Learning Based Intrusion Detection System Research Articles

ResNet50-1D-CNN: A new lightweight resNet50-One-dimensional convolution neural network transfer learning-based approach for improved intrusion detection in cyber-physical systems

The cyber-physical system (CPS) plays a crucial role in supporting critical infrastructure like water treatment facilities, gas stations, air conditioning components, and smart grids, which are essential to society. However, these systems are facing a growing susceptibility to a wide range of emerging attacks. Cyber-attacks against CPS have the potential to cause disruptions in the accurate sensing and actuation processes, resulting in significant harm to physical entities and posing concerns for the overall safety of society. Unlike common security measures like firewalls and encryption, which often aren't enough to deal with the unique problems that CPS architectures present, deploying machine learning-based intrusion detection systems (IDS) that are specifically made for CPS has become an important way to make them safer. The application of machine learning algorithms has been suggested as a means of mitigating cyber-attacks on CPS. However, the limited availability of labelled data pertaining to emerging attack techniques poses a significant challenge to the accurate detection of such attacks. In the given scenario, transfer learning emerges as a promising methodology for the detection of cyber-attacks, as it involves the implicit modelling of the system. In this research, we propose a new lightweight transfer learning method via ResNet50-CNN1D for intrusion detection in CPS. The Adaptive Gradient (Adagrad) optimizer was applied in the proposed model to minimize the loss function through the adjustment of network weight. We tested how well the suggested ResNet50-1D-CNN model worked using the UNSW-NB15 dataset and a control system dataset called HAI. The HAI dataset was taken from the testbed and based on a planned physical attack scenario. By calculating the coefficient scores for the top ten (10) features in the HAI and UNSW-NB15 data, it was possible to determine the relevance of a feature. The rationale behind employing transfer learning was to mitigate the complexity associated with the classification of cyber-attacks and runtime. The utilization of transfer learning resulted in notable reductions in both the training and testing times required for the detection of attacks. On the HAI data, the results showed an accuracy of 97.32 %, recall of 98.41 %, F1-score of 96.32 %, and precision of 97.09 %. On the UNSW-NB15 data, the results showed an accuracy of 99.89 %, recall of 99.09 %, F1-score of 98.01 %, and precision of 98.70 %.

A review on machine learning based intrusion detection system for internet of things enabled environment

Within an internet of things (IoT) environment, the fundamental purpose of various devices is to gather the abundant amount of data that is being generated and then transmit this data to the predetermined server over the internet. IoT connects billions of objects and the internet to communicate without human intervention. But network security and privacy issues are increasing very fast, in today's world. Because of the prevalence of technological advancement in regular activities, internet security has evolved into a necessary requirement. Because technology is integrated into every aspect of contemporary life, cyberattacks on the internet of things represent a bigger danger than attacks against traditional networks. Researchers have found that combining machine learning techniques into an intrusion detection system (IDS) is an efficient way to get beyond the limitations of conventional IDSs in an IoT context. This research presents a comprehensive literature assessment and develops an intrusion detection system that makes use of machine learning techniques to address security problems in an IoT environment. Along with a comprehensive look at the state of the art in terms of intrusion detection systems for IoT-enabled environments, this study also examines the attributes of approaches, common datasets, and existing methods utilized to construct such systems.

Strengthening Wireless Network Security: Supervised Machine Learning-Based Intrusion Detection for Enhanced Threat Mitigation

