Robust Intrusion Detection System Research Articles

A Robust DDoS Intrusion Detection System Using Convolutional Neural Network

In today’s digital age, the proliferation of network-connected devices has triggered a surge in cyberattacks. Distributed Denial-of-Service (DDoS) attacks pose a particularly formidable challenge to network security by disrupting access to vital services. While numerous researchers have proposed DDoS detection methods utilizing machine learning and deep learning techniques, developing a robust and reliable DDoS intrusion detection system remains challenging. This challenge is exacerbated by issues such as highly imbalanced data, multi-classification, and computational complexity. This paper proposes an innovative feature selection approach to create a robust intrusion detection system capable of detecting and classifying recent common DDoS attack types. We evaluate the performance of our model on the CICDDoS2019 benchmark dataset. Our experimental results demonstrate that our proposed model outperforms existing methods, achieving a detection accuracy of 96.82%, a recall of 96.82%, a precision of 96.76%, and an F1 score of 96.50%. Additionally, our model exhibits faster prediction times, with the ability to predict an attack in just 0.189 ms. Notably, our approach, combined with preprocessing and feature selection techniques, outperforms previous works and baseline models in DDoS attack classification.

SYN-GAN: A robust intrusion detection system using GAN-based synthetic data for IoT security

As technological communication progresses, diverse datasets are exchanged across distributed environments using the Internet of Things (IoT). However, the IoT environment is vulnerable to attacking and breaching data privacy or making a robust system worse by providing attack data. To address potential risks of attacks, researchers have been conducting experiments on network intrusion detection systems (NIDS) to mitigate threats effectively. The issue of data imbalance and associated data collection costs persists, hindering the ability of machine learning (ML) models to learn malicious behaviour effectively and consequently impacting the accuracy of network threat detection. Addressing these issues, our study explores the potential of using 100% synthetic data generated via Generative Adversarial Networks (GAN) for training ML models in Network Intrusion Detection Systems (NIDS). This approach reduces the dependency on real-world data significantly, paving the way for a more flexible and ethically convenient model-building process. For the UNSW-NB15 dataset, we achieved an accuracy of 90%, a precision of 91%, a recall of 90%, and an F1 score of 89%. For the NSL-KDD dataset, our results showed an accuracy of 84%, a precision of 85%, a recall of 84%, and an F1 score of 84%. For the BoT-IoT dataset, we attained perfect scores of 100% across all metrics. These outcomes indicate that the values obtained from our analysis demonstrate high performance, yielding comparative or superior results to previous studies. Therefore, our study successfully replicates real-world network intrusion detection data, showing new opportunities for the use of generative data in cyber security.

SecureNet: Network Intrusion Detection using Machine Learning and Deep Learning Techniques

Abstract: In the ever-evolving landscape of cybersecurity, the need for robust intrusion detection systems has become paramount. This paper introduces a cutting-edge intrusion detection algorithm designed to enhance network security through the integration of advanced machine learning and deep learning methodologies. The proposed algorithm capitalizes on the strengths of both paradigms to achieve a comprehensive and adaptive approach to identifying malicious activities within a network. This research focuses on enhancing network security through the development and evaluation of a novel intrusion detection system leveraging both deep learning and traditional machine learning approaches. Utilizing the NSL-KDD dataset, we employ the Long Short-Term Memory (LSTM) model, a superior version of Recurrent Neural Networks (RNNs), and the K-Nearest Neighbors (KNN) algorithm for binary and multi-class classification of network intrusion anomalies. The LSTM model excels in capturing temporal dependencies, enabling the detection of nuanced sequential patterns, while the KNN algorithm contributes to a comprehensive classification framework. Experimental results demonstrate the effectiveness of the hybrid methodology, showcasing improved accuracy, precision, and recall compared to traditional methods. This research underscores the potential of integrating deep learning and classical machine learning techniques to bolster the capabilities of intrusion detection systems in safeguarding against evolving cyber threats

