Accuracy Of Intrusion Detection Research Articles

Reinventing Web Security: An Enhanced Cycle-Consistent Generative Adversarial Network Approach to Intrusion Detection

Web3.0, as the link between the physical and digital domains, faces increasing security threats due to its inherent complexity and openness. Traditional intrusion detection systems (IDSs) encounter formidable challenges in grappling with the multidimensional and nonlinear traffic data characteristic of the Web3.0 environment. Such challenges include insufficient samples of attack data, inadequate feature extraction, and resultant inaccuracies in model classification. Moreover, the scarcity of certain traffic data available for analysis by IDSs impedes the system’s capacity to document instances of malicious behavior. In response to these exigencies, this paper presents a novel approach to Web3.0 intrusion detection, predicated on the utilization of cycle-consistent generative adversarial networks (CycleGANs). Leveraging the data transformation capabilities of its generator, this method facilitates bidirectional conversion between normal Web3.0 behavioral data and potentially intrusive behavioral data. This transformative process not only augments the diversity and volume of recorded intrusive behaviors but also clandestinely simulates various attack scenarios. Furthermore, through fostering mutual competition and learning between the discriminator and generator, the approach enhances the ability to discern the defining characteristics of potential intrusive behaviors, thereby bolstering the accuracy of intrusion detection. To substantiate the efficacy of the CycleGAN-based intrusion detection method, simulation experiments were conducted utilizing public datasets, including KDD CUP 1999 (KDD), CIC-DDOS2019, CIC-IDS2018, and SR-BH 2020. The experimental findings evince the method’s remarkable accuracies across the four datasets, attaining rates of 99.81%, 97.79%, 89.25%, and 95.15%, respectively, while concurrently maintaining low false-positive rates. This research contributes novel insights and methodologies toward the advancement of Web3.0 intrusion detection through the application of CycleGAN technology, which is poised to play a pivotal role in fortifying the security landscape of Web3.0.

An Improved Network Intrusion Detection Method Based On CNN-LSTM-SA

Network intrusion detection is an essential component of contemporary cybersecurity strategies, and the development of efficient techniques to accurately identify malicious activities has become a priority. This study investigates the performance of various conventional machine learning algorithms, including decision trees, naive Bayes, naive Bayes trees, random forest, random trees, MLP, and SVM, in detecting network intrusions using binary and multi-classification approaches. Furthermore, the study proposes a deep learning method, CNN-LSTM-SA, which consistently outperforms conventional machine learning techniques in terms of precision, recall, F1 score, and overall accuracy for network intrusion detection. Specifically, the proposed method combines CNN and LSTM with SA in machine learning theory to extract more optimized, strongly correlated features. The proposed method is evaluated using the benchmark NSL-KDD database. The results indicate that the CNN-LSTM-SA method holds great potential in enhancing the efficacy of network intrusion detection systems.

TICPS: A trustworthy collaborative intrusion detection framework for industrial cyber–physical systems

The networking of industrial cyber–physical systems (CPS) introduces increased security vulnerabilities, necessitating advanced intrusion detection systems (IDS). Many current studies aiming to enhance IDS capabilities leverage Federated Learning (FL) technology for collaborative intrusion detection. However, devices deployed in an industrial setting in a distributed manner are vulnerable to cyber and poisoning attacks. Compromised clients can create malicious parameters to disrupt intrusion detection models, making them ineffective in identifying attacks. Nevertheless, existing FL-based intrusion detection methods exhibit suboptimal performance in detecting malicious clients and resisting poisoning attacks. To address these issues, we propose TICPS, a collaborative intrusion detection framework based on a trustworthy model update strategy to detect cyber threats from industrial CPS. The framework enables multiple industrial CPS to collaboratively construct an intrusion detection model and evaluate the security of each industrial CPS node using an update evaluation mechanism, ensuring effective intrusion detection even in the presence of poisoning. Extensive experiments on real-world industrial CPS datasets demonstrate that TICPS can effectively detect various types of cyber threats targeting industrial CPS. In particular, the framework achieves an intrusion detection accuracy of 94% even when the proportion of malicious agents reaches 80% under three typical poisoning attacks.