With the wireless networks deemed vital to our laced and unetched world, it has become fundamental to safeguard these networks against the various cyber threats that are on the rise. As a research proposal I show concern about the application of a Supervised Machine Learning-Based Intrusion Detection system, making use of Multilayer Perceptron (MLP), Support Vector Machine (SVM), and Logistic Regression (LR) algorithms. The survey begins with the introduction of an intrusive inter-dataset technology that merges two catalytic datasets into a single efficient model that can address both model security and data sensitivity concerns. The evaluation of several machine learning algorithms within the proposed framework is a core component of the analysis. The accuracy measure as well as others such as recall, precision, and F1 score, are applied to evaluate the algorithms' level in detecting intrusions from different datasets.The investigation shows that the intra-dataset routing noticeably improves the detecting modules' efficiency. Notably, the model of MLP algorithm has enhanced recall which in turn shows the enhancement of the positive instance identification. SVM shows increased precision, which accounts for the improved accuracy since correct names of positive cases are produced more often. LR finds it overall enhance the precisions thus attesting to its efficiency in correct deductions. The research documents the multifaceted nature of IDS, prompting the intervention of the machine learning algorithms with ddoswdelcome intrusion detection system. A study which provides a practical guide for network administrators and security professionals hinting on how to select algorithms to meet the distinct security needs in their enterprises. These findings contribute to the open discourse on wireless network security and serve as a base for future study of this engaging research topic in constantly developing field of intrusion detection.

An Efficient Deep Learningbased Intrusion Detection System for Internet of Things Networks with Hybrid Feature Reduction and Data Balancing Techniques

With the increasing use of Internet of Things (IoT) technologies, cyber-attacks on IoT devices are also increasing day by day. Detecting attacks on IoT networks before they cause any damage is crucial for ensuring the security of the devices on these networks. In this study, a novel Intrusion Detection System (IDS) was developed for IoT networks. The IoTID20 and BoT-IoT datasets were utilized during the training phase and performance testing of the proposed IDS. A hybrid method combining the Principal Component Analysis (PCA) and the Bat Optimization (BAT) algorithm was proposed for dimensionality reduction on the datasets. The Synthetic Minority Over-SamplingTechnique (SMOTE) was used to address the problem of data imbalance in the classes of the datasets. The Convolutional Neural Networks (CNN) model, a deep learning method, was employed for attack classification. The proposed IDS achieved an accuracy rate of 99.97% for the IoTID20 dataset and 99.98% for the BoT-IoT dataset in attack classification. Furthermore, detailed analyses were conducted to determine the effects of the dimensionality reduction and data balancing models on the classification performance of the proposed IDS.

Optimizing deep learning-based intrusion detection in cloud computing environment with chaotic tunicate swarm algorithm

Intrusion Detection (ID) in cloud environments is vital to maintain the safety and integrity of data and resources. However, the presence of class imbalance, where normal samples significantly outweigh intrusive instances, poses a challenge in constructing a potential ID system. Deep Learning (DL) methods, with their capability to automatically study complex patterns and features, present a promising solution in various ID tasks. Such methods can automatically learn intricate features and patterns from the input dataset, making them suitable for detecting anomalies and finding intrusions in cloud environments. Therefore, this study proposes a Class Imbalance Data Handling with an Optimal Deep Learning-Based Intrusion Detection System (CIDH-ODLIDS) in a cloud computing atmosphere. The CIDH-ODLIDS technique leverages optimal DL-based classification and addresses class imbalance. Primarily, the CIDH-ODLIDS technique preprocesses the input data using a Z-score normalization approach to ensure data quality and consistency. To handle class imbalance, the CIDH-ODLIDS technique employs oversampling techniques, particularly focused on synthetic minority oversampling techniques such as Adaptive Synthetic (ADASYN) sampling. ADASYN generates synthetic instances for the minority class depending on the available data instances, effectively balancing the class distribution and mitigating the impact of class imbalance. For the ID process, the CIDH-ODLIDS technique utilizes a Fuzzy Deep Neural Network (FDNN) model, and its tuning procedure is performed using the Chaotic Tunicate Swarm Algorithm (CTSA). CTSA is employed to choose the learning rate of the FDNN methods optimally. The experimental assessment of the CIDH-ODLIDS method is extensively conducted on three IDS datasets. The comprehensive comparison results confirm the superiority of the CIDH-ODLIDS algorithm over existing techniques.

