generation Technique based on Meerkat Clan Algorithm and WordNet

Honeywords Generation Technique based on Meerkat Clan Algorithm and WordNet

Optimizing Intrusion Detection Systems in Three Phases on the CSE-CIC-IDS-2018 Dataset

Object-oriented Model to Predict Crop Yield Using Satellite-based Vegetation Index

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