Exploring Lightweight Deep Learning Techniques for Intrusion Detection Systems in IoT Networks: A Survey

The proliferation of Internet users has coincided with a commensurate increase in the amount of very important, sensitive, and private information being transferred across the Internet. Malicious actors are increasingly targeting networks to breach them and obtain illegal access to critical information since this trend has revealed holes in security systems. In addition to endangering the privacy of the data concerned, these breaches disrupt the smooth functioning of systems. Therefore, in light of these dangers, intrusion detection systems (IDSs) are now an essential part of any cybersecurity program. The goal of these systems is to detect and report any suspicious activity by constantly monitoring and analyzing network traffic. Numerous review articles have investigated various methods for network intrusion detection. To improve detection accuracy while keeping computing efficiency high, this survey study investigates lightweight deep learning techniques for intrusion detection systems. These techniques include pruning, quantization, clustering, and collaborative optimization. This study analyzes five different types of new real-world traffic datasets (i.e., CSE-CIC- IDS2018, NSL-KDD, Bot-IoT, ToN IoT Network, and UNSW-NB15) and evaluates the performance of several machine learning and deep learning techniques. This survey provides metrics for measuring the accuracy of intrusion detection across various systems, which may be used to assess performance.

Machine learning approach for intrusion detection system using dimensionality reduction

As cyberspace has emerged, security in all the domains like networks, cloud, and databases has become a greater concern in real-time distributed systems. Existing systems for detecting intrusions (IDS) are having challenges coping with constantly changing threats. The proposed model, DR-DBMS (dimensionality reduction in database management systems), creates a unique strategy that combines supervised machine learning algorithms, dimensionality reduction approaches and advanced rule-based classifiers to improve intrusion detection accuracy in terms of different types of attacks. According to simulation results, the DR-DBMS system detected the intrusion attack in 0.07 seconds and with a smaller number of features using the dimensionality reduction and feature selection techniques efficiently.

Meta learning-based few-shot intrusion detection for 5G-enabled industrial internet

AbstractWith the formation and popularization of the 5G-enabled industrial internet, cybersecurity risks are increasing, and the limited number of attack samples, such as zero-day, leaves a short response time for security protectors, making it substantially more difficult to protect industrial control systems from new types of malicious attacks. Traditional supervised intrusion detection models rely on a large number of samples for training and their performance needs to be improved. Therefore, there is an urgent need for few-shot intrusion detection. Aiming at the above problems, this paper proposes a detection model based on a meta-learning framework, which aims to effectively improve the accuracy and real-time performance of intrusion detection, and designs a meta-learning intrusion detection model containing a sample generation module, a feature mapping module and a feature metric module. Among them, the sample generation module introduces the residual block into the Natural GAN and proposes a new method to generate high-quality antagonistic samples—Res-Natural GAN, which is used to enhance the antagonism of the generated samples and the feature mining degree, to improve the accuracy of malicious traffic detection; the feature mapping module proposes a new attention mechanism, the multi-head fast attention mechanism, which is applied to the encoder structure of the transformer and combined with a parameter optimization algorithm based on particle swarm mutation to shorten the mapping time and improve the real-time performance of the model while mapping the features effectively; the feature metric module proposes a prototype structure based on a prototype storage update algorithm and combines it with a prototype network to achieve correct classification by measuring the Euclidean distance between the detected samples and the class of prototypes, and to shorten the inference time while ensuring the detection accuracy; finally, the three modules are combined to form a real-time meta-learning intrusion detection model. To evaluate the proposed model, five different types of experiments are conducted on multiple public datasets. The experimental results show that the model has higher detection accuracy than the traditional model for both few-shot and zero-shot malicious attacks, and is not only applicable to 5G-enabled industrial internet, but also generalized to different network environments and attack types.

Towards unbalanced multiclass intrusion detection with hybrid sampling methods and ensemble classification

Intrusion Detection Systems (IDS) play a crucial role in securing computer networks against malicious activities. However, their efficacy is consistently hindered by the persistent challenge of class imbalance in real-world datasets. While various methods, such as resampling techniques, ensemble methods, cost-sensitive learning, data augmentation, and so on, have individually addressed imbalance classification issues, there exists a notable gap in the literature for effective hybrid methodologies aimed at enhancing IDS performance. To bridge this gap, our research introduces an innovative methodology that integrates hybrid undersampling and oversampling strategies within an ensemble classification framework. This novel approach is designed to harmonize dataset distributions and optimize IDS performance, particularly in intricate multi-class scenarios. In-depth evaluations were conducted using well-established intrusion detection datasets, including the Car Hacking: Attack and Defense Challenge 2020 (CHADC2020) and IoTID20. Our results showcase the remarkable efficacy of the proposed methodology, revealing significant improvements in precision, recall, and F1-score metrics. Notably, the hybrid-ensemble method demonstrated an exemplary average F1 score exceeding 98% for both datasets, underscoring its exceptional capability to substantially enhance intrusion detection accuracy. In summary, this research represents a significant contribution to the field of IDS, providing a robust solution to the pervasive challenge of class imbalance. The hybrid framework not only strengthens IDS efficacy but also illuminates the seamless integration of undersampling and oversampling within ensemble classifiers, paving the way for fortified network defenses.

