Intrusion Detection System Using Feature Selection and Classification Technique

In the present state of affairs, the motive behind every educational organization is to uplift the academic achievement of students. Educational data mining (EDM) is an upward field of research, and it is very helpful for academic institutions to predict the academic performance of the students. Educational datasets are the basis of various predictive models. Quality of these models can be improved by using feature selection (FS). To get the required benefits from the available data, there must be some tools for analysis and prediction. In lieu of the above, machine learning/data mining are most suitable. In educational data mining, for better accuracy of prediction models’ and quality of various educational datasets, feature selection (FS) plays a vital role. Feature selection (FS) algorithms abolish inappropriate information from the repositories of educational background so that performance of classifier in terms of accuracy could be increased and the same could be used for better decision. In lieu of the above, a best feature selection algorithm must be selected. In this paper, two filter selection approaches namely correlation feature selection (CFS) and wrapper-based feature selection have been used to demonstrate the importance of selection of a feature subset for a classification problem. The present paper aims to find the detailed investigation of filter feature selection algorithms along with the classification algorithms on a given dataset. We found result with numerous numbers of features from various Feature selection algorithms and classifiers which will help the researcher to discover the most excellent mixture of filter feature selection algorithms and its associated classifiers. The result indicates that SMO and J48 have the highest accuracy measures with the correlation feature selection algorithms, while Naïve Bayes has the highest accuracy measures with the wrapper subset feature selection algorithms for predicting high, medium and low grade for the students.

New hybrid data mining model for credit scoring based on feature selection algorithm and ensemble classifiers

Feature selection attracted much interest from researchers in many fields such as network security, pattern recognition and data mining. In this paper, we present a wrapper-based feature selection algorithm aiming at modeling lightweight intrusion detection system (IDS) by (1) using modified random mutation hill climbing (MRMHC) as search strategy to specify a candidate subset for evaluation; (2) using support vector machines (SVMs) as wrapper approach to obtain the optimum feature subset. We have examined the feasibility of our feature selection algorithm by conducting several experiments on KDD 1999 intrusion detection dataset which was categorized as DOS, PROBE, R2L and U2R. The experimental results show that our approach is able not only to speed up the process of selecting important features but also to guarantee high detection rates. Furthermore, our experiments indicate that intrusion detection system with a combination of our proposed approach has smaller computational resources than that with GA-SVM which is a popular feature selection algorithm in the field.

An efficient intrusion detection system based on hypergraph - Genetic algorithm for parameter optimization and feature selection in support vector machine

Irrelevant feature in heart disease dataset affects the performance of binary classification model. Consequently, eliminating irrelevant and redundant feature (s) from training set with feature selection algorithm significantly improves the performance of classification model on heart disease detection. Sequential feature selection (SFS) is successful algorithm to improve the performance of classification model on heart disease detection and reduces the computational time complexity. In this study, sequential feature selection (SFS) algorithm is implemented for improving the classifier performance on heart disease detection by removing irrelevant features and training a model on optimal features. Furthermore, exhaustive and permutation based feature selection algorithm are implemented and compared with SFS algorithm. The implemented and existing feature selection algorithms are evaluated using real world Pima Indian heart disease dataset and result appears to prove that the SFS algorithm outperforms as compared to exhaustive and permutation based feature selection algorithm. Overall, the result looks promising and more effective heart disease detection model is developed with accuracy of 99.3%.

Generally, an IDS (10) is responsible to find out the attack and normal behavior analyzing intrusion detection datasets. But sometimes the attacker intelligence causes fail to identify the attack. Because attacker having various methods to attack which cannot be every time identifiable by an IDS. This paper provides a divide and conquers feature reducing and feature selection algorithm to reduce the feature set from a large KDD 99 dataset. Then reduced feature sets are classified on a KDD dataset with the help of the Tanagra data mining tool. So, the proposed algorithm is selected important feature set and classified with maximized classification rate. In every data base have their records with large number of features. Among the feature sets some features may be ir- relevant and the rest of the feature may be important. So, before going to perform any operation on a large dataset, it is very useful to reduce the database into the important feature set. Now the task to identify the important feature sets is more or less difficult. But, many scientists already succeeded to develop their own technique to identify im- portant feature sets. Generally, in intrusion detection system scientists are used KDD dataset which having 41 features for every record with 37 different types of attacks. So, for reducing the feature set we have to find hidden relationship in the data set which may help us to find out the important features. In this paper divide and conquer algorithm is used to find the hidden relationship to select the important feature sets. The proposed algorithm is applied to KDD datasets for both feature selection and classification using the reduced feature sets with the help of Tanagra7 tool. Now-a-days rapid increased of network system needs more security for protecting the unauthorized access to the security of information sharing and access. As, most of the attackers have dynamic behavior, success to access the unauthorized information. To protect the attacker KDD database recorded with taking most of the dynamic behavior of the attacker in TCP dump format and then the datasets are analyzed to detect attack or normal behavior by an intrusion detection system. But an IDS may take long time to analyze the KDD data set, due to large records and 41 features. So, the proposed algorithm selects important feature sets and reducing the overhead of IDS for analyzing the whole KDD dataset. The rest of the paper is kept as, section II, providing some related work, section III, giving divide and conquer algorithm, section IV, providing experimental result, section V, concluding the paper.

