Collaborative IDS Configuration: A Two-Layer Game-Theoretical Approach

Internet of things intrusion detection model and algorithm based on cloud computing and multi-feature extraction extreme learning machine

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.

Chapter 5 - Intrusion Prevention and Detection Systems

The rapid evolution of software-driven functionalities in modern vehicles has led to the availability of advanced driving assistance systems. In tandem with the above developments, the increasing number of sensors in vehicles to support such new features has led to a significant rise in the number of in-vehicle electronic control units (ECUs), network interfaces, and communication protocols. This growing complexity enhances vehicle performance, connectivity, and comfort applications. The increased connectivity to the outside world, however, introduces new securitychallenges. As automotive networks become more interconnected, they are exposed to a wider array of cyber threats that endanger the safety of passengers as well as threaten the loss of their personal and private information. This necessitates the inclusion of robust Intrusion Detection Systems (IDS) in vehicular networks to safeguard vehicle communications. Given the importance of the problem, multiple IDSs have been proposed in the research literature in the past 10 years. However, conventional IDS implementations often impose significant computational overhead, requiring additional ECUs and network elements that can contribute to increased system cost, complexity, and power consumption. In this thesis, the feasibility of consolidating intrusion detection capabilities within critical ECUs is explored to establish an efficient, embedded IDS architecture for modern automotive networks. By integrating IDS functionalities into hybrid ECU architectures, the aim is to mitigate the need for additional hardware peripherals utilised as IDSs while maintaining a robust approach to network security. This thesis focuses on the widely used Controller Area Network (CAN) protocol, which serves as the backbone of in-vehicle communication, enabling real-time and reliabledata exchange among ECUs. To enhance network security, approaches are proposed that integrate quantised machine learning-based IDS models as lightweight, isolated security modules within ECU hardware. This minimises disruptions to standard software tasks while ensuring efficient per-message handling for real-time detection. To evaluate the feasibility of this approach, three key aspects have been investigated: (1) whether IDS models can be lightweight enough for embedded deployment, (2) their generalisability across multiple datasets and unseen attack types, and (3)their ability to support per-message detection at line rates. The proposed IDS models are trained to detect a diverse range of cyber threats, including Denial-of-Service (DoS), Fuzzing, and Spoofing attacks, which pose significant risks to automotive networks. The experimental results demonstrate that the proposed IDSs achieve high detection accuracy across multiple attack vectors while maintaining low per-message processing latency and minimal energy consumption. This ensures compliance with the stringent constraints of real-time automotive environments, making embeddeddeployment of IDSs within a vehicle’s network both practical and efficient. By leveraging the consolidation of IDS capabilities within critical ECUs, my research contributes to the advancement of secure and resilient automotive network architectures, paving the way for enhanced cybersecurity in next-generation vehicles.

Review on intrusion detection using feature selection with machine learning techniques

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

The Internet of Things (IoT) is increasingly deployed at the edge under resource and environmental constraints, which limits the practicality of traditional intrusion detection systems (IDSs) on IoT hardware. This paper presents two IDS configurations. First, we develop a baseline IDS with fixed hyperparameters, achieving 99.20% accuracy and ~0.002 ms/sample inference latency on a desktop machine; this configuration is suitable for high-performance platforms but is not intended for constrained IoT deployment. Second, we propose a lightweight, edge-oriented IDS that applies ANOVA-based filter feature selection and uses a genetic algorithm (GA) for the bounded hyperparameter tuning of the classifier under stratified cross-validation, enabling efficient execution on Raspberry Pi-class devices. The lightweight IDS achieves 98.95% accuracy with ~4.3 ms/sample end-to-end inference latency on Raspberry Pi while detecting both low-volume and high-volume (DoS/DDoS) attacks. Experiments are conducted in a Raspberry Pi-based real lab using an up-to-date mixed-modal dataset combining system/network telemetry and heterogeneous physical sensors. Overall, the proposed framework demonstrates a practical, hardware-aware, and reproducible way to balance detection performance and edge-level latency using established techniques for real-world IoT IDS deployment.

Intrusion detection is a familiar phrase in the information and network security domain. An Intrusion Detection System (IDS) is a device or software that will keep track of the networks, for unlawful movements, and policy breaches that arise within the network. There are different forms of IDS, Host Intrusion Detection System (HIDS) helps in identifying unauthorized activities on the host, Network Intrusion Detection System (NIDS) helps in identifying attacks in the network, whereas Distributed Intrusion Detection System (DIDS) consists of multiple IDS over a large area of network where individual IDS communicates with each other or with the central the authorized central server. The proposed work has a three-layered architecture for DIDS for securing data sharing among different IDS. The bottom layer uses multiple IDS, the fog layer is supported with Blockchain functionality, and the cloud service at the upper layer stores required data permanently for future analysis. The fog computing-based architecture for DIDS tries to implement the application in a scalable and trustless environment using distributed ledger technology. The evaluation of the proposed work is carried out for fog, cloud, and integrated fog-cloud with the Blockchain functionality and without Blockchain functionality in measuring performance metrics related to throughput, service latency, response time, block creation time, and block execution time.

