A Feed-Forward and Pattern Recognition ANN Model for Network Intrusion Detection

Iqbal, Ahmed; Aftab, Shabib

doi:10.5815/ijcnis.2019.04.03

Cited by 36 publications

(24 citation statements)

References 25 publications

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“…Matthew's Correlation Coefficient (MCC) is defined as a ratio of the observed and predicted binary classifications and ranges from -1 to +1. The results closer to 1 depicts the good prediction whereas closer to or below 0 indicates the bad performance [24], [32].…”

Section: Tp Tn Accuracy Tp Tn Fp Fnmentioning

confidence: 99%

Performance Analysis of Machine Learning Techniques on Software Defect Prediction using NASA Datasets

Iqbal¹,

Aftab²,

Ali³

et al. 2019

IJACSA

Self Cite

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Defect prediction at early stages of software development life cycle is a crucial activity of quality assurance process and has been broadly studied in the last two decades. The early prediction of defective modules in developing software can help the development team to utilize the available resources efficiently and effectively to deliver high quality software product in limited time. Until now, many researchers have developed defect prediction models by using machine learning and statistical techniques. Machine learning approach is an effective way to identify the defective modules, which works by extracting the hidden patterns among software attributes. In this study, several machine learning classification techniques are used to predict the software defects in twelve widely used NASA datasets. The classification techniques include: Naïve Bayes (NB), Multi-Layer Perceptron (MLP). Radial Basis Function (RBF), Support Vector Machine (SVM), K Nearest Neighbor (KNN), kStar (K*), One Rule (OneR), PART, Decision Tree (DT), and Random Forest (RF). Performance of used classification techniques is evaluated by using various measures such as: Precision, Recall, F-Measure, Accuracy, MCC, and ROC Area. The detailed results in this research can be used as a baseline for other researches so that any claim regarding the improvement in prediction through any new technique, model or framework can be compared and verified.

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Section: Tp Tn Accuracy Tp Tn Fp Fnmentioning

confidence: 99%

Performance Analysis of Machine Learning Techniques on Software Defect Prediction using NASA Datasets

Iqbal¹,

Aftab²,

Ali³

et al. 2019

IJACSA

Self Cite

View full text Add to dashboard Cite

show abstract

“…Many researchers have used machine learning techniques to resolve the classification problems in various areas including: sentiment analysis [11,12,13,14,15,16], network intrusion detection [17] "in press" [18], [19], rainfall prediction [20,21], and software defect prediction [10], [29] etc.. Some selected studies regarding the software defect predictions are discussed here briefly.…”

Section: Related Workmentioning

confidence: 99%

A Feature Selection based Ensemble Classification Framework for Software Defect Prediction

Iqbal¹,

Aftab²,

Ullah³

et al. 2019

IJMECS

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Software defect prediction is one of the emerging research areas of software engineering. The prediction of defects at early stage of development process can produce high quality software at lower cost. This research contributes by presenting a feature selection based ensemble classification framework which consists of four stages: 1) Dataset selection, 2) Feature Selection, 3) Classification, and 4) Results. The proposed framework is implemented from two dimensions, one with feature selection and second without feature selection. The performance is evaluated through various measures including: Precision, Recall, F-measure, Accuracy, MCC and ROC. 12 Cleaned publically available NASA datasets are used for experiments. The results of both the dimensions of proposed framework are compared with the other widely used classification techniques such as: "Naïve Bayes (NB), Multi-Layer Perceptron (MLP). Radial Basis Function (RBF), Support Vector Machine (SVM), K Nearest Neighbor (KNN), kStar (K*), One Rule (OneR), PART, Decision Tree (DT), and Random Forest (RF)". Results reflect that the proposed framework outperformed other classification techniques in some of the used datasets however class imbalance issue could not be fully resolved.

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“…Many researchers have used machine learning techniques to solve the binary classification problems such as Sentiment Analysis [1,2,3,4,5,6], Rainfall Prediction [7,8], Network Intrusion Detection [9,10], and Software Defect Prediction [11,12,13,14,15,16]. Some of the studies related to software defect prediction are discussed here.…”

Section: Related Workmentioning

confidence: 99%

Design of (FPID) controller for Automatic Voltage Regulator using Differential Evolution Algorithm

Alawad¹,

Rahman²

2019

IJMECS

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Testing is one of the crucial activities of software development life cycle which ensures the delivery of high quality product. As software testing consumes significant amount of resources so, if, instead of all software modules, only those are thoroughly tested which are likely to be defective then a high quality software can be delivered at lower cost. Software defect prediction, which has now become an essential part of software testing, can achieve this goal. This research presents a framework for software defect prediction by using feature selection and ensemble learning techniques. The framework consists of four stages: 1) Dataset Selection, 2) Pre Processing, 3) Classification, and 4) Reflection of Results. The framework is implemented on six publically available Cleaned NASA MDP datasets and performance is reflected by using various measures including: F-measure, Accuracy, MCC and ROC. First the performance of all search methods within the framework on each dataset is compared with each other and the method with highest score in each performance measure is identified. Secondly, the results of proposed framework with all search methods are compared with the results of 10 well-known supervised classification techniques. The results reflect that the proposed framework outperformed all of other classification techniques.

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A Feed-Forward and Pattern Recognition ANN Model for Network Intrusion Detection

Cited by 36 publications

References 25 publications

Performance Analysis of Machine Learning Techniques on Software Defect Prediction using NASA Datasets

Performance Analysis of Machine Learning Techniques on Software Defect Prediction using NASA Datasets

A Feature Selection based Ensemble Classification Framework for Software Defect Prediction

Design of (FPID) controller for Automatic Voltage Regulator using Differential Evolution Algorithm

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