Hybrid RNN-LSTM Networks for Enhanced Intrusion Detection in Vehicle CAN Systems

Electric vehicles (EVs) use electric motors for propulsion, relying on electric energy stored in batteries or other energy storage devices. The standard communication protocol used in EVs is the Control Area Network (CAN), a communication protocol widely used in the automotive industry for networking and communication between various components within a vehicle. CAN protocol, designed without any care about protection, as automotive systems become more connected, the vulnerability to cyber threats, including intrusion attacks. The most common intrusion attacks on EVs are Denial of Service (DoS), Fuzzy, and Impersonation Attacks. These become a significant challenge due to the imperative need for robust Intrusion Detection Systems (IDS) in CAN networks. This paper explores the application of advanced deep learning techniques, specifically Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks, to enhance the effectiveness of intrusion detection in the EV domain. We will use a hybrid Deep-learning model to improve the analysis. First, we will apply the RNN model, and the output will come as input for the second model, LSTM. Our proposed hybrid model achieved an accuracy of 93%. The outcomes of this research contribute to advancing cybersecurity measures in vehicular networks, ensuring the integrity and safety of connected vehicles. The applicability of RNN and LSTM techniques in the context of CAN networks demonstrates their potential to evolve as integral components of next-generation intrusion detection systems, fostering a secure and resilient automotive ecosystem.

A Robust Intrusion Detection System Empowered by Generative Adversarial Networks

There is a very bleak outlook on cyber security due to the rapid expansion of the Internet and the ever-changing terrain of cyber-attacks. This paper explores the field of intrusion detection through network analysis, with a particular emphasis on applying machine learning (ML) and deep learning (DL) approaches. For every ML/DL technique, a thorough tutorial overview is given together with a review of pertinent research publications. These studies were read, indexed, and summarised according to their thermal or temporal correlations with great care. The paper also provides information on frequently used network datasets in this field, which is relevant given the critical role that data plays in ML/DL techniques. It also discusses the difficulties in using ML/DL for cyber security and provides insightful recommendations for future lines of inquiry. Interestingly, the KDD data set shows up as a reputable industry standard for intrusion detection methods. A lot of work is being done to improve intrusion detection techniques, and both training and evaluating the detection model's quality depend equally on the quality of the data. The KDD data collection is thoroughly analysed in this research, with a special emphasis on four different attribute classes: Basic, Content, Traffic, and Host. We use the Modified Random Forest (MRF) technique to classify these properties.

Parameterization and Performance Analysis of a Scalable, near Real-Time Packet Capturing Platform

The rapid evolution of technology has fostered an exponential rise in the number of individuals and devices interconnected via the Internet. This interconnectedness has prompted companies to expand their computing and communication infrastructures significantly to accommodate the escalating demands. However, this proliferation of connectivity has also opened new avenues for cyber threats, emphasizing the critical need for Intrusion Detection Systems (IDSs) to adapt and operate efficiently in this evolving landscape. In response, companies are increasingly seeking IDSs characterized by horizontal, modular, and elastic attributes, capable of dynamically scaling with the fluctuating volume of network data flows deemed essential for effective monitoring and threat detection. Yet, the task extends beyond mere data capture and storage; robust IDSs must integrate sophisticated components for data analysis and anomaly detection, ideally functioning in real-time or near real-time. While Machine Learning (ML) techniques present promising avenues for detecting and mitigating malicious activities, their efficacy hinges on the availability of high-quality training datasets, which in turn poses a significant challenge. This paper proposes a comprehensive solution in the form of an architecture and reference implementation for (near) real-time capture, storage, and analysis of network data within a 1 Gbps network environment. Performance benchmarks provided offer valuable insights for prototype optimization, demonstrating the capability of the proposed IDS architecture to meet objectives even under realistic operational scenarios.

