Building a Cloud IDS Using an Efficient Feature Selection Method and SVM

Intrusion detection is a significant technique for detecting various attacks in Wireless Mesh Networks (WMNs). Intrusion Detection Systems (IDS) powered by machine learning algorithms play a vital role in enhancing security by actively monitoring data traffic in real-time. The performance of attack detection in IDS is reduced by the existence of noisy and unwanted features in the network data. Hence, extracting Informative Features (IF) is essential for enhancing the effectiveness of the IDS. The proposed approach optimally selects IF from the network dataset, which facilitates in accurately detecting intrusions. Existing methods lack in efficient feature selection, leading to higher computational complexity and lower detection performance. This research aims to enhance IDS accuracy by leveraging the eXtreme Gradient Boosting (XGBoost) algorithm to classify the intrusions in the mesh network of smart grid and the optimal feature selection carried out with the help of Golden Eagle Optimization Algorithm (GEOA). This novel hybrid IDS model enhances the attack detection in WMNs. The proposed IDS’s performance is improved by using a feature selection method based on the GEOA, and then obtained results are compared against evolutionary algorithms-based features selection method. The proposed model of IDS using XGBoost algorithm is tested on a smart grid dataset, and its performance is compared with existing literature works.

A simple and efficient filter feature selection method via document-term matrix unitization

Advances in data collection have resulted in an exponential growth of high-dimensional microarray datasets for binary classification in bioinformatics and medical diagnostics. These datasets generally possess many features but relatively few samples, resulting in challenges associated with the “curse of dimensionality”, such as feature redundancy and an elevated risk of overfitting. While traditional feature selection approaches, such as filter-based and wrapper-based approaches, can help to reduce dimensionality, they often struggle to capture feature interactions while adequately preserving model generalization. Therefore, this paper introduces the Adaptive Cluster-Guided Simple, Fast, and Efficient (ACG-SFE) feature selection, a hybrid approach designed to address the challenges of high-dimensional microarray data in binary classification. ACG-SFE enhances the Simple, Fast, and Efficient (SFE) evolutionary feature selection model by integrating hierarchical clustering to dynamically group correlated features based on the optimal number of clusters determined by the Silhouette index, Davies-Bouldin score, and the feature-to-observation ratio while adaptively selecting representative features within clusters using mutual information and adjusting the selection threshold through a progress factor. This hybrid filter-wrapper approach improves feature interactions, effectively minimizing redundancy and overfitting while enhancing classification performance. The proposed model is assessed against four state-of-the-art evolutionary feature selection models on 11 high-dimensional microarray datasets. Experimental results indicate that ACG-SFE effectively selects a small yet pertinent feature subset, minimizing redundancy while attaining enhanced classification accuracy and F-measure. Furthermore, its reduced RMSE between train and test accuracy substantiates its capability to reduce overfitting, outperforming comparative models. These findings establish ACG-SFE as an effective feature selection model for handling high-dimensional microarray data in binary classification, enhancing classification accuracy while selecting minimal relevant features to reduce unnecessary complexity and the risk of overfitting.

The intrusion detection system (IDS) deals with huge amount of data which contains irrelevant and redundant features causing slow training and testing process, higher resource consumption as well as poor detection rate. To overcome these limitations, we introduce the concept of lightweight IDS. The lightweight IDSs are small, powerful, and flexible enough to be used as permanent elements of the network security infrastructure. In this paper, we propose a novel concept for building lightweight IDS based on two different approaches. The first approach is using a features selection approach by applying fuzzy enhanced support vector decision function (Fuzzy ESVDF) algorithm. This approach is able to improve system efficiency. The second approach is using IDS classification scheme. The IDS classification scheme divides the detection process into four types according to the TCP/IP network model (application layer IDS, transport layer IDS, network layer IDS, and link layer IDS). This IDS classification scheme enhances an organizationpsilas ability to detect most types of attack (improving system accuracy and generality). Also, it improves IDS scalability and extendibility. To design the proposed system, several experiments have been conducted, and they indicate that the proposed lightweight IDS can deliver satisfactory system performance.

Processing huge amounts of network data is one of the largest challenges for network-based intrusion detection system (IDS). Usually these data contain lots of irrelevant or redundant features. To improve the efficiency of IDS, relevant features are necessary to be extracted from original data via feature selection approaches. In this paper, an effective feature selection approach based on Bayesian Network classifier is proposed. And with the same intrusion detection benchmark dataset (NSL-KDD), the performance of the proposed approach is evaluated and compared with other commonly used feature selection methods. It is shown by empirical results that features selected by our approach have decreased the time to detect attacks and increased the classification accuracy as well as the true positive rates significantly.

Information system is vital for any company. However, the opening to the outside world makes the computer system more vulnerable to attack. It is essential to protect it. Intrusion Detection System (IDS) is an auditing mechanism that analyzes the traffic system and applications to identify normal use of the system and an intrusion attempt and also it prevent security managers. Despite the advantages of IDS, they suffer from a few problems. The major problem in the field of intrusion detection is the classification problem. Genetic Fuzzy System (GFS) are models capable of integrating accuracy and high comprehensibility in their results. They have been widely employed to solve classification problems. In this paper, we use a new GFS model called Genetic Programming Fuzzy Inference System for Classification (GPFIS-Class). It based on Multi-Gene Genetic Programming (MGGP). This model is not used in the intrusion detection area. We use an efficient feature selection method to eliminate data redundancy and irrelevant features in order to analyze the huge data namely the NSL-KDD data set.

