Enhanced Intrusion Detection Research Articles

Explainable AI-based innovative hybrid ensemble model for intrusion detection

AbstractCybersecurity threats have become more worldly, demanding advanced detection mechanisms with the exponential growth in digital data and network services. Intrusion Detection Systems (IDSs) are crucial in identifying illegitimate access or anomalous behaviour within computer network systems, consequently opposing sensitive information. Traditional IDS approaches often struggle with high false positive rates and the ability to adapt embryonic attack patterns. This work asserts a novel Hybrid Adaptive Ensemble for Intrusion Detection (HAEnID), an innovative and powerful method to enhance intrusion detection, different from the conventional techniques. HAEnID is composed of a string of multi-layered ensemble, which consists of a Stacking Ensemble (SEM), a Bayesian Model Averaging (BMA), and a Conditional Ensemble method (CEM). HAEnID combines the best of these three ensemble techniques for ultimate success in detection with a considerable cut in false alarms. A key feature of HAEnID is an adaptive mechanism that allows ensemble components to change over time as network traffic patterns vary and new threats appear. This way, HAEnID would provide adequate protection as attack vectors change. Furthermore, the model would become more interpretable and explainable using Shapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME). The proposed Ensemble model for intrusion detection on CIC-IDS 2017 achieves excellent accuracy (97-98%), demonstrating effectiveness and consistency across various configurations. Feature selection further enhances performance, with BMA-M (20) reaching 98.79% accuracy. These results highlight the potential of the ensemble model for accurate and reliable intrusion detection and, hence, is a state-of-the-art choice for accuracy and explainability.

A Hybrid Approach for Intrusion Detection by Dragonfly Optimization and Bayesian Neural Network Methodologies

The technique of keeping an eye out for malicious activity or policy violations on a computer network or system is known as intrusion detection. It entails examining system logs, network traffic, and other data sources to spot potential security breaches, misuse, and illegal access. Network-based and host-based intrusion detection systems (IDS) are the two types available. The former analyzes network traffic and packets, while the latter concentrates on specific hosts or devices. This paper investigates how to improve intrusion detection systems in cyber security by combining dragonfly optimization (DFO) and Bayesian neural network (BNN) methodologies. The NSL-KDD dataset, which is tested and trained in the system, is the source of the dataset used in the study. By utilizing the optimization skills of DFO and the probabilistic reasoning powers of BNN, the hybrid technique improves the accuracy and flexibility of detecting and addressing network intrusions. This research illustrates the effectiveness of the suggested methodology in navigating dynamic network environments and identifying emerging dangers through a thorough examination. In addition to improving detection performance, the integration of BNN and DFO gives enterprises insightful information about infiltration patterns and the ability to take proactive measures to thwart hostile activity. The created model's enhanced intrusion detection performance is demonstrated by the experimental findings. These results are validated in terms of accuracy, precision, recall, execution time, and F1 score by comparing them with models that are currently in use.

ShNFN: Shepard Neuro-Fuzzy Network for Intrusion Detection in Fog Computing

Fog computing is a type of distributed computing that makes data storage and computation closer to the network edge. While fog computing offers numerous advantages, it also introduces several challenges, particularly in terms of security. Intrusion Detection System (IDS) plays a crucial role in securing fog computing environments by monitoring network traffic and system activities for signs of malicious behavior. Several techniques can be employed to enhance intrusion detection in fog computing environments. Accordingly, this paper proposes a Shepard Neuro-Fuzzy Network (ShNFN) for intrusion detection in fog computing. Initially, in the cloud layer, the input data are passed to data transformation to transform the unstructured data into structured form. Here, data transformation is done employing the Box-Cox transformation. Following this, the feature selection is done in terms of information gain and symmetric uncertainty process and it is used to create a relationship between two variables. After that, the data are classified by employing the proposed ShNFN. The ShNFN is attained by fusing two networks, such as Cascade Neuro-Fuzzy Network (Cascade NFN) and Shepard Convolutional Neural Networks (ShCNN). After this, the physical process is executed at the endpoint layer. Finally, intrusion detection is accomplished in the fog layer by the proposed ShNFN method. The performance of the intrusion detection using ShNFN is calculated by the metrics of recall, F-measure and precision. The proposed method achieves the values of 93.3%, 92.5% and 94.8% for recall, F-measure, and precision, respectively.