A Review of Machine Learning-based Intrusion Detection System

Intrusion detection systems are mainly prevalent proclivity within our culture today. Interference exposure systems function as countermeasures to identify web-based protection threats. This is a computer or software program that monitors unauthorized network activity and sends alerts to administrators. Intrusion detection systems scan for known threat signatures and anomalies in normal behaviour. This article also analyzed different types of infringement finding systems and modus operandi, focusing on support-vector-machines; Machine-learning; fuzzy-logic; and supervised-learning. For the KDD dataset, we compared different strategies based on their accuracy. Authors pointed out that using support vector machine and machine learning together improves accuracy.

An Intrusion System for Internet of Things Security Breaches Using Machine Learning Techniques

Effective identification and categorization of network attacks are paramount for ensuring robust security. However, contemporary techniques often struggle to accurately discern and classify novel attack patterns. This research introduces an innovative framework designed to achieve reliable attack detection and classification by harnessing the synergistic capabilities of utilizing DenseNet convolutional neural networks and rap music analysis techniques. Our approach leverages feature extraction through the Attention Pyramid Network (RAPNet) framework, tailored to extract pertinent features from input data, alongside binary Pigeon optimization. Subsequently, we employ feature selection using the optimization algorithm (BPOA). Once the optimal features are identified, we employ the Densenet201 model to categorize attacks across various datasets, including Bot-IoT, CICIDS2017, and CICIDS2019, through deep learning methodologies. To address the challenge posed by imbalanced data, we introduce conditional generative adversarial networks for generating additional data samples for minority classes, thus mitigating the issue. In contrast to recent intrusion detection methods, our results showcase the model’s exceptional precision in detecting and categorizing achieving accuracy rates of 99.12%, 99.01%, and 99.18% for Bot-IoT, CICIDS2017, and CICIDS2019 datasets, respectively. Despite the potential benefits of a machine learning-based intrusion detection system (IDS) for Internet of Things (IoT) security, several limitations must be considered. These include the lack of standardized security protocols across various IoT devices and platforms, which makes it challenging to develop a uniform IDS. Furthermore, machine learning models, including those for intrusion detection, can be vulnerable to adversarial attacks that can circumvent or mislead the model’s decision-making process. Thus, the potential for sophisticated attacks on IoT systems must be considered when developing such a system.

Effective Intrusion Detection System Using Deep Learning for DDoS Attacks

An increasing demand for information technology and cloud computing has led to various security threats over internet. Amongst all types of threats, Distributed Denial of Service (DDoS) attacks are the most common, posing a challenge to a cyber security. It allows unauthorized users to gain access to services, and preventing authorized users. Traditional intrusion detection, antivirus software, and firewalls are not enough to detect these attacks. This paper proposed an efficient deep learning-based intrusion detection system (IDS) for DDoS attacks detection. Proposed model is a hybrid CNN-LSTM model which is combination of best DL algorithms to take advantage of CNN to extract spatial features and LSTM to extract temporal features. The real-time DDoS-specific benchmark-dataset CICDDoS2019 used for training, testing, and validation. An autoencoder has been used for dimensionality reduction, and SMOTE method is used to balance the minority class of dataset. The experimental results proved that the model outperforms than other state-of-the-art models, with an accuracy score of 99.86%.

Machine Learning-Based Intrusion Detection System for Encrypted Attacks

In today's society, information and communication technology is developing rapidly. With the gradual maturity and popularization of encryption technology, more and more malicious attacks are also using encryption technology to evade the scrutiny of traditional traffic detection systems. Therefore, accurate identification of encrypted attacks has become a research hotspot in the international community. This paper proposes an encrypted traffic detection method based on convolutional neural network (CNN) technology to address the issues of tedious steps and low recognition accuracy in manually extracting traffic features. This method does not require manual or expert feature extraction, and can automatically extract advanced features through CNNs, which are then fed into XGBoost classifiers for classification processing. On the basis of the above methods, this article designs and implements an encrypted traffic intrusion detection system, which is divided into five parts: traffic collection, data processing, model detection, data visualization, and traffic blocking. Reasonable explanations and technical introductions are provided for these modules.