Intrusion Detection using Deep Learning

The focus of cloud computing nowadays has been reshaping the digital epoch, in which clients now face serious concerns about the security and privacy of their data hosted in the cloud, as well as increasingly sophisticated and frequent cyber attacks. Therefore, it has become imperative for both individuals and organizations to implement a robust intrusion detection system (IDS) capable of monitoring packets in the network, distinguishing between benign and malicious behaviour, and detecting the type of attacks. IDS based on ML are efficient and precise in spotting network threats. Yet, for large dimensional data sizes, the performance of these systems decreases. Thus, it is critical to build a suitable feature selection approach that selects necessary features without having an impact on the classification process or causing information loss. Furthermore, training ML models on unbalanced datasets show a rising false positive rate (FPR) and a lowering detection rate (DR). In this paper, we present an improved cloud IDS designed by incorporating the synthetic minority over-sampling technique (SMOTE) to address the imbalanced data issue, and for feature selection, we propose to use a hybrid approach that includes three techniques: information gain (IG), chi-square (CS), and particle swarm optimization (PSO). Finally, the random forest (RF) model is utilized for detecting and classifying various types of attacks. The suggested system has been verified by the UNSW-NB15 and Kyoto datasets, achieving accuracies of over 98% and 99% in the multi-class classification scenario, respectively. It was noticed that an intrusion detection system with fewer informative features would operate more effectively. The simulation results significantly outperform other methodologies proposed in the related work in terms of different evaluation metrics.

Cloud Intrusion Detection System

Cloud computing is currently reshaping the digital landscape, with a heightened focus on security and privacy concerns for data stored in the cloud. As cyberattacks grow in sophistication and frequency, individuals and organizations alike must prioritize robust intrusion detection systems (IDS). These systems, particularly those utilizing machine learning (ML), excel at identifying network threats but face challenges with large data sizes, leading to decreased performance. Effective feature selection becomes crucial to maintain classification accuracy and prevent information loss. Additionally, addressing imbalanced datasets is vital to mitigate false positives and enhance detection rates. In this study, we propose an enhanced cloud IDS integrating the synthetic minority oversampling technique (SMOTE) for data imbalance and a hybrid feature selection method combining information gain (IG), chi-square (CS), and particle swarm optimization (PSO). Leveraging the random forest (RF) model, our system achieves exceptional accuracies exceeding 98% and 99% on the UNSW-NB15 and Kyoto datasets, respectively. Notably, fewer informative features enhance system efficiency, as evidenced by superior performance compared to existing methodologies

IoT Guardian: A Novel Feature Discovery and Cooperative Game Theory Empowered Feature Selection with ML Model for IoT Threats & Attack Detection

Cyber intrusion attacks increasingly target the Internet of Things (IoT) ecosystem, exploiting vulnerable devices and networks. Malicious activities must be identified early to minimize damage and mitigate threats. Using actual benign and attack traffic from the CICIoT2023 dataset, this WORK aims to evaluate and benchmark machine-learning techniques for IoT intrusion detection. There are four main phases to the system. First, the CICIoT2023 dataset is refined to remove irrelevant features and clean up missing and duplicate data. The second phase employs statistical models and artificial intelligence to discover novel features. The most significant features are then selected in the third phase based on cooperative game theory. Using the original CICIoT2023 dataset and a dataset containing only novel features, we train and evaluate a variety of machine learning classifiers. On the original dataset, Random Forest achieved the highest accuracy of 99%. Still, with novel features, Random Forest's performance dropped only slightly (96%) while other models achieved significantly lower accuracy. As a whole, the work contributes substantial contributions to tailored feature engineering, feature selection, and rigorous benchmarking of IoT intrusion detection techniques. IoT networks and devices face continuously evolving threats, making it necessary to develop robust intrusion detection systems.

Innovative Machine Learning Algorithms for Classification and Intrusion Detectionv By IJISRT

With the escalating sophistication of cyber threats, the need for robust intrusion detection systems has become paramount in safeguarding information systems. This research addresses the limitations of traditional methods by proposing and evaluating innovative machine learning algorithms for classification in intrusion detection. The study explores a diverse set of algorithms designed to enhance accuracy, efficiency, and adaptability in the dynamic landscape of cybersecurity. The introduction provides a context for the research, emphasizing the critical role of intrusion detection in contemporary cybersecurity. A comprehensive literature review underscores the shortcomings of existing methodologies and sets the stage for the introduction of novel machine learning approaches. The research methodology outlines the dataset, evaluation metrics, and the training/testing process, ensuring transparency and replicability. The heart of the paper lies in the exploration of innovative machine learning algorithms. Each algorithm is introduced, highlighting unique features and innovations. The experimental results showcase the performance of these algorithms, with detailed comparisons against traditional counterparts. The discussion section interprets the results, emphasizing the practical implications and potential advancements these algorithms bring to the field. Addressing challenges encountered during implementation, the paper outlines future directions for research, providing a roadmap for continued innovation. The conclusion succinctly summarizes key findings, accentuating the groundbreaking contributions of the proposed machine learning algorithms to intrusion detection. This research significantly advances the discourse on intrusion detection systems, offering a paradigm shift towards more effective and adaptive solutions in the face of evolving cyber threats.