This study is aimed at assessing the usefulness of different feature selection and classification methodologies in the context of sleep apnea hypopnea syndrome (SAHS) detection. Feature extraction, selection and classification stages were applied to analyze blood oxygen saturation (SaO2) recordings in order to simplify polysomnography (PSG), the gold standard diagnostic methodology for SAHS. Statistical, spectral and nonlinear measures were computed to compose the initial feature set. Principal component analysis (PCA), forward stepwise feature selection (FSFS) and genetic algorithms (GAs) were applied to select feature subsets. Fisher's linear discriminant (FLD), logistic regression (LR) and support vector machines (SVMs) were applied in the classification stage. Optimum classification algorithms from each combination of these feature selection and classification approaches were prospectively validated on datasets from two independent sleep units. FSFS + LR achieved the highest diagnostic performance using a small feature subset (4 features), reaching 83.2% accuracy in the validation set and 88.7% accuracy in the test set. Similarly, GAs + SVM also achieved high generalization capability using a small number of input features (7 features), with 84.2% accuracy on the validation set and 84.5% accuracy in the test set. Our results suggest that reduced subsets of complementary features (25% to 50% of total features) and classifiers with high generalization ability could provide high-performance screening tools in the context of SAHS.

Detecting cancer at an early stage is an important application, which reduces the risk of death for a cancerous patient. Data from tissues is generated using microarray DNA technology and these data contains genes/attributes of very large number and samples of relatively small number, making it tough to predict the classifier. Hence, several machine learning algorithms with more emphasis on feature selection were proposed to solve the problem of handling large number of genes/attributes. Most of the feature selection (FS) algorithms mentioned in the literature are Supervised learning algorithms. Authors proposed an Unsupervised feature selection algorithm, making it more independent of the class label. Simple Feature ranking algorithm using Single Value Decomposition (SVD)-Entropy is the first step in the proposed feature selection algorithm. SVD-Entropy based method selects attributes independent of the each other, hence reduces complexity involved in multiple association of attributes in large datasets. At second stage, Correlation among attributes is used to remove attributes/features that are highly correlated to each other. Once the features are selected, a logistic regression model for predicting the class label. The model predicts whether its a cancer or non-cancer causing tissue. The proposed algorithm proved to be an efficient approach in terms of accurately predicting the cancer causing tissues. Experiments were carried on three different datasets like Ovarian, Lung and Breast cancer. The accuracy scores achieved for these datasets are 100, 75 and 96.4 percent, respectively, proving the efficiency of the approach.

Feature (or variable) selection is the process of identifying the minimal set of features with the highest predictive performance on the target variable of interest. Numerous feature selection algorithms have been developed over the years, but only few have been implemented in R and made publicly available R as packages while offering few options. The R package MXM offers a variety of feature selection algorithms, and has unique features that make it advantageous over its competitors: a) it contains feature selection algorithms that can treat numerous types of target variables, including continuous, percentages, time to event (survival), binary, nominal, ordinal, clustered, counts, left censored, etc; b) it contains a variety of regression models that can be plugged into the feature selection algorithms (for example with time to event data the user can choose among Cox, Weibull, log logistic or exponential regression); c) it includes an algorithm for detecting multiple solutions (many sets of statistically equivalent features, plain speaking, two features can carry statistically equivalent information when substituting one with the other does not effect the inference or the conclusions); and d) it includes memory efficient algorithms for high volume data, data that cannot be loaded into R (In a 16GB RAM terminal for example, R cannot directly load data of 16GB size. By utilizing the proper package, we load the data and then perform feature selection.). In this paper, we qualitatively compare MXM with other relevant feature selection packages and discuss its advantages and disadvantages. Further, we provide a demonstration of MXM’s algorithms using real high-dimensional data from various applications.