Auto metric graph neural network optimized with woodpecker mating algorithm for detecting network layer attacks in mobile ad hoc networks

ABSTRACT Nowadays, Mobile Ad hoc Network (MANET) gains more attraction due to its seamless and dynamic use. The existing methods in MANET are plagued by some weaknesses, like higher computation time, undermining their real-time responsiveness. Whereas, lower accuracy in intrusion detection compromises the reliability of these systems, potentially leading to missed threats posing a challenge for effective security measures in dynamic MANET environments. To mitigate these weaknesses, an Auto Metric Graph Neural Network optimized with Woodpecker Mating Algorithm is proposed in this paper for detecting Network Layer Attack in MANET (AGNN-WMA-DNLA-MANET). Initially, the data are gathered from CIC-IDS 2019 dataset. The gathered data undergoes pre-processing to eliminate redundancy and missing values replacement using the Local Least Squares method. Afterward, the preprocessing output is fed to AGNN for classification, which classifies the attack as active, passive, or normal. Then, Woodpecker Mating Algorithm (WMA) is proposed to optimize the AGNN weight parameters to ensure the accurate classification. The proposed technique is executed in NS2 tool. The metrics, like accuracy, false positive rate (FPR) and attack detection rate (ADR), computation time, and RoC is analyzed. The proposed AGNN-WMA-DNLA-MANET approach provides 14.68%, 7.142%, and 4.65% higher accuracy for active attack, 15.19%, 2.77%, and 9.85% higher accuracy for passive attack and 38.18%, 12.02%, and 7.59% better accuracy for normal compared with existing methods, such as Dependable intrusion detection scheme under deep convolutional neural network (DCNN-MANET), deep learning-based intrusion detection scheme for MANET (AE-DNN-MANET), and Benchmarking of machine learning for anomaly based intrusion detection scheme (ML-MANET), respectively.

Improved Elman Deep Learning Model for Intrusion Detection System in Internet of Things

Many researchers have developed intrusion detection systems in the past using conventional techniques such as artificial neural networks, fuzzy clustering, evolutionary algorithms, association rule mining, and support vector machines. However, in terms of false negative rates and detection rates, these methods did not yield the best outcomes. To address these problems, we proposed a hybrid deep learning model (HDLM) based on intrusion detection and prevention in IoT devices. Initially, the data are collected from KDDCup-99 and NSL-KDD datasets. Then, the important features are extracted from the dataset using the Forward Feature Selection Algorithm (FFSA). Finally, the extracted features are given to the HDLM classifier. The proposed HDLM is a combination of Elman Recurrent Neural Network (ERNN) and Subtraction-Average-Based Optimizer (SABO). The performance of the suggested method is assessed using performance metrics including precision, recall, accuracy, sensitivity, specificity, and F_Measure. The experimental results show that the proposed method attained the maximum intrusion detection accuracy of 98.52%.

A hybrid deep learning approach for enhanced network intrusion detection

The contemporary era places paramount importance on network security and cloud environments, driven by increased data transmission demands, the flexibility of cloud services, and the prevalence of global resources. Addressing the escalating threat of computer malware, the development of efficient intrusion detection systems (IDS) is imperative. This research focuses on the challenges posed by imbalanced datasets and the necessity for unsupervised learning to enhance network security. The proposed hybrid deep learning method utilizes raw data from the CSE-CIC-IDS-2018 dataset, integrating imbalanced and unsupervised learning techniques. After preprocessing and normalization, feature extraction through principal component analysis (PCA) reduces dimensionality from seventy-eight fields to ten essential features. Clustering, employing the density-based spatial clustering of applications with noise (DBSCAN) algorithm optimized with particle swarm optimization (PSO), is applied to the extracted features, distinguishing between attack and non-attack packets. Addressing dataset imbalances, imbalanced learning techniques are employed, and unsupervised learning is exemplified through the AutoEncoder (AE) algorithm. The attack cluster’s data is input into AE, a deep learning-based approach, yielding outputs for attack classification. The proposed technique (PCA+DBSCANPSO+AE) achieves an impressive 99.19% accuracy in intrusion detection, surpassing contemporary methodologies and five existing techniques. This research not only enhances accuracy but also addresses imbalanced learning challenges, utilizing the power of unsupervised learning for robust network security.