Intrusion Detection System using Blockchain

Abstract: This paper investigates the integration of an Intrusion Detection System (IDS) within the context of blockchain technology. The objective is to enhance the security posture of blockchain networks by detecting and mitigating potential intrusions. Through a meticulous examination of the current threat landscape and the unique challenges posed by blockchain systems, this research proposes a robust IDS framework tailored to the specific requirements of decentralized and distributed ledger environments. The study employs [specific methodology/approach] to assess the effectiveness of the proposed IDS, presenting conclusive findings that contribute to the ongoing discourse on securing blockchain ecosystems. The implications of this research extend to bolstering the resilience of blockchain networks against emerging threat.

Multi-scale Convolutional Feature Fusion Network Based on Attention Mechanism for IoT Traffic Classification

AbstractThe Internet of Things (IoT) has been extensively utilized in domains such as smart homes, healthcare, and other industries. With the exponential growth of Internet of Things (IoT) devices, they have become prime targets for malicious cyber-attacks. Effective classification of IoT traffic is, therefore, imperative to enable robust intrusion detection systems. However, IoT traffic data contain intricate spatial relationships and topological information, which traditional methods for traffic identification lack the capability to fully extract features and capture crucial characteristics. We propose a multi-scale convolutional feature fusion network augmented with a Convolutional Block Attention Module (MCF-CBAM) for accurate IoT traffic classification. The network incorporates three critical innovations: (1) Parallel convolution extracts multi-scale spatial features from traffic data. The 1 × 1 convolution operation reduces the amount of parameters and calculations of the network, thereby improving work efficiency. (2) The attention module suppresses less informative features while highlighting the most discriminative ones, enabling focused learning on decisive features. (3) Cross-scale connections with channel jumps reuse features from prior layers to enhance generalization. We evaluate the method extensively on three widely adopted public datasets. Quantitative results demonstrate MCF-CBAM establishes new state-of-the-art performance benchmarks for IoT traffic classification, surpassing existing methods by a significant margin. Qualitative visualizations of the learned attention weights provide intuitive insights into how the network automatically discovers the most decisive spatial features for identification. With its strong empirical performance and interpretable attention mechanisms, this work presents a promising deep learning solution to augment real-world IoT intrusion detection systems against growing cybersecurity threats.

A robust intrusion detection system based on a shallow learning model and feature extraction techniques.

The escalating prevalence of cybersecurity risks calls for a focused strategy in order to attain efficient resolutions. This study introduces a detection model that employs a tailored methodology integrating feature selection using SHAP values, a shallow learning algorithm called PV-DM, and machine learning classifiers like XGBOOST. The efficacy of our suggested methodology is highlighted by employing the NSL-KDD and UNSW-NB15 datasets. Our approach in the NSL-KDD dataset exhibits exceptional performance, with an accuracy of 98.92%, precision of 98.92%, recall of 95.44%, and an F1-score of 96.77%. Notably, this performance is achieved by utilizing only four characteristics, indicating the efficiency of our approach. The proposed methodology achieves an accuracy of 82.86%, precision of 84.07%, recall of 77.70%, and an F1-score of 80.20% in the UNSW-NB15 dataset, using only six features. Our research findings provide substantial evidence of the enhanced performance of the proposed model compared to a traditional deep-learning model across all performance metrics.