Machine learning-based intrusion detection system for detecting web attacks

<p>The increasing use of smart devices results in a huge amount of data, which raises concerns about personal data, including health data and financial data. This data circulates on the network and can encounter network traffic at any time. This traffic can either be normal traffic or an intrusion created by hackers with the aim of injecting abnormal traffic into the network. Firewalls and traditional intrusion detection systems detect attacks based on signature patterns. However, this is not sufficient to detect advanced or unknown attacks. To detect different types of unknown attacks, the use of intelligent techniques is essential. In this paper, we analyse some machine learning techniques proposed in recent years. In this study, several classifications were made to detect anomalous behaviour in network traffic. The models were built and evaluated based on the Canadian Institute for Cybersecurity-intrusion detection systems dataset released in 2017 (CIC-IDS-2017), which includes both current and historical attacks. The experiments were conducted using decision tree, random forest, logistic regression, gaussian naïve bayes, adaptive boosting, and their ensemble approach. The models were evaluated using various evaluation metrics such as accuracy, precision, recall, F1-score, false positive rate, receiver operating characteristic curve, and calibration curve.</p>

Novel Machine Learning Approach for DDoS Cloud Detection: Bayesian-Based CNN and Data Fusion Enhancements.

Cloud computing has revolutionized the information technology landscape, offering businesses the flexibility to adapt to diverse business models without the need for costly on-site servers and network infrastructure. A recent survey reveals that 95% of enterprises have already embraced cloud technology, with 79% of their workloads migrating to cloud environments. However, the deployment of cloud technology introduces significant cybersecurity risks, including network security vulnerabilities, data access control challenges, and the ever-looming threat of cyber-attacks such as Distributed Denial of Service (DDoS) attacks, which pose substantial risks to both cloud and network security. While Intrusion Detection Systems (IDS) have traditionally been employed for DDoS attack detection, prior studies have been constrained by various limitations. In response to these challenges, we present an innovative machine learning approach for DDoS cloud detection, known as the Bayesian-based Convolutional Neural Network (BaysCNN) model. Leveraging the CICDDoS2019 dataset, which encompasses 88 features, we employ Principal Component Analysis (PCA) for dimensionality reduction. Our BaysCNN model comprises 19 layers of analysis, forming the basis for training and validation. Our experimental findings conclusively demonstrate that the BaysCNN model significantly enhances the accuracy of DDoS cloud detection, achieving an impressive average accuracy rate of 99.66% across 13 multi-class attacks. To further elevate the model's performance, we introduce the Data Fusion BaysFusCNN approach, encompassing 27 layers. By leveraging Bayesian methods to estimate uncertainties and integrating features from multiple sources, this approach attains an even higher average accuracy of 99.79% across the same 13 multi-class attacks. Our proposed methodology not only offers valuable insights for the development of robust machine learning-based intrusion detection systems but also enhances the reliability and scalability of IDS in cloud computing environments. This empowers organizations to proactively mitigate security risks and fortify their defenses against malicious cyber-attacks.

Intrusion Detection System

The use of encrypted data, the diversity of new protocols, and the surge in the number of malicious activities worldwide have posed new challenges for intrusion detection systems (IDS). In this scenario, existing signature-based IDS are not performing well. Various researchers have proposed machine learning-based IDS to detect unknown malicious activities based on behaviour patterns. Results have shown that machine learning-based IDS perform better than signature-based IDS (SIDS) in identifying new malicious activities in the communication network. In this paper, the authors have analyzed the IDS dataset that contains the most current common attacks and evaluated the performance of network intrusion detection systems by adopting two data resampling techniques and 10 machine learning classifiers. It has been observed that the top three IDS models—KNeighbors, XGBoost, and AdaBoost—outperform binary-class classification with 99.49%, 99.14%, and 98.75% accuracy, and XGBoost, KNneighbors, and GaussianNB outperform in multi-class classification with 99.30%, 98.88%, and 96.66% accuracy.

