Metaheuristic Based IDS Using Multi-objective Wrapper Feature Selection and Neural Network Classification

Diabetes Mellitus (DM) is a global health challenge, and accurate early detection is critical for effective management. The study explores the potential of machine learning for improved diabetes prediction using microarray gene expression data and PIMA data set. Researchers utilizing a hybrid feature extraction method such as Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) followed by metaheuristic feature selection algorithms as Harmonic Search (HS), Dragonfly Algorithm (DFA), Elephant Herding Algorithm (EHA). Evaluated the performance of a system by using the following classifiers as Non-Linear Regression—NLR, Linear Regression—LR, Gaussian Mixture Model—GMM, Expectation Maximization—EM, Bayesian Linear Discriminant Analysis—BLDA, Softmax Discriminant Classifier—SDC, and Support Vector Machine with Radial Basis Function kernel—SVM-RBF classifier on two publicly available datasets namely the Nordic Islet Transplant Program (NITP) and the PIMA Indian Diabetes Dataset (PIDD). The findings demonstrate significant improvement in classification accuracy compared to using all genes. On the Nordic islet transplant dataset, the combined ABC-PSO feature extraction with EHO feature selection achieved the highest accuracy of 97.14%, surpassing the 94.28% accuracy obtained with ABC alone and EHO selection. Similarly, on the PIMA Indian diabetes dataset, the ABC-PSO and EHO combination achieved the best accuracy of 98.13%, exceeding the 95.45% accuracy with ABC and DFA selection. These results highlight the effectiveness of our proposed approach in identifying the most informative features for accurate diabetes prediction. It is observed that the parametric values attained for the datasets are almost similar. Therefore, this research indicates the robustness of the FE and FS along with classifier techniques with two different datasets.

The Spiking Neural Network (SNN) model is a third-generation neural network model that uses spike pulse trains to detect and classify electrical problems in the electrical equipment under consideration. To diagnose the thermal issue in the early stages, it is required to evaluate and monitor the electrical components. The detection method in Infrared Thermography (IRT) is hybrid optimization, which is a Dragonfly Algorithm (DA)–Ant Colony Optimization (ACO) technique that produces a higher exploration and exploitation rate while moving toward an optimal solution with a higher convergence rate. The innovative hybrid DA–ACO method used in this study aims to find the best weights for the SNN model while also extracting the most useful characteristics for defect detection in electrical equipment. To improve classification accuracy, the optimum features picked using the created hybrid DA–ACO are provided to the SNN model. Based on conventional and other optimization methods, the proposed method provided superior results from the execution results.

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.

Intrusion Detection System (IDS) is considered as one of the essential components of a secure network. Due to the high number of network packet features, one of the major problems of IDS is false intrusion alerts and low intrusion detection rates. Feature selection removes all redundant or irrelevant features among the various features of network packets. For this reason, it plays a pivotal role in overcoming the mentioned problems and can improve the accuracy of intrusion detection system. In this paper, a new human-inspired optimization algorithm called coronavirus herd immunity optimizer (CHIO) is proposed for feature selection in IDS. CHIO is able to select the optimal subset of features from numerous features without affecting system performance. In order to select the feature, two types of classifiers, K-Nearest Neighbor (KNN) and Artificial Neural Network (ANN), are used to obtain the accuracy of intrusion detection. In addition, the ANN classifier is trained with the classic Gradient Descent (GD) method as well as the two intelligent methods Artificial Bee Colony (ABC) and Harmony Search (HS). In order to demonstrate the performance, our method is tested on 20% of NSL-KDD and its results are reported and compared to other studies. The proposed method has been able to achieve better performance in terms of intrusion detection accuracy and number of features compared to similar works.

In this paper, a new intrusion detection system (IDS) is presented to deal with distributed denial of service (DDoS) attacks. A combined algorithm based on Harris Hawks Optimization (HHO) and Dragonfly Algorithm (DA) is proposed to select relevant features and eliminate irrelevant and redundant features from the NSL-KDD dataset. The extracted features are presented to a multilayer perceptron (MLP) neural network. This network (as a classifier) divides the network traffic into two classes, normal and attack categories. Performance of the proposed model is evaluated with two standard and widely-used datasets in the field of intrusion detection: NSL-KDD and UNSW-NB15. The results of the simulations clearly show the superiority of the proposed method compared to the previous methods in terms of critical evaluation criteria such as accuracy, precision, recall, and F-Measure. Specifically, the proposed method exhibited improvements of 96.9%, 97.6%, 96%, and 96.8% in these metrics, respectively (compared to the baseline method). The main reason for these improvements is the ability of the combined algorithm to intelligently select the optimal features and reduce the dimensions of the data. This careful selection of features allows the MLP neural network to focus on critical information, increasing the classification accuracy and ultimately improving the performance of the intrusion detection system. This research showed that combining optimization algorithms and machine learning works well. So, it is effective for tackling DDoS attacks. It can lead to better intrusion detection systems. These systems will be more efficient and accurate.

Genetic-fuzzy rule mining approach and evaluation of feature selection techniques for anomaly intrusion detection

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.