Based on benchmarking various methods and analyzing multiple real scRNA-seq datasets, a computational platform/workflow and a set of tips for best practices are developed for analyzing causal interactions or relationships in single cells.

One of the key challenges of the machine learning (ML) based intrusion detection system (IDS) is the expensive computation time which is largely caused by the redundant, incomplete, and unrelated features contain in the IDS datasets. To overcome such challenges and ensure building efficient and more accurate IDS models, many researchers utilize preprocessing techniques such as normalization and feature selection, and a hybrid modeling approach is typically used. In this work, we propose a hybrid IDS modeling approach with an algorithm for feature selection (FS) and another for building the IDS. The FS method is a wrapper-based FS with a decision tree as the feature evaluator. Five selected ML algorithms are individually used in combination with the proposed FS method to build five IDS models using the UNSW-NB15 dataset. As a baseline, five more IDS models are built, in a single modeling approach, using the full features of the datasets. We evaluate the effectiveness of our proposed method by comparing it with the baseline models and also with state-of-the-art works. Our method achieves the best DR of 97.95% and proved to be quite effective in comparison to state-of-the-art works. We, therefore, recommend its usage especially in IDS modeling with the UNSW-NB15 dataset.

Chapter 14 - Review on hybrid feature selection and classification of microarray gene expression data

Text classification has been highlighted as the key process to organize online texts for better communication in the Digital Media Age. Text classification establishes classification rules based on text features, so the accuracy of feature selection is the basis of text classification. Facing fast-increasing Chinese electronic documents in the digital environment, scholars have accumulated quite a few algorithms for the feature selection for the automatic classification of Chinese texts in recent years. However, discussion about how to adapt existing feature selection algorithms for various types of Chinese texts is still inadequate. To address this, this study proposes three improved feature selection algorithms and tests their performance on different types of Chinese texts. These include an enhanced CHI square with mutual information (MI) algorithm, which simultaneously introduces word frequency and term adjustment (CHMI); a term frequency–CHI square (TF–CHI) algorithm, which enhances weight calculation; and a term frequency–inverse document frequency (TF–IDF) algorithm enhanced with the extreme gradient boosting (XGBoost) algorithm, which improves the algorithm’s ability of word filtering (TF–XGBoost). This study randomly chooses 3000 texts from six different categories of the Sogou news corpus to obtain the confusion matrix and evaluate the performance of the new algorithms with precision and the F1-score. Experimental comparisons are conducted on support vector machine (SVM) and naive Bayes (NB) classifiers. The experimental results demonstrate that the feature selection algorithms proposed in this paper improve performance across various news corpora, although the best feature selection schemes for each type of corpus are different. Further studies of the application of the improved feature selection methods in other languages and the improvement in classifiers are suggested.

Intrusion Detection Systems (IDS) play a critical role in identifying cyber threats and securing network environments. This research proposes an optimized IDS by leveraging the Dragonfly Algorithm (DA) for feature selection and employing Extra Trees (ET) and K-Nearest Neighbors (KNN) for classification. The UNSW-NB15 dataset is used for evaluation, ensuring a robust and realistic assessment. The implementation, conducted in Python, demonstrates that ET, when combined with DA-based feature selection, achieves an accuracy of 100%, highlighting its effectiveness in distinguishing between normal and malicious traffic. The results confirm that intelligent feature selection significantly enhances IDS performance, reducing computational complexity while improving detection accuracy. This study contributes to the advancement of IDS methodologies, offering a highly efficient approach for real-world cybersecurity applications.

The classical linear multi-factor stock selection model is widely used for long-term stock price trend prediction. However, the stock market is chaotic, complex, and dynamic, for which reasons the linear model assumption may be unreasonable, and it is more meaningful to construct a better-integrated stock selection model based on different feature selection and nonlinear stock price trend prediction methods. In this paper, the features are selected by various feature selection algorithms, and the parameters of the machine learning-based stock price trend prediction models are set through time-sliding window cross-validation based on 8-year data of Chinese A-share market. Through the analysis of different integrated models, the model performs best when the random forest algorithm is used for both feature selection and stock price trend prediction. Based on the random forest algorithm, a long-short portfolio is constructed to validate the effectiveness of the best model.