A Rule Status Monitoring Algorithm for Rule-Based Intrusion Detection and Prevention Systems

Intrusion detection has been widely used in many application domains; thus, it has caught significant attention in academic fields these years. Assembled with more and more sub-systems, the network is more vulnerable to multiple attacks aiming at the network security. Compared with the other issues such as complex environment and resources-constrained devices, network security has been the biggest challenge for Internet construction. To deal with this problem, a fundamental measure for safeguarding network security is to select an intrusion detection algorithm. As is known, it is less effective to determine the abnormal behavior as an intrusion and learn the entire scope of the normal behavior with the traditional anomaly-based algorithm for Internet intrusion detection. In this paper, we propose an intrusion-detecting algorithm of shadowed rough-fuzzy clustering based on Mahalanobis distance, named MSRFCM. It adopts dissimilarity measurement of Mahalanobis distance to identify the relevant variables that significantly influence the clustering performance and reduce the error rate in the process of partitioning clusters with high attribute correlation. And shadowed rough-fuzzy clustering (SRFCM) is applied to obtaining real value-approaching prototypes based on iteration and partitioning the data set into more meaningful clusters. Through simulation with the NSL-KDD intrusion data set and three other intrusion data sets, the Mahalanobis distance-based shadowed rough-fuzzy clustering algorithm has improved performance in intrusion detection.

Internet of Medical Things (IoMT) is network of interconnected medical devices (smart watches, pace makers, prosthetics, glucometer, etc.), software applications, and health systems and services. IoMT has successfully addressed many old healthcare problems. But it comes with its drawbacks essentially with patient’s information privacy and security related issues that comes from IoMT architecture. Using obsolete systems can bring security vulnerabilities and draw attacker’s attention emphasizing the need for effective solution to secure and protect the data traffic in IoMT network. Recently, intrusion detection system (IDS) is regarded as an essential security solution for protecting IoMT network. In the past decades, machines learning (ML) algorithms have demonstrated breakthrough results in the field of intrusion detection. Notwithstanding, to our knowledge, there is no work that investigates the power of machines learning algorithms for intrusion detection in IoMT network. This paper aims to fill this gap of knowledge investigating the application of different ML algorithms for intrusion detection in IoMT network. The investigation analysis includes ML algorithms such as K-nearest neighbor, Naïve Bayes, support vector machine, artificial neural network and decision tree. The benchmark dataset, Bot-IoT which is publicly available with comprehensive set of attacks was used to train and test the effectiveness of all ML models considered for investigation. Also, we used comprehensive set of evaluation metrics to compare the power of ML algorithms with regard to their detection accuracy for intrusion in IoMT networks. The outcome of the analysis provides a promising path to identify the best the machine learning approach can be used for building effective IDS that can safeguard IoMT network against malicious activities.

Security becomes the main concern when the resources are shared over a network for many purposes. For ease of use and time saving several services offered by banks and other financial companies are accessible over mobile apps and computers connected with the Internet. Intrusion detection (ID) is the act of detecting actions that attempt to compromise the confidentiality, integrity, or availability of a shared resource over a network. Intrusion detection does not include the prevention of intrusions. A different solution is required for intrusion prevention. The major intrusion detection technique is host-based where major accountabilities are taken by the server itself to detect relevant security attacks. In this paper, an intrusion detection algorithm using data mining is presented. The proposed algorithm is compared with the signature apriori algorithm for performance. The proposed algorithm observed better results. This framework may help to explore new areas of future research in increasing security in the banking and financial sector enabled by an intrusion detection system (IDS).

Intrusion detection is mainly achieved by using optimization algorithms. The need for optimization algorithms for intrusion detection is necessitated by the increasing number of features in audit data, as well as the performance failure of the human-based smart intrusion detection system (IDS) in terms of their prolonged training time and classification accuracy. This article presents an improved intrusion detection technique for binary classification. The proposal is a combination of different optimizers, including Rao optimization algorithm, extreme learning machine (ELM), support vector machine (SVM), and logistic regression (LR) (for feature selection & weighting), as well as a hybrid Rao-SVM algorithm with supervised machine learning (ML) techniques for feature subset selection (FSS). The process of selecting the least number of features without sacrificing the FSS accuracy was considered a multi-objective optimization problem. The algorithm-specific, parameter-less concept of the proposed Rao-SVM was also explored in this study. The KDDCup 99 and CICIDS 2017 were used as the intrusion dataset for the experiments, where significant improvements were noted with the new Rao-SVM compared to the other algorithms. Rao-SVM presented better results than many existing works by reaching 100% accuracy for KDDCup 99 dataset and 97% for CICIDS dataset.

Malware authors are continuously developing crime toolkits. This has led to the situation of zero-day attacks, where malware harm computer systems despite the protection from existing Intrusion Detection Systems (IDSs). We propose an Intelligent Intrusion Detection and Prevention System (IIDPS) approach that combines the Signature based Intrusion Detection system (SIDS), Anomaly based Intrusion Detection System (AIDS) and Response Intrusion Detection System (RIDS). We used a risk assessment approach to determine an appropriate response action against each attack event. We also demonstrated the IIDPS make the detection and prevention of malware more effective.

In this paper, a new learning algorithm for adaptive network intrusion detection using naive Bayesian classifier and decision tree is presented, which performs balance detections and keeps false positives at acceptable level for different types of network attacks, and eliminates redundant attributes as well as contradictory examples from training data that make the detection model complex. The proposed algorithm also addresses some difficulties of data mining such as handling continuous attribute, dealing with missing attribute values, and reducing noise in training data. Due to the large volumes of security audit data as well as the complex and dynamic properties of intrusion behaviours, several data miningbased intrusion detection techniques have been applied to network-based traffic data and host-based data in the last decades. However, there remain various issues needed to be examined towards current intrusion detection systems (IDS). We tested the performance of our proposed algorithm with existing learning algorithms by employing on the KDD99 benchmark intrusion detection dataset. The experimental results prove that the proposed algorithm achieved high detection rates (DR) and significant reduce false positives (FP) for different types of network intrusions using limited computational resources.