A fog-edge-enabled intrusion detection system for smart grids

The Smart Grid (SG) heavily depends on the Advanced Metering Infrastructure (AMI) technology, which has shown its vulnerability to intrusions. To effectively monitor and raise alarms in response to anomalous activities, the Intrusion Detection System (IDS) plays a crucial role. However, existing intrusion detection models are typically trained on cloud servers, which exposes user data to significant privacy risks and extends the time required for intrusion detection. Training a high-quality IDS using Artificial Intelligence (AI) technologies on a single entity becomes particularly challenging when dealing with vast amounts of distributed data across the network. To address these concerns, this paper presents a novel approach: a fog-edge-enabled Support Vector Machine (SVM)-based federated learning (FL) IDS for SGs. FL is an AI technique for training Edge devices. In this system, only learning parameters are shared with the global model, ensuring the utmost data privacy while enabling collaborative learning to develop a high-quality IDS model. The test and validation results obtained from this proposed model demonstrate its superiority over existing methods, achieving an impressive percentage improvement of 4.17% accuracy, 13.19% recall, 9.63% precision, 13.19% F1 score when evaluated using the NSL-KDD dataset. Furthermore, the model performed exceptionally well on the CICIDS2017 dataset, with improved accuracy, precision, recall, and F1 scores reaching 6.03%, 6.03%, 7.57%, and 7.08%, respectively. This novel approach enhances intrusion detection accuracy and safeguards user data and privacy in SG systems, making it a significant advancement in the field.

A Secured Intrusion Detection System for Mobile Edge Computing

With the proliferation of mobile devices and the increasing demand for low-latency and high-throughput applications, mobile edge computing (MEC) has emerged as a promising paradigm to offload computational tasks to the network edge. However, the dynamic and resource-constrained nature of MEC environments introduces new challenges, particularly in the realm of security. In this context, intrusion detection becomes crucial to safeguard the integrity and confidentiality of sensitive data processed at the edge. This paper presents a novel Secured Edge Computing Intrusion Detection System (SEC-IDS) tailored for MEC environments. The proposed SEC-IDS framework integrates both signature-based and anomaly-based detection mechanisms to enhance the accuracy and adaptability of intrusion detection. Leveraging edge computing resources, the framework distributes detection tasks closer to the data source, thereby reducing latency and improving real-time responsiveness. To validate the effectiveness of the proposed SEC-IDS framework, extensive experiments were conducted in a simulated MEC environment. The results demonstrate superior detection rates compared to traditional centralized approaches, highlighting the efficiency and scalability of the proposed solution. Furthermore, the framework exhibits resilience to resource constraints commonly encountered in edge computing environments.

Effective Multi-Stage Training Model for Edge Computing Devices in Intrusion Detection

Intrusion detection poses a significant challenge within expansive and persistently interconnected environments. As malicious code continues to advance and sophisticated attack methodologies proliferate, various advanced deep learning-based detection approaches have been proposed. Nevertheless, the complexity and accuracy of intrusion detection models still need further enhancement to render them more adaptable to diverse system categories, particularly within resource-constrained devices, such as those embedded in edge computing systems. This research introduces a three-stage training paradigm, augmented by an enhanced pruning methodology and model compression techniques. The objective is to elevate the system's effectiveness, concurrently maintaining a high level of accuracy for intrusion detection. Empirical assessments conducted on the UNSW-NB15 dataset evince that this solution notably reduces the model's dimensions, while upholding accuracy levels equivalent to similar proposals.