Oversampling and undersampling for intrusion detection system in the supervisory control and data acquisition IEC 60870‐5‐104

AbstractSupervisory control and data acquisition systems are critical in Industry 4.0 for controlling and monitoring industrial processes. However, these systems are vulnerable to various attacks, and therefore, intelligent and robust intrusion detection systems as security tools are necessary for ensuring security. Machine learning‐based intrusion detection systems require datasets with balanced class distribution, but in practice, imbalanced class distribution is unavoidable. A dataset created by running a supervisory control and data acquisition IEC 60870‐5‐104 (IEC 104) protocol on a testbed network is presented. The dataset includes normal and attacks traffic data such as port scan, brute force, and Denial of service attacks. Various types of Denial of service attacks are generated to create a robust and specific dataset for training the intrusion detection system model. Three popular techniques for handling class imbalance, that is, random over‐sampling, random under‐sampling, and synthetic minority oversampling, are implemented to select the best dataset for the experiment. Gradient boosting, decision tree, and random forest algorithms are used as classifiers for the intrusion detection system models. Experimental results indicate that the intrusion detection system model using decision tree and random forest classifiers using random under‐sampling achieved the highest accuracy of 99.05%. The intrusion detection system model's performance is verified using various metrics such as recall, precision, F1‐Score, receiver operating characteristics curves, and area under the curve. Additionally, 10‐fold cross‐validation shows no indication of overfitting in the created intrusion detection system model.

An adaptive distributed intrusion detection system in local network: Hybrid classification methods

In the realm of cybersecurity, the incessant evolution of network attacks necessitates advanced and robust intrusion detection systems (IDS). The major issues with these systems are numerous: false positivenegative alarms, delayed response and detection time, size of processed data, adaptability to future threats, scalability of the system, difficulty in detecting distributed attacks, and downtime (fault tolerance). We propose a system that introduces a distributed framework aimed at enhancing network security by effectively identifying subtle deviations from normal network behavior. This is achieved through transfer learning based on artificial neural networks, and support vector machine (SVM), capitalizing on their complementary strengths in recognizing complex patterns and addressing high-dimensional datasets. To validate the efficacy of the proposed approach, the NSL-KDD dataset is utilized within a distributed IDS architecture. It consists of several intrusion detection nodes representing subnetworks. A node consists of two agents that work collaboratively. A way is proposed to avoid interference between analysis agents: the network agents manager monitors the functioning of the nodes and displays the results of each vulnerability-detecting node in each subnet separately. Such communication between agents should reduce FPAS (false positive alarms) significantly. The Detection engine extracts relevant features of network attacks to solve the problem of SVM in processing huge sizes of data and detect adaptive future threats to detect famous distributed denial of services (DDOS) attacks in real-time. The system is highly scalable by increasing the number of intrusion detection system nodes if necessary. Central processing is avoided to circumvent a system failure situation, where processing and decision-making take place at the detection node level within each subnet.

Implement an Intrusion Detection System Utilizing Machine Learning and Principal Component Analysis

The ever-evolving cybersecurity sector requires robust intrusion detection systems (IDS). Traditional rules-based measures are no longer sufficient due to the complexity of cyber threats, requiring new approaches. This study presents the architecture of an intrusion detection system combining machine learning and principal component analysis (PCA) to increase network security. A network traffic classification system was built and tested on the NSL-KDD dataset and used PCA for dimensionality reduction. The results were cross-validated to reduce overfitting and ensure generalizability of the model. Low-variance precision refers to the consistency of the cross-validation fold. The combination of PCA and machine learning models exceeds previous studies with an F1 score for the random forest model of over 99%. The study improves intrusion detection and network protection against cyber-attacks.

Predicting the Impact of Data Poisoning Attacks in Blockchain-Enabled Supply Chain Networks

As computer networks become increasingly important in various domains, the need for secure and reliable networks becomes more pressing, particularly in the context of blockchain-enabled supply chain networks. One way to ensure network security is by using intrusion detection systems (IDSs), which are specialised devices that detect anomalies and attacks in the network. However, these systems are vulnerable to data poisoning attacks, such as label and distance-based flipping, which can undermine their effectiveness within blockchain-enabled supply chain networks. In this research paper, we investigate the effect of these attacks on a network intrusion detection system using several machine learning models, including logistic regression, random forest, SVC, and XGB Classifier, and evaluate each model via their F1 Score, confusion matrix, and accuracy. We run each model three times: once without any attack, once with random label flipping with a randomness of 20%, and once with distance-based label flipping attacks with a distance threshold of 0.5. Additionally, this research tests an eight-layer neural network using accuracy metrics and a classification report library. The primary goal of this research is to provide insights into the effect of data poisoning attacks on machine learning models within the context of blockchain-enabled supply chain networks. By doing so, we aim to contribute to developing more robust intrusion detection systems tailored to the specific challenges of securing blockchain-based supply chain networks.