A NSGA2-LR wrapper approach for feature selection in network intrusion detection

The intrusion detection system has been widely studied and deployed by researchers for providing better security to computer networks. The increasing volume of attacks, com-bined with the rapid improvement of machine learning (ML) has made the collaboration of intrusion detection techniques with machine learning and deep learnings are a popular subject and a feasible approach for cyber threat protection. Machine learning usually involves the training process using huge sample data. Since the huge input data may cause a negative effect on the training and detection performance of the machine learning model, feature selection becomes a crucial technique to rule out the irrelevant and redundant features from the dataset. This study applied a feature selection approach for intrusion detection that incorporated state-of-the-art feature selection algorithms with attack characteristic feature to produce an optimized set of features for the machine learning algorithms, which was then used to train the machine learning model. CSECIC- IDS2018 dataset, the most recent benchmark dataset with a wide attack diversity and features have been used to create the efficient feature subset. The result of the experiment was produced using machine learning models with a decision tree classifier and analyzed with respect to the accuracy, precision, recall, and f1 score.

The novelty and growing sophistication of cyber threats mean that high accuracy and interpretable machine learning models are needed more than ever before for Intrusion Detection and Prevention Systems. This study aims to solve this challenge by applying Explainable AI techniques, including Shapley Additive explanations feature selection, to improve model performance, robustness, and transparency. The method systematically employs different classifiers and proposes a new hybrid method called Hybrid Bagging-Boosting and Boosting on Residuals. Then, performance is taken in four steps: the multistep evaluation of hybrid ensemble learning methods for binary classification and fine-tuning of performance; feature selection using Shapley Additive explanations values retraining the hybrid model for better performance and reducing overfitting; the generalization of the proposed model for multiclass classification; and the evaluation using standard information metrics such as accuracy, precision, recall, and F1-score. Key results indicate that the proposed methods outperform state-of-the-art algorithms, achieving a peak accuracy of 98.47% and an F1 score of 96.19%. These improvements stem from advanced feature selection and resampling techniques, enhancing model accuracy and balancing precision and recall. Integrating Shapley Additive explanations-based feature selection with hybrid ensemble methods significantly boosts the predictive and explanatory power of Intrusion Detection and Prevention Systems, addressing common pitfalls in traditional cybersecurity models. This study paves the way for further research on statistical innovations to enhance Intrusion Detection and Prevention Systems performance.

Graph technology emerges as an important topic in the field of data mining and machine learning community. The analysis of high-dimensional data is crucial to identify a smaller subset of features which are informative for classification and clustering. In this paper, an efficient graph feature selection method is proposed to render the analysis of high-dimensional data tractable. Here, the feature scores are calculated for obtaining the weights of the edges in the weighted graph to identify the optimal feature subset. One advantage of this method is that it can successfully identify the optimal features for machine learning. The experimental results on our dataset verify the effectiveness and efficiency of the proposed method.

Recent advances and emerging challenges of feature selection in the context of big data

Several new challenges as well as specialized difficulties are getting accumulated for big data that are against both scholarly research groups as well as and business IT sending. The rich big data sources are set up on information streams as well as the dimensionality scourge. It is difficult to precisely assess these big data for decision making systems. In the recent times, several domains are handling big datasets in which there is large number of additional features. The main aim of feature selection techniques is to eliminate noisy, redundant, or unrelated features that cause poor classification performance. This research implements the Feature selection employing Information Gain, Bacterial Foraging Optimization (BFO) as well as Hybrid BFO to compute on big data. Outcomes on various data sets reveal that the suggested Naive Bayes, KNN method performs better when compared to the method analyzed in the literature.

Orthogonally constrained matrix factorization for robust unsupervised feature selection with local preserving

Network intrusion detection system are significant features that contribute to the enterprises and organization network success. In the past decade, Intrusion Detection system (IDS) using several methods have been proposed and implemented to safeguard the networks within an organization to be reliable, secure, and readily available. Although, some of these methods are built utilizing machine learning concepts. IDS utilizing machine learning techniques are accurate, effective and efficient in spotting attacks. However, the machine learning model performance reduces with high dimensional intrusion dataset. Therefore, it is sacrosanct to implement an efficient feature selection technique that can generate a great impact on the classification stage. Also, the feature selection phase is the most serious phase in IDS based on machine learning. This phase is expensive both in time and efforts. Furthermore, most of the proposed machine learning intrusion detection system suffered from low detection accuracy and high false positive rate when the models are experimented on high dimensional dataset. The aim of this paper is to propose a binary firefly algorithm (BFFA) based feature selection for IDS. We first performed normalization in the first stage of the model. Subsequently, the BFFA algorithm was used for feature selection stage. We adopted random forest algorithm for the classification phase. The experiment was performed on high dimensional University of New South Wales-NB 2015 (UNSW-NB15) dataset with seventy five percent of the data used for training the model and twenty percent for testing. The findings showed an accuracy of 99.72%, detection rate of 99.84%, precision of 99.27%, recall of 99.84% and F-score of 99.56%. The results were gauge with the state-of-the-art results and our results were found outstanding.

An efficient wavelet-based feature selection (FS) method is proposed in this paper for subject recognition using ground reaction force measurements. Our approach relies on a local fuzzy evaluation measure with respect to patterns that reveal the adequacy of data coverage for each feature. Furthermore, FS is driven by a fuzzy complementary criterion (FuzCoC) which assures that those features are iteratively introduced, providing the maximum additional contribution with regard to the information content given by the previously selected features. On the basis of the principles of FuzCoC, we develop two novel techniques. At Stage 1, wavelet packet (WP) decomposition of gaits is accomplished to obtain a set of discriminating frequency sub-bands. A computationally simple FS method is then applied at Stage 2, providing a compact set of powerful and complementary features, from WP coefficients. The quality of our approach is validated via comparative analysis against existing methods on gait recognition.