An Explainable and Resilient Intrusion Detection System for Industry 5.0

Industry 5.0 is a futuristic transformative model that aims to develop a hyperconnected, automated, and data-driven industrial ecosystem. This digital transformation will boost productivity and efficiency throughout the production process but will be more prone to new sophisticated cyber-attacks. Deep learning-based Intrusion Detection Systems (IDS) have the potential to recognize intrusions with high accuracy. However, these models are complex and are treated as a black box by developers and security analysts due to the inability to interpret the decisions made by these models. Motivated by the challenges, this paper presents an explainable and resilient IDS for Industry 5.0. The proposed IDS is designed by combining bidirectional long short-term memory networks (BiLSTM), a bidirectional-gated recurrent unit (Bi-GRU), fully connected layers and a softmax classifier to enhance the intrusion detection process in Industry 5.0. Then, we employ the SHapley Additive exPlanations (SHAP) mechanism to interpret and understand the features that contributed the most in the decision of the proposed cyber-resilient IDS. The evaluation of the proposed model using the explainability can ensure that the model is working as expected. The experimental results based on the CICIDDoS2019 dataset confirms the superiority of the proposed IDS over some recent approaches.

Cloud Computing Security via Intelligent Intrusion Detection Mechanisms

New challenges of data breaches, unauthorized access, and insider threats have emerged with the rise of Cloud Computing. Attackers find cloud systems attractive due to their common infrastructure. To combat these security threats, strong security procedures must be integrated. An intrusion detection system (IDS) is a vital instrument for safeguarding networks and cloud environments. It tracks system activity and network traffic. This study compares the efficacy of various machine learning models for intrusion detection. The Random-Forest-Classifier model performed the best with a test accuracy of 99.88% in recognizing known and unknown threats. Furthermore, various benefits have been identified when contrasting this intelligent intrusion detection system with conventional rule-based and signature-based methods. The use of machine learning in intrusion detection systems offers enhanced security measures and improved threat detection capabilities, highlighting the importance of leveraging advanced technologies to secure cloud environments. This research illuminates the necessity to employ cutting-edge techniques to protect cloud computing systems from malicious actions in the era of cybersecurity. More than that, it stresses the necessity to invest in enrichment mechanisms in machine learning-based intrusion detection systems to stay abreast with the evolving security problems.

Hybrid deep learning-based intrusion detection system for wireless sensor network

Hybrid deep learning-based intrusion detection system for wireless sensor network

Overview and Exploratory Analyses of CICIDS2017 Intrusion Detection Dataset

Intrusion detection systems are used to detect attacks in a network. Machine learning (ML) approaches have been widely used to build such Intrusion Detection Systems (IDSs) because they are more accurate when built from a very large and representative dataset. In recent times, one of the benchmark datasets that are used to build ML-based Intrusion Detection models is CICIDS2017 dataset. The dataset is contained in eight groups and was collected from the Canadian Institute on Cyber Security dataset repository. The dataset is available in both PCAP and netflow formats. This study used the netflow records in the CIDIDS2017 dataset as they are found to contain newer attacks,very large and are found useful for traffic analysis. Exploratory Data Analysis (EDA) techniques were used to reveal various characteristics of the dataset. The general objective is to provide more insights on the nature, structure and issues with the dataset so as to identify the best ways for using it to achieve improved ML-based IDS models. Furthermore, some of the open problems that can arise from the use of the dataset in any machine learning-based Intrusion Detection systems are highlighted and possible solutions are briefly discussed. The EDA techniques used revealed important relationships among input variables and the target class. The study concluded that the EDA can better influence the decision of future IDS researches that use the dataset. Thus, improved machine learning-based intrusion detection systems can be built from the dataset once it is well understood and pre-processed.