Convolutional neural network for intrusion detection using blockchain technology

Cyber-physical systems (CPS) are becoming increasingly ubiquitous, connecting the physical world with the cyber realm. This convergence has exposed CPS to a growing threat landscape, necessitating robust intrusion detection systems (IDS) to safeguard critical infrastructure. Deep learning (DL) has emerged as a powerful tool for IDS, and convolutional neural networks (CNNs) have demonstrated exceptional performance in this domain. However, traditional IDS models are susceptible to data tampering and manipulation, compromising their integrity and effectiveness. Blockchain technology, with its inherent immutability and tamper-proof nature, offers a promising solution to enhance the security and reliability of IDS models. In this study, we propose a CNN-based IDS model that leverages blockchain technology to secure network traffic data. Our hypothesis is that integrating blockchain with CNNs can significantly improve the security and robustness of IDS models against data tampering and manipulation. To test our hypothesis, we employ a greedy-based genetic algorithm to select the most relevant features from network traffic data, followed by training a CNN model using the selected features. Finally, we evaluate the trained CNN model on a real-world dataset, demonstrating its ability to accurately classify network traffic as normal or intrusive. The results of our evaluation reveal that the proposed CNN-based IDS model achieves a classification accuracy of 99.2%, surpassing traditional IDS models. Moreover, our model exhibits enhanced resilience against data tampering and manipulation, demonstrating the effectiveness of blockchain integration in safeguarding the integrity of IDS models. Our findings underscore the potential of blockchain-enhanced CNNs as a robust and secure solution for intrusion detection in CPS, ensuring the integrity and protection of critical infrastructure.

RAIDS: Robust autoencoder-based intrusion detection system model against adversarial attacks

Machine learning-based intrusion detection systems (IDS) are essential security functions in conventional and software-defined networks alike. Their success and the security of the networks they protect depend on the accuracy of their classification results. Adversarial attacks against machine learning, which seriously threaten any IDS, are still not countered effectively. In this study, we first develop a method that employs generative adversarial networks to produce adversarial attack data. Then, we propose RAIDS, a robust IDS model, designed to be resilient against adversarial attacks. In RAIDS, an autoencoder's reconstruction error is used as a prediction value for a classifier. Also, to prevent the attacker from guessing about the feature set, multiple feature sets are created and used to train baseline machine learning classifiers. A LightGBM classifier is then trained with the results produced by two autoencoders and an ensemble of baseline machine learning classifiers. The results show that the proposed robust model can increase overall accuracy by at least 13.2% and F1-score by more than 110% against adversarial attacks without the need for adversarial training.

Robust Deep Learning Based Framework for Detecting Cyber Attacks from Abnormal Network Traffic

The internet's recent rapid growth and expansion have raised concerns about cyberattacks, which are constantly evolving and changing. As a result, a robust intrusion detection system was needed to safeguard data. One of the most effective ways to meet this problem was by creating the artificial intelligence subfields of machine learning and deep learning models. Network integration is frequently used to enable remote management, monitoring, and reporting for cyber-physical systems (CPS). This work addresses the primary assault categories such as Denial of Services(DoS), Probe, User to Root(U2R) and Root to Local(R2L) attacks. As a result, we provide a novel Recurrent Neural Networks (RNN) cyberattack detection framework that combines AI and ML techniques. To evaluate the developed system, we employed the Network Security Laboratory-Knowledge Discovery Databases (NSL-KDD), which covered all critical threats. We used normalisation to eliminate mistakes and duplicated data before pre-processing the data. Linear Discriminant Analysis(LDA) is used to extract the characteristics. The fundamental rationale for choosing RNN-LDA for this study is that it is particularly efficient at tackling sequence issues, time series prediction, text generation, machine translation, picture descriptions, handwriting recognition, and other tasks. The proposed model RNN-LDA is used to learn time-ordered sequences of network flow traffic and assess its performance in detecting abnormal behaviour. According to the results of the experiments, the framework is more effective than traditional tactics at ensuring high levels of privacy. Additionally, the framework beats current detection techniques in terms of detection rate, false positive rate, and processing time.