A Binary Firefly Algorithm Based Feature Selection Method on High Dimensional Intrusion Detection Data

The rapid growth of the Internet of Things (IoT) has introduced considerable security challenges, making Intrusion Detection Systems (IDS) essential for protecting connected devices from cyber threats. This paper presents an Artificial Intelligence (AI)-driven Intrusion Detection System (IDS) designed for IoT environments, utilizing machine learning and anomaly detection techniques. The system leverages the NSL-KDD dataset to distinguish between normal and anomalous network traffic. We implemented multiple feature selection techniques, including correlation-based selection, Recursive Feature Elimination (RFE), and LASSO, to optimize model performance. Various machine learning classifiers, including Random Forest (RF), Bagging, Support Vector Machines (SVM), and K-Nearest Neighbors (KNN), were evaluated using key performance metrics such as accuracy, recall, precision, and AUC-ROC. Experimental results indicate that Random Forest and Bagging outperformed other models, achieving the highest accuracy of 99.89%, with an AUC-ROC of 99.88%, outperforming other models in intrusion detection. Additionally, feature selection methods effectively reduced dataset dimensionality while maintaining high detection performance. Despite these promising results, challenges such as high training time, false positives, and real-time adaptability remain critical concerns for practical deployment in IoT systems.

Blockchain and federated learning-based intrusion detection approaches for edge-enabled industrial IoT networks: a survey

Attacks on networks are currently the most pressing issue confronting modern society. Network risks affect all networks, from small to large. An intrusion detection system must be present for detecting and mitigating hostile attacks inside networks. Machine Learning and Deep Learning are currently used in several sectors, particularly the security of information, to design efficient intrusion detection systems. These systems can quickly and accurately identify threats. However, because malicious threats emerge and evolve regularly, networks need an advanced security solution. Hence, building an intrusion detection system that is both effective and intelligent is one of the most cognizant research issues. There are several public datasets available for research on intrusion detection. Because of the complexity of attacks and the continually evolving detection of an attack method, publicly available intrusion databases must be updated frequently. A convolutional recurrent neural network is employed in this study to construct a deep-learning-based hybrid intrusion detection system that detects attacks over a network. To boost the efficiency of the intrusion detection system and predictability, the convolutional neural network performs the convolution to collect local features, while a deep-layered recurrent neural network extracts the features in the proposed Hybrid Deep-Learning-Based Network Intrusion Detection System (HDLNIDS). Experiments are conducted using publicly accessible benchmark CICIDS-2018 data, to determine the effectiveness of the proposed system. The findings of the research demonstrate that the proposed HDLNIDS outperforms current intrusion detection approaches with an average accuracy of 98.90% in detecting malicious attacks.

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.

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.

Network Intrusion Detection Systems (NIDS) are utilized to find hostile network connections. This can be accomplished by looking at traffic network activity, but it takes a lot of work. The NIDS heavily utilizes approaches for data extraction and machine learning to find anomalies. In terms of feature selection, NIDS is far more effective. This is accurate since anomaly identification uses a number of time-consuming features. Because of this, the feature selection method influences how long it takes to analyze movement patterns and how clear it is. The goal of the study is to provide NIDS with an attribute selection approach. PSO has been used for that purpose. The Network Intrusion Detection System that is being developed will be able to identify any malicious activity in the network or any unusual behavior in the network, allowing the identification of the illegal activities and safeguarding the enormous amounts of confidential data belonging to the customers from being compromised. In the research, datasets were produced utilising both a network infrastructure and a simulation network. Wireshark is used to gather data packets whereas Cisco Packet Tracer is used to build a network in a simulated environment. Additionally, a physical network consisting of six node MCUs connected to a laptop and a mobile hotspot, has been built and communication packets are being recorded using the Wireshark tool. To train several machine learning models, all the datasets that were gathered—created datasets from our own studies as well as some common datasets like NSDL and UNSW acquired from Kaggle—were employed. Additionally, PSO, which is an optimization method, has been used with these ML algorithms for feature selection. In the research, KNN, decision trees, and ANN have all been combined with PSO for a specific case study. And it was found demonstrated the classification methods PSO + ANN outperformed PSO + KNN and PSO + DT in this case study.