Enhanced intrusion detection framework for securing IoT network using principal component analysis and CNN

ABSTRACT The Internet of Things (IoT) has transformed our world by connecting smart devices and enabling seamless interactions. This reliance, however, has led to new security issues and types of attacks. It is of the utmost importance to safeguard the security of IoT networks, with network intrusion detection systems (NIDS) having a significant impact. This paper proposes a novel approach integrating Principal Component Analysis (PCA), Pearson Correlation Coefficient (PCC), and Convolutional Neural Network (CNN) to overcome these security issues. Our innovative method reduces data dimensionality and selects highly correlated features using PCC and PCA, addressing overfitting and improving model performance while maintaining high computational speed and low costs. Our approach uniquely distinguishes between benign and threat packets by employing 1D-CNN, 2D-CNN, and 3D-CNN algorithms trained on Edge-IIoTset and NSL-KDD benchmark datasets. The findings from our experiments indicate that the proposed framework significantly enhances accuracy, precision, recall, and F1-score compared to existing models for both binary and multiclass classifications. Our binary classification models achieved exceptional performance, with an average accuracy of 99.76%, 99.79% precision, 99.89% recall, and 99.85% F1-score on the Edge-IIoTset dataset. On the NSL-KDD dataset, the models attained 99.20% accuracy, 98.07% precision, 97.95% recall, and 97.71% F1-score. For multiclass classification, the proposed model demonstrated an average accuracy of 99.41%, precision of 98.61%, recall of 98.49%, and an F1-score of 98.56% on the Edge-IIoTset dataset. On the NSL-KDD dataset, the model achieved 92.43% accuracy, 93.21% precision, 93.60% recall, and a 93.7% F1-score. Our research introduces a significant advancement that substantially improves NIDS capabilities, making IoT networks safer and more connected.

Intrusion detection systems for the internet of things: a probabilistic anomaly detection approach

The expansion of IoT domains presents new cybersecurity challenges. In this paper, we propose an enhanced Intrusion Detection System (IDS) designed to tackle these emerging security issues. Our approach harnesses the power of unsupervised learning techniques, particularly the Variational Autoencoder (VAE), to reduce data dimensionality while preserving essential features. By integrating advanced clustering algorithms–including K-means, Gaussian Mixture Model (GMM), and Variational Bayesian Gaussian Mixture Model (VBGMM)–we facilitate precise data categorization within IoT networks. Our conducted experiments using the IoT Network Intrusion Dataset demonstrate that VAE-based methods consistently outperform traditional Autoencoders (AE), achieving superior performance across critical metrics such as False Alarm Rate (FAR), Detection Rate (DR), Accuracy, Precision, and Area Under the ROC Curve (AUC).

Enhanced intrusion detection model based on principal component analysis and variable ensemble machine learning algorithm

The intrusion detection system (IDS) model, which can identify the presence of intruders in the network and take some predefined action for safe data transit across the network, is advantageous in achieving security in both simple and advanced network systems. Several IDS models have various security problems, such as low detection accuracy and high false alarms, which can be caused by the network traffic dataset's excessive dimensionality and class imbalance in the creation of IDS models. Principal Component Analysis (PCA) has proven to be a helpful feature selection technique for dimensionality reduction. As a result, because it is a linear transformation, it has challenges capturing non-linear relationships between feature properties in the network traffic datasets. This paper proposes a variable ensemble machine learning method to solve the problem and achieve a low variance model with high accuracy and low false alarm. First, PCA is combined with the AdaBoost ensemble machine learning algorithm, which acts as stagewise additive modelling to compensate for PCA's deficiency in feature selection in network traffic by minimizing the exponential loss function. Secondly, PCA is used for feature selection, and a LogitBoost classifier algorithm can be used for multiclass classification and acts as an additive tree regression to compensate for the PCA's weakness by minimizing the Logistic Loss to provide an optimal classifier output. Finally, the low variance ability of RandomForest, which employs the bagging approach, is applied to eliminate overfittings. The experiments of the IDS model developed from the proposed methods were evaluated on the WSN-DS, NSL-KDD, and UNSW-N15 datasets. The performance of the methods, PCA with AdaBoost, on the WSN-DS dataset has an accuracy score of 92.3 %, an 89.0 % accuracy score on the NSL-KDD dataset, and a 67.9 % accuracy score on UNSW-N15, which is the least accurate score. PCA and RandomForest surpassed them by scoring 100 % accuracy on all three datasets. PCA and Bagging have an accuracy score of 99.8 % on the WSN-DS dataset, 100 % on the NSL-KDD dataset, and 93.4 % on the UNSW-N15 dataset. In comparison, PCA and LogitBoost have an accuracy score of 98.9 % on the WSN-DS dataset, 100 % on the NSL-KDD dataset, and 88.7 % on the UNSW-N15 dataset.