Sentimental Analysis (SA) becomes a familiar topic among business people, which is commonly applied for the classification of sentiments from online reviews. It is generally treated as a sentiment classification (SC) problem where the online reviews are categorized into positive or negative polarities using the words that exist in the online reviews. With this motivation, this paper presents a new K-means clustering with hybrid metaheuristic algorithm (KMC-HMA) for SA and classification. The proposed KMC-HMA technique initially performs data preprocessing to remove the unwanted words from the product reviews. In addition, K-means clustering technique is used for the clustering of the massive quantity of the applied product reviews. Moreover, the clustered data are fed into the classification model based on hybrid ant colony optimization (ACO) with dragonfly algorithm (DFA). The ACO algorithm is used for the classification of product reviews and the performance of the ACO algorithm can be optimally tuned by the use of DFA. The performance validation of the KMC-HMA technique is validated using two datasets such as Canon and ipod. The experimental values pointed out the superior performance of the KMC-HMA technique over the recent state of art techniques.

With the continuous development of computer networks, the security of the network has become increasingly prominent. A major threat to network security is the intrusion of information systems through the network. Intrusion detection of the traditional intrusion detection and alarm technology is not sufficient. Based on neural network technology, this paper studies the intrusion detection and alarm correlation technology. Based on the research on the working principle and workflow of the existing intrusion detection system, a new neural network-based intrusion detection and alarm method is proposed. A neural network-based intrusion detection and alarm system is designed and implemented. Through the experiment of the system prototype, the results show that the intrusion detection and alarm system based on the neural network has a higher detection rate and a lower false alarm rate for intrusion behaviors such as denial of service attack and has higher detection ability for unknown attack behaviors.

Damage to Retinal Nerve Fiber Layer can Cause Glaucoma. Glaucoma is an inflammation of the optic eye which is characterized by progressive deterioration of Optic Nerve Head and field of view. Problems that require a classification solution are hindered by the large data dimensions. Artificial Bee Colony is one of the evolution algorithms widely used for feature selection and optimization. Gray level Coocurrence matrix is used as a feature extraction method, the Artificial bee colony method is used as a feature selection and Support Vector Machine used as Classification. The proposed method using Artificial Bee Colony gets improved Accuracy compared to method without using Artificial Bee Colony. The results obtained by the proposed method were 95% for accuracy, 95.9% for specificity and 93.7% for sensitivity where methods that did not use Artificial Bee Colony obtained an accuracy of 93.8%, Specificity sebsar 90.3% and Sensitivity of 92.6%.

Using multiple classifiers for predicting the risk of endovascular aortic aneurysm repair re-intervention through hybrid feature selection.

The advancements in network technology and associated services have increased the volume of data traffic. However, the harmful impact caused by intruders has also increased. Intrusion activities can be addressed by utilizing Network Intrusion Prevention Systems (NIPS). NIPS can detect attacks by either comparing the traffic with predefined patterns or identifying deviations from normal traffic behavior. In this paper, the machine learning algorithms will be used to enhance the performance of the NIPS systems. First, feature selection algorithms will be used to remove irrelevant features from the network traffic data. In particular, the widely used Dragonfly Algorithm (DA) will be used for feature selection. Then, the Gradient-Boosted Trees (GBT) and Logistic Regression (LR) will be used to find the attack data based on the features selected by the DA algorithm. The UNSWNB15 dataset was used to assess the proposed NIPS system. The accuracy of GBT and LR reaches 100 % and 99.93 %, respectively.

Predicting heat transfer rate of a ribbed triple-tube heat exchanger working with nanofluid using neural network enhanced by advanced optimization algorithms

Devices, sensors, and physical items are all part of what is known as the Internet of Things (IoT) embedded with technology that allows them to gather, exchange, and act upon information without human intervention. Security in IoT is essential to secure sensitive information, safeguard interconnected networks and devices from cyberattacks, and ensure the trustworthiness and reliability of IoT environment, thereby alleviating risks of privacy breaches, potential physical harm, and financial losses. IoT security includes authentication, encryption, access control, and intrusion detection measures to alleviate security attacks and protect against potential vulnerabilities. One efficient method of addressing security concerns in an Internet of Things (IoT) setting is to implement a system for intrusion detection (IDS), which employs ML and DL approaches to fortify protections. Therefore, this study introduces a new Spring Search Algorithm based on Feature Selection with Optimal Machine Learning Empowered Intrusion Detection (SSAFS-OMLID) in IoT framework. The projected SSAFS-OMLID model intends to detect the occurrence and recognition of intrusions in the IoT framework. Data pre-processing is the first step in preparing the data for analysis. After the data has been pre-processed, the SSAFS model is employed to pick features. Then, hop field neural network (HNN) classifier is exploited for the intrusion detection in the network. Finally, Sooty Tern Optimization Algorithm (STOA) is utilized to effectually adjust the parameters included in the HNN performance. In order to prove that the SSAFS-OMLID method is the best, researchers conducted extensive experiments, and the results showed that the method is very efficient according to several criteria.

An improved feature selection approach using global best guided Gaussian artificial bee colony for EMG classification