Cyberattack detection in wireless sensor networks using a hybrid feature reduction technique with AI and machine learning methods

This paper proposes an intelligent hybrid model that leverages machine learning and artificial intelligence to enhance the security of Wireless Sensor Networks (WSNs) by identifying and preventing cyberattacks. The study employs feature reduction techniques, including Singular Value Decomposition (SVD) and Principal Component Analysis (PCA), along with the K-means clustering model enhanced information gain (KMC-IG) for feature extraction. The Synthetic Minority Excessively Technique is introduced for data balancing, followed by intrusion detection systems and network traffic categorization. The research evaluates a deep learning-based feed-forward neural network algorithm's accuracy, precision, recall, and F-measure across three vital datasets: NSL-KDD, UNSW-NB 15, and CICIDS 2017, considering both full and reduced feature sets. Comparative analysis against benchmark machine learning approaches is also conducted. The proposed algorithm demonstrates exceptional performance, achieving high accuracy and reliability in intrusion detection for WSNs. The study outlines the system configuration and parameter settings, contributing to the advancement of WSN security.

Federated Learning-based Information Leakage Risk Detection for Secure Medical Internet of Things

The Medical Internet of Things (MIoT) requires extreme information and communication security, particularly for remote consultation systems. MIoT’s integration of physical and computational components creates a seamless network of medical devices providing high-quality care via continuous monitoring and treatment. However, traditional security methods such as cryptography cannot prevent privacy compromise and information leakage caused by security breaches. To solve this issue, this paper proposes a novel Federated Learning Intrusion Detection System (FLIDS). FLIDS combines Generative Adversarial Network (GAN) and Federated Learning (FL) to detect cyber attacks like Denial of Service (DoS), data modification, and data injection using machine learning. FLIDS shows exceptional performance with over 99% detection accuracy and 1% False Positive Rate (FPR). It saves bandwidth by transmitting 3.8 times fewer bytes compared to central data collection. These results prove FLIDS’ effectiveness in detecting and mitigating security threats in Medical Cyber-Physical Systems (MCPS). The paper recommends scaling up FLIDS to use computing resources from multiple mobile devices for better intrusion detection accuracy and efficiency while reducing the burden on individual devices in MIoT.

Intrusion Detection in Internet of Things Systems: A Feature Extraction with Naive Bayes Classifier Approach

The Internet of Things (IoT) has proliferated, transitioning from modest home automation to encompass sectors like healthcare, agriculture, transportation, and manufacturing. This evolution is characterized by devices' ability to autonomously gather, disseminate, and analyze data, leading to improved real-time decision-making, predictive insights, and customized user experiences. The ubiquity of IoT, while promising, introduces significant data security concerns. The vast number of interlinked devices and diverse and often insufficient security features make them vulnerable to cyber threats, emphasizing the need for robust security mechanisms. Intrusion Detection Systems (IDS) have traditionally acted as vital guards against such threats; however, with the ever-increasing data in the IoT, traditional IDS models, such as Naive Bayes, face processing speed and accuracy challenges. This paper introduces a novel model, "FE+NB," which merges advanced Feature Extraction (FE) with the Naive Bayes (NB) classifier. Central to this model is the "Temporal-Structural Synthesis" technique tailored for IoT traffic data, focusing on data compression, temporal and structural analyses, and Feature Selection (FS) using mutual information. Consequently, the model enhances efficiency and accuracy in Intrusion Detection (ID) in complex IoT networks.

Firefly algorithm based WSN-IoT security enhancement with machine learning for intrusion detection

A Wireless Sensor Network (WSN) aided by the Internet of Things (IoT) is a collaborative system of WSN systems and IoT networks are work to exchange, gather, and handle data. The primary objective of this collaboration is to enhance data analysis and automation to facilitate improved decision-making. Securing IoT with the assistance of WSN necessitates the implementation of protective measures to confirm the safety and reliability of the interconnected WSN and IoT components. This research significantly advances the current state of the art in IoT and WSN security by synergistically harnessing the potential of machine learning and the Firefly Algorithm. The contributions of this work are twofold: firstly, the proposed FA-ML technique exhibits an exceptional capability to enhance intrusion detection accuracy within the WSN-IoT landscape. Secondly, the amalgamation of the Firefly Algorithm and machine learning introduces a novel dimension to the domain of security-oriented optimization techniques. The implications of this research resonate across various sectors, ranging from critical infrastructure protection to industrial automation and beyond, where safeguarding the integrity of interconnected systems are of paramount importance. The amalgamation of cutting-edge machine learning and bio-inspired algorithms marks a pivotal step forward in crafting robust and intelligent security measures for the evolving landscape of IoT-driven technologies. For intrusion detection in the WSN-IoT, the FA-ML method employs a support vector machine (SVM) machine model for classification with parameter tuning accomplished using a Grey Wolf Optimizer (GWO) algorithm. The experimental evaluation is simulated using NSL-KDD Dataset, revealing the remarkable enhancement of the FA-ML technique, achieving a maximum accuracy of 99.34%. In comparison, the KNN-PSO and XGBoost models achieved lower accuracies of 96.42% and 95.36%, respectively. The findings validate the potential of the FA-ML technique as an active security solution for WSN-IoT systems, harnessing the power of machine learning and the Firefly Algorithm to bolster intrusion detection capabilities.