Hybrid deep learning-based intrusion detection system for wireless sensor network

Hybrid deep learning-based intrusion detection system for wireless sensor network

ANALYZING THE CAN PROTOCOL: VULNERABILITIES, PROTECTIVE MEASURES AND IMPROVEMENTS

The Controller Area Network (CAN) bus, originally designed for internal communication among a limited number of Electronic Control Units (ECUs) within vehicles, faces significant security challenges due to the rapid expansion of ECUs in modern automobiles. This expansion necessitates accessibility for diagnostic purposes, yet the CAN protocol lacks fundamental security features such as encryption, authentication, and integrity checks, rendering it vulnerable to various attacks including message injection, Denial of Service (DoS), and masquerading ECUs. This paper surveys existing literature and investigates potential intrusions on the CAN bus, highlighting attacks ranging from GPS spoofing to remote sensor tampering. Various solutions are discussed, including network subdivision, encryption, authentication techniques, and intrusion detection systems (IDS). Recent research proposes IDS solutions utilizing machine learning algorithms such as Random Forest, Support Vector Machine, and Deep Neural Networks (DNN), demonstrating effectiveness in detecting known attacks. However, challenges persist in identifying unknown attacks and enhancing overall system performance. Innovative approaches, such as event-triggered detection and bloom filtering techniques, show promise in mitigating specific attack vectors but may introduce overhead or limitations in real-time response. Deep learning-based IDS systems exhibit high performance but struggle with the detection of novel attacks. Furthermore, while some solutions address specific vulnerabilities, others propose more comprehensive security measures, such as preventing remote manipulation of critical vehicle functions like steering and braking. Overall, the research emphasizes the critical need for robust security measures in automotive systems to safeguard against evolving threats to vehicle safety and integrity.

Implementation and Evaluation of Intrusion Detection Systems using Machine Learning Classifiers on Network Traffic Data

Strong Intrusion Detection Systems (IDS) are now essential given how much more crucial services and communication are being reliant on digital networks. Through the use of machine learning classifiers on network traffic data, this research shows the deployment and thorough evaluation of IDS. The first step of the study is to gather and preprocess a wide dataset of network traffic, which includes both legitimate and criminal operations. A high-dimensional feature set is produced when important information is extracted from the raw data using feature engineering techniques. In order to simulate the patterns of network traffic, a variety of machine learning methods are used, such as Decision Trees, Random Forests, Support Vector Machines, and Neural Networks. The models are also put to the test in a variety of situations, such as those with changing levels of network traffic, different kinds of attacks, and false-positive rates. Results show that machine learning-based IDS is more accurate than conventional rule-based systems at identifying and categorising network intrusions. Assessments are made of the models' ability to scale up and change to accommodate new threats. A thorough examination of IDS utilising machine learning classifiers on actual network traffic data is provided in this research, which, in turn, advances network security. The results highlight the value of machine learning in improving the precision and sturdiness of intrusion detection systems and protecting crucial network infrastructures from new cyber threats.

A simple framework to enhance the adversarial robustness of deep learning-based intrusion detection system

Deep learning based intrusion detection systems (DL-based IDS) have emerged as one of the best choices for providing security solutions against various network intrusion attacks. However, due to the emergence and development of adversarial deep learning technologies, it becomes challenging for the adoption of DL models into IDS. In this paper, we propose a novel IDS architecture that can enhance the robustness of IDS against adversarial attacks by combining conventional machine learning (ML) models and Deep Learning models. The proposed DLL-IDS consists of three components: DL-based IDS, adversarial example (AE) detector, and ML-based IDS. We first develop a novel AE detector based on the local intrinsic dimensionality (LID). Then, we exploit the low attack transferability between DL models and ML models to find a robust ML model that can assist us in determining the maliciousness of AEs. If the input traffic is detected as an AE, the ML-based IDS will predict the maliciousness of input traffic, otherwise the DL-based IDS will work for the prediction. The fusion mechanism can leverage the high prediction accuracy of DL models and low attack transferability between DL models and ML models to improve the robustness of the whole system. In our experiments, we observe a significant improvement in the prediction performance of the IDS when subjected to adversarial attack, achieving high accuracy with low resource consumption.