Shielding networks: enhancing intrusion detection with hybrid feature selection and stack ensemble learning

The frequent usage of computer networks and the Internet has made computer networks vulnerable to numerous attacks, highlighting the critical need to enhance the precision of security mechanisms. One of the most essential measures to safeguard networking resources and infrastructures is an intrusion detection system (IDS). IDSs are widely used to detect, identify, and track malicious threats. Although various machine learning algorithms have been used successfully in IDSs, they are still suffering from low prediction performances. One reason behind the low accuracy of IDSs is that existing network traffic datasets have high computational complexities that are mainly caused by redundant, incomplete, and irrelevant features. Furthermore, standalone classifiers exhibit restricted classification performance and typically fail to produce satisfactory outcomes when dealing with imbalanced, multi-category traffic data. To address these issues, we propose an efficient intrusion detection model, which is based on hybrid feature selection and stack ensemble learning. Our hybrid feature selection method, called MI-Boruta, combines mutual information (MI) as a filter method and the Boruta algorithm as a wrapper method to determine optimal features from our datasets. Then, we apply stacked ensemble learning by using random forest (RF), Catboost, and XGBoost algorithms as base learners with multilayer perceptron (MLP) as meta-learner. We test our intrusion detection model on two widely recognized benchmark datasets, namely UNSW-NB15 and CICIDS2017. We show that our proposed IDS outperforms existing IDSs in almost all performance criteria, including accuracy, recall, precision, F1-Score, false positive rate, true positive rate, and error rate.

Enhanced intrusion detection with fiber optic sensors in rainy weather conditions: a comparative study of phase parameter matching and cross rebuilding algorithms for higher end communication

Enhanced intrusion detection with fiber optic sensors in rainy weather conditions: a comparative study of phase parameter matching and cross rebuilding algorithms for higher end communication

A comprehensive survey on deep learning‐based intrusion detection systems in Internet of Things (IoT)

AbstractThe proliferating popularity of Internet of Things (IoT) devices has led to wide‐scale networked system implementations across multiple disciplines, including transportation, medicine, smart homes, and many others. This unprecedented level of interconnectivity has introduced new security vulnerabilities and threats. Ensuring security in these IoT settings is crucial for protecting against malicious activities and safeguarding data. Real‐time identification and response to potential intrusions and attacks are essential, and intrusion detection systems (IDS) are pivotal in this process. However, the dynamic and diverse nature of the IoT environment presents significant challenges to existing IDS solutions, which are often based on rule‐based or statistical approaches. Deep learning, a subset of artificial intelligence, has shown great potential to enhance IDS in IoT. Deep learning models can identify complex patterns and characteristics by utilizing artificial neural networks, automatically building hierarchical representations from data. This capability results in more precise and efficient intrusion detection in IoT‐based systems. The primary aim of this survey is to present an extensive overview of the current research on deep learning and IDS in the IoT domain. By examining existing literature, discussing mainstream datasets, and highlighting current challenges and potential prospects, this survey provides valuable insights into the prevailing scenario and future directions for using deep learning in IDS for IoT. The findings from this research aim to enhance intrusion detection techniques in IoT environments and promote the development of more effective antimalware solutions against cyber threats targeting IoT device systems.

An innovative model for an enhanced dual intrusion detection system using LZ‐JC‐DBSCAN, EPRC‐RPOA and EG‐GELU‐GRU

AbstractThe rise of suspicious activities in network communication, driven by increased internet accessibility, necessitates the development of advanced intrusion detection systems (IDS). Existing IDS solutions often exhibit poor performance in detecting suspicious activity and fail to identify various attack types within packet capture (PCAP) files, which monitor network traffic. This paper proposes a deep learning‐based dual IDS model designed to address these issues. The process begins with utilizing the CSE‐CIC‐IDS2019 dataset to extract features from PCAP files. Suspicious activities are detected using the Exponential Geometric‐Gaussian Error Linear Units‐Gated Recurrent Unit (EG‐GELU‐GRU) method. Normal data undergoes further feature extraction and preprocessing through Log ZScore‐Jacosine Density‐Based Spatial Clustering of Applications with Noise (LZ‐JC‐DBSCAN). Feature selection is optimized using the Entropy Pearson R Correlation‐Red Panda optimization algorithm. Suspicious files are flagged, while load balancing is performed on normal data. Attack detection is achieved through word embedding with the Glorot Kaufman‐bidirectional encoder representations from transformers technique and classification via the EG‐GELU‐GRU model. Attacked packets are blocked, and the method is reapplied for attack‐type classification. Experimental results using Python demonstrate the model’s superior performance, achieving 98.18% accuracy and 98.73% precision, surpassing existing approaches and significantly enhancing intrusion detection capabilities.