Machine learning (ML) and deep learning (DL) are used in numerous fields, particularly to develop effective intrusion detection systems (IDS). Existing wireless network IDS, which rely on a single ML algorithm and have limitations. These include a high rate of false positives, difficulties in recognizing distinct attack patterns, and a high acquisition cost for annotated training datasets. However, hostile threats are always evolving, networks need a smart security solution. In comparison to other ML approaches, DL algorithms are more successful in intrusion detection. This paper presents a DL based ensemble model that combines Multi-verse through Chaotic Atom Search Optimization (MCA) for preprocessing, which eliminates unsolicited/recurrent information in the dataset. The process of optimized feature selection uses Principal Component Analysis (PCA), Chaotic Manta-ray Foraging Optimizations (CMFO), and a grounded grouping method to partition the optimized feature dataset into k-diverse clusters. The recommended model then stacks Support Vector Machine (SVM) as the ensemble model’s meta-learner classifier, pre-training the hybrid DL prototypes using the optimized feature dataset cluster. The CNN-LSTM and CNN-GRU models, which integrate Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU), are the hybrid DL prototype’s key components. The suggested model’s performance has been enhanced and compared to six ML techniques: NB, SVM, J48, RF, MLP, and kNN models, utilizing measures such as accuracy, precision, recall, and F-measure. The public can access the Aegean Wi-Fi Intrusion Dataset (AWID) which is used for evaluating the recommended model and is outperformed the contemporary models in the literature.

As the reliance on digital networks grows, the need for robust security measures becomes paramount. It offers a thorough analysis of creating and assessing a Network Intrusion Detection System (NIDS) incorporating various machine learning algorithms. The selected algorithms consist of K Nearest Neighbour, Naïve Bayes, Decision Tree, Support Vector Machine, and Logistic Regression. Moreover, widely recognised ensemble learning models like CatBoost, AdaBoost, Gradient Boost, Random Forest, and Extreme Gradient Boost. The core objective is to improve the efficiency of intrusion detection by evaluating various machine-learning models. Utilizes feature selection techniques based on chi-square to enhance the relevance and effectiveness of the selected features. The evaluation metrics, particularly Matthews’ Correlation Coefficient (MCC) and ROC Curves, are used in assessing the models’ efficacy. They offer valuable insights into the models’ capability to differentiate between normal and malicious network activities. This study aims to advance cybersecurity by comparing classical machine learning and ensemble models for network intrusion detection. Significantly, our results underscore XGBoost’s effectiveness with 0.996 accuracy and 0.993 MCC, in enhancing digital network security.

Distributed feature selection (DFS) strategy for microarray gene expression data to improve the classification performance

Nature-inspired intrusion detection system for protecting software-defined networks controller

DBFS: An effective Density Based Feature Selection scheme for small sample size and high dimensional imbalanced data sets

Occam's razor in dimension reduction: Using reduced row Echelon form for finding linear independent features in high dimensional microarray datasets

The article gives a general idea of intrusion detection systems (IDS) and machine learning, presents a classification of machine learning methods. The most commonly used data sets for training IDS are considered. The main performance indicators used to assess the IDS are described. A review of modern research on the development of intrusion detection methods using machine learning is made, the strengths and weaknesses of each solution are described. The data sets used in them, the obtained estimates of the effectiveness of the proposed intrusion detection methods, are analyzed. Based on the results of the analysis, development trends, unresolved problems and promising areas of research were identified.

High-dimensional datasets with low sample sizes (HDLSS) are pivotal in the fields of biology and bioinformatics. One of core objective of HDLSS is to select most informative features and discarding redundant or irrelevant features. This is particularly crucial in bioinformatics, where accurate feature (gene) selection can lead to breakthroughs in drug development and provide insights into disease diagnostics. Despite its importance, identifying optimal features is still a significant challenge in HDLSS. To address this challenge, we propose an effective feature selection method that combines gradual permutation filtering with a heuristic tribrid search strategy, specifically tailored for HDLSS contexts. The proposed method considers inter-feature interactions and leverages feature rankings during the search process. In addition, a new performance metric for the HDLSS that evaluates both the number and quality of selected features is suggested. Through the comparison of the benchmark dataset with existing methods, the proposed method reduced the average number of selected features from 37.8 to 5.5 and improved the performance of the prediction model, based on the selected features, from 0.855 to 0.927. The proposed method effectively selects a small number of important features and achieves high prediction performance.

Intrusion detection is a critical component of secure information systems. Current intrusion detection systems (IDS) especially NIDS (Network Intrusion Detection System) examine all data features to detect intrusions. However, some of the features may be redundant or contribute little to the detection process and therefore they have great impact on the system performance. This paper proposes a lightweight intrusion detection model that is computationally efficient and effective based on feature selection and Maximum Entropy (ME) model. Firstly, the issue of identifying important input features is addressed. Since elimination of the insignificant and/or useless inputs leads to a simplification of the problem, therefore results to faster and more accurate detection. Secondly, classic ME model is used to learn and detect intrusions using the selected important features. Experimental results on the well-known KDD 1999 dataset show the proposed model is effective and can be applied to real-time intrusion detection environments.