Securing the Financial Realm: Unveiling Cybersecurity Threats, Vulnerabilities, and Advanced Security Solutions in Banking

With the increasing reliance on digital platforms for financial transactions, the security of banking systems has become a critical concern. Intrusion detection plays a pivotal role in safeguarding these systems against unauthorized access and malicious activities. This research paper explores the application of Support Vector Machines (SVMs) as a robust and efficient tool for detecting intrusions in banking systems. SVMs, known for their ability to handle high-dimensional data and nonlinear patterns, are employed to enhance the accuracy and reliability of intrusion detection in the complex and dynamic banking environment. Existing intrusion detection methods struggle to cope with the diverse and evolving nature of cyber threats. This research is motivated by the potential of SVMs to provide a solution, given their capacity to classify intricate patterns and adapt to changing environments.

A Cryptographic based I2ADO-DNN Security Framework for Intrusion Detection in Cloud Systems

Cloud computing's popularity and success are directly related to improvements in the use of Information and Communication Technologies (ICT). The adoption of cloud implementation and services has become crucial due to security and privacy concerns raised by outsourcing data and business applications to the cloud or a third party. To protect the confidentiality and security of cloud networks, a variety of Intrusion Detection System (IDS) frameworks have been developed in the conventional works. However, the main issues with the current works are their lengthy nature, difficulty in intrusion detection, over-fitting, high error rate, and false alarm rates. As a result, the proposed study attempts to create a compact IDS architecture based on cryptography for cloud security. Here, the balanced and normalized dataset is produced using the z-score preprocessing procedure. The best attributes for enhancing intrusion detection accuracy are then selected using an Intelligent Adorn Dragonfly Optimization (IADO). In addition, the trained features are used to classify the normal and attacking data using an Intermittent Deep Neural Network (IDNN) classification model. Finally, the Searchable Encryption (SE) mechanism is applied to ensure the security of cloud data against intruders. In this study, a thorough analysis has been conducted utilizing various parameters to validate the intrusion detection performance of the proposed I2ADO-DNN model.

Novel Network Intrusion Detection Based on Feature Filtering Using FLAME and New Cuckoo Selection in a Genetic Algorithm

Recently, networks have faced a significant challenge in terms of security due to constant unauthorized access by hackers, resulting in the compromise of network user data. To enhance network security, there are various approaches that can be employed, including the utilization of firewalls, encryption, and antivirus software. Among these methods, one type of system that can be implemented is an intrusion detection system (IDS), which actively monitors the network to identify any intrusions. In order to effectively detect any unauthorized or malicious activities, sophisticated techniques such as genetic algorithms, cuckoo searches, and FLAME are employed. This research proposes a novel IDS that aims to improve the detection of intrusions. The proposed IDS initially conducts feature filtration using fuzzy clustering through the local approximation of the membership algorithm (FLAME), which effectively reduces the number of features that need to be analyzed and processed. Subsequently, the system detects attacks by implementing an extended classifier system (XCS) that incorporates a genetic algorithm (GA) to enhance the accuracy of intrusion detection. By incorporating the cuckoo search and selection within GA, while considering different crossover and mutation probabilities, instead of solely relying on traditional GA, it is anticipated that intrusion detection accuracy will be improved. To evaluate the performance of the proposed IDS, it was tested on the KDD99 dataset and compared with several other IDSs that were also tested on the same dataset. The experimental results clearly indicate that the proposed IDS significantly enhances the detection rate (DR) and accuracy while also significantly reducing the false alarm rate (FAR) and these impressive results were 100%, 99.99% and 0.05% respectively. The performance comparisons reveal that, overall, the proposed IDS outperforms several existing IDSs in terms of intrusion detection performance.