Detection of Intrusions using Transformer - LSTM Model

The project focuses on addressing the escalating issue of web attacks and ensuring cyberspace security in the era of advancing internet technology. The primary goal is to enhance intrusion detection systems (IDS) by moving beyond traditional keyword and rule- based methods. The project explores the integration of artificial intelligence, specifically deep learning techniques, to develop a more robust and adaptive approach to identifying network intrusion events. With the limitations of conventional detection methods in mind, there is a critical need for a more effective and versatile intrusion detection system. The rise in diverse attack methods necessitates an intelligent system capable of autonomously adapting to new attack patterns without relying on manual rule creation. This project is designed to benefit both individuals and organizations reliant on network services. Improved intrusion detection ensures a higher level of cybersecurity, safeguarding sensitive data and systems. The project's outcomes have the potential to contribute significantly to the broader field of network security. Leveraging the power of deep learning, the proposed model- Transformer (autoencoder) LSTM combines Transformer and LSTM architectures. Multiple attention mechanisms are employed to select and extract features, while the LSTM model facilitates understanding the sequential relationships within network traffic, enabling the accurate identification of intrusion events. And we are comparing the model with DNN, LSTM and Transformer(autoencoder). To enhance performance, CNN and CNN+LSTM extensions were incorporated into the project, complementing the Transformer (autoencoder) LSTM model. These models aim to further refine feature extraction and exploit spatial relationships within network traffic. Key Words:intrusion detection, transformer, LSTM, deep learning.

Enhancing cybersecurity in cloud computing and WSNs: A hybrid IDS approach

The evolution of cloud computing has revolutionized how users access services, simplifying the development and deployment of applications across various industries. With its pervasive adoption, robust security measures become imperative. Integrating Intrusion Detection Systems (IDSs) into cloud computing and Wireless Sensor Networks (WSNs) addresses these challenges. IDSs serve as attentive protectors, monitoring network traffic and responding to breaches promptly, enhancing security across industries reliant on cloud services. Similarly, IDS integration in WSNs ensures the security of mission-critical operations, despite resource constraints and dynamic topologies, facilitated by cloud computing. This research proposes a hybrid IDS approach, leveraging the NSL-KDD dataset and methodologies like Intrusion Support Scalar Impact Rate (ISSIR), Optimized Support Vector Machine (OSVM), Extended Long-Short-Term Memory (ELSTM), and Multilayer Perceptron Neural Network (MLPNN), enhancing intrusion detection efficacy. ISSIR aids in feature selection, OSVM mitigates localization errors, ELSTM enables precise anomaly detection, and MLPNN provides robust defense mechanisms. Each method is integrated into a collaborative framework to address specific challenges in detecting intrusions with higher accuracy and reduced false positives. The interplay between these methodologies strengthens the overall intrusion detection framework, addressing the dynamic nature of cybersecurity threats. Results demonstrate the superior performance of MLPNN across various metrics, showcasing its effectiveness in accurately predicting outcomes compared to other models. The proposed MLPNN hybrid system achieves an accuracy of 99.9%, surpassing state-of-the-art methods. This study underscores the significance of advancing IDSs in cloud computing and WSNs, offering insights into enhancing security and mitigating vulnerabilities in an interconnected digital landscape.

Comparative Analysis of DNN, GBT, and KNN Models for Network Intrusion Detection

Network intrusion detection is critical to cybersecurity, aiming to identify and mitigate unauthorized access and attacks on computer systems and networks. This study evaluates the effectiveness of three machine learning techniques—deep neural networks (DNN), gradient boost trees (GBT), and k-nearest neighbors (KNN)—in detecting network intrusions. The performance of these models was assessed using a comprehensive dataset of 2,540,047 records encompassing 49 features across nine attack categories. The results indicate that GBT outperforms DNN and KNN in accuracy and robustness. These findings highlight the potential of GBT for enhancing intrusion detection systems and contribute valuable insights into the comparative performance of different machine learning algorithms in cybersecurity applications.

Fuzzy Ranking for Enhanced Security: Optimizing Feature Selection in VANET Intrusion Detection

In Vehicular Ad-Hoc Networks (VANETs), ensuring efficient intrusion detection while minimizing false alarms is paramount for maintaining network security and reliability. Traditional methods often struggle to strike this balance effectively. This study proposes a novel approach that employs fuzzy ranking to enhance intrusion detection efficiency in VANETs while mitigating false positive and negative factors. At the core of our approach lies the development of an innovative fuzzy ranking mechanism to identify optimal features extracted from VANET nodes. These features serve as critical indicators of potential security threats within the network. By systematically collecting and analyzing data from various sources including vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications, our approach aims to capture nuanced patterns and anomalies indicative of malicious behavior. The fuzzy ranking technique enables us to prioritize features based on their relevance to intrusion detection, thereby facilitating more effective feature selection. This process ensures that only the most informative features are utilized, enhancing the overall efficiency of the intrusion detection system. Features such as packet transmission rates, signal strength variations, route deviation patterns, and network topology characteristics are carefully evaluated and ranked to optimize detection accuracy. To train our intrusion detection model, we leverage machine learning algorithms such as deep neural networks, support vector machines, and decision trees. By utilizing labeled datasets containing examples of both benign and malicious network activities, our model learns to distinguish between normal and anomalous behavior, thus enabling timely threat detection. Furthermore, we integrate fuzzy logic-based anomaly detection techniques to identify previously unseen or zero-day attacks, enhancing the system's robustness. In addition to improving detection accuracy, our approach focuses on preventing harmful warnings caused by false alarms. We achieve this by implementing a comprehensive filtering mechanism that distinguishes between transient anomalies and genuine security threats. Contextual factors such as traffic conditions, environmental variables, and historical network behavior are considered to reduce false positives while ensuring reliable threat detection.Through extensive experimentation and evaluation, we demonstrate the effectiveness of our approach in enhancing intrusion detection efficiency and reducing false positive/negative factors in VANETs. The proposed methodology offers a promising solution to the security challenges faced by VANETs, paving the way for safer and more secure vehicular communication systems in the future.

Enhancing Intrusion Detection in Wireless Sensor Networks through Machine Learning Techniques and Context Awareness Integration

Enhancing Intrusion Detection in Wireless Sensor Networks through Machine Learning Techniques and Context Awareness Integration

Enhanced Intrusion Detection in IoT with a Novel PRBF Kernel and Cloud Integration

Enhanced Intrusion Detection in IoT with a Novel PRBF Kernel and Cloud Integration

Advancing IoT Security: Integrative Machine Learning Models for Enhanced Intrusion Detection in Wireless Sensor Networks

Advancing IoT Security: Integrative Machine Learning Models for Enhanced Intrusion Detection in Wireless Sensor Networks

Enhancing Intrusion Detection with Dimensionality Reduction Methods Using Machine Learning

Enhancing Intrusion Detection with Dimensionality Reduction Methods Using Machine Learning

Enhancing intrusion detection: a hybrid machine and deep learning approach

The volume of data transferred across communication infrastructures has recently increased due to technological advancements in cloud computing, the Internet of Things (IoT), and automobile networks. The network systems transmit diverse and heterogeneous data in dispersed environments as communication technology develops. The communications using these networks and daily interactions depend on network security systems to provide secure and reliable information. On the other hand, attackers have increased their efforts to render systems on networks susceptible. An efficient intrusion detection system is essential since technological advancements embark on new kinds of attacks and security limitations. This paper implements a hybrid model for Intrusion Detection (ID) with Machine Learning (ML) and Deep Learning (DL) techniques to tackle these limitations. The proposed model makes use of Extreme Gradient Boosting (XGBoost) and convolutional neural networks (CNN) for feature extraction and then combines each of these with long short-term memory networks (LSTM) for classification. Four benchmark datasets CIC IDS 2017, UNSW NB15, NSL KDD, and WSN DS were used to train the model for binary and multi-class classification. With the increase in feature dimensions, current intrusion detection systems have trouble identifying new threats due to low test accuracy scores. To narrow down each dataset’s feature space, XGBoost, and CNN feature selection algorithms are used in this work for each separate model. The experimental findings demonstrate a high detection rate and good accuracy with a relatively low False Acceptance Rate (FAR) to prove the usefulness of the proposed hybrid model.

Enhancing Intrusion Detection Systems with Recurrent Neural Networks in D23 Deep Learning

Strong cybersecurity safeguards are increasingly necessary as the digital landscape changes to protect sensitive data and vital infrastructure. To detect and reduce any risks to network security, intrusion detection systems, or IDS, are essential. To improve the precision and effectiveness of anomaly detection, this paper suggests a novel method of intrusion detection that makes use of recurrent neural networks (RNNs). Standard intrusion detection systems (IDS) frequently depend on static rule-based systems, which may find it difficult to adjust to changing and dynamic cyber threats. On the other hand, sequence-retention neural networks, or RNNs, they have a special capacity to recognize patterns and temporal connections in sequential input. Traditional IDS methods are contrasted with the suggested system, which is assessed using common datasets. The RNN-based IDS is successful at detecting known and unknown threats while reducing false positives, according to preliminary data.