Machine Learning Based Intrusion Detection Research Articles

ROSPaCe: Intrusion Detection Dataset for a ROS2-Based Cyber-Physical System and IoT Networks

Most of the intrusion detection datasets to research machine learning-based intrusion detection systems (IDSs) are devoted to cyber-only systems, and they typically collect data from one architectural layer. Often the attacks are generated in dedicated attack sessions, without reproducing the realistic alternation and overlap of normal and attack actions. We present a dataset for intrusion detection by performing penetration testing on an embedded cyber-physical system built over Robot Operating System 2 (ROS2). Features are monitored from three architectural layers: the Linux operating system, the network, and the ROS2 services. The dataset is structured as a time series and describes the expected behavior of the system and its response to ROS2-specific attacks: it repeatedly alternates periods of attack-free operation with periods when a specific attack is being performed. This allows measuring the time to detect an attacker and the number of malicious activities performed before detection. Also, it allows training an intrusion detector to minimize both, by taking advantage of the numerous alternating periods of normal and attack operations.

Blockchain Assisted Fireworks Optimization with Machine Learning based Intrusion Detection System (IDS)

Blockchain Assisted Fireworks Optimization with Machine Learning based Intrusion Detection System (IDS)

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.

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 Generic Adversarial Networks (CGAN) 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 for 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 intrusion detection system. 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.

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.

Machine Learning-based Intrusion Detection Technique for IoT: Simulation with Cooja

Machine Learning-based Intrusion Detection Technique for IoT: Simulation with Cooja

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.

Strategizing IoT Network Layer Security Through Advanced Intrusion Detection Systems and AI-Driven Threat Analysis

This research introduces an algorithmic framework for enhancing the security of Internet of Things (IoT) networks. The Enhanced Anomaly Detection (EAD) algorithm initiates the process by detecting anomalies in real-time IoT data, serving as the foundational layer. The Behavior Analysis for Profiling (BAP) algorithm builds upon EAD, adding behavior analysis for profiling and adaptive identification of abnormal behavior. Signature-Based Detection (SBD) involves pre-identified attack signatures, which supports detection of known attacks and provides proactive defense measures against documented threats. The MLID, or the Machine Learning-Based Intrusion Detection, algorithm uses trained machine learning models in order to detect anomalies and the adaptability to changing security risks. The Real-Time Threat Intelligence Integration (RTI) algorithm integrates updated threat intelligence feeds, which improves the framework's responsiveness to emerging threats. The visual representations illustrate once again the idea of the new framework being very accurate at intergration, applicability, and overal security effectiveness. The research makes a standard solution which proves to be a smart and responsive way guarding the IoT networks reducing and even fighting known and potential threats in a real-time mode.

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.

Safeguarding Connections: Machine Learning Powered Intrusion Detection

Abstract: It's crucial to have reliable intrusion detection systems. a cutting-edge method of machine learning-based intrusion detection. Our solution uses cutting-edge algorithms to detect and eliminate any threats instantly, acting as a preventative measure against a wide range of cyberattacks. Since the model has been trained on a large number of datasets, it can eventually strengthen network security by evolving and adapting to new threats. Naive Bayes (NB) classifiers and correlation-based feature selection (CFS) methods are used to reduce the amount of data. For attack classification, the Intrusion Detection System recommends using an Instance-Based Learning algorithm (IBK) in combination with a Multilayer Perceptron (MLP).

Optimized machine learning enabled intrusion detection 2 system for internet of medical things

This research paper proposes an intrusion detection system that combines particle swarm optimization and AdaBoost algorithms to classify and detect malware-related records in innovative health app platforms. The research employs the NSL KDD dataset, consisting of 125,973 instances and 41 features, with data split into training (20 %) and testing (80 %) sets. A feature selection process using Particle Swarm Optimization identifies 12 relevant features for intrusion detection. The intrusion detection system effectively categorizes various attack types, including Denial of Service (DoS), User-to-root (U2R), Root-to-local (R2L), and Probe attacks. Regarding classifier performance, AdaBoost exhibits the highest recall value (0.966667), highlighting its strong intrusion detection capabilities. The experimental results demonstrate that the PSO-AdaBoost approach achieves superior accuracy, precision, and recall in intrusion detection. By integrating machine learning-based intrusion detection systems into smart health apps, healthcare providers can enhance patient care, reduce costs, and ensure the confidentiality of sensitive medical information. This research highlights the potential of ML-IDSs in strengthening the security infrastructure surrounding medical IoT devices and improving patient outcomes in the interconnected world of the Internet of Medical Things.

The intrinsic dimensionality of network datasets and its applications1

Modern network infrastructures are in a constant state of transformation, in large part due to the exponential growth of Internet of Things (IoT) devices. The unique properties of IoT-connected networks, such as heterogeneity and non-standardized protocol, have created critical security holes and network mismanagement. In this paper we propose a new measurement tool, Intrinsic Dimensionality (ID), to aid in analyzing and classifying network traffic. A proxy for dataset complexity, ID can be used to understand the network as a whole, aiding in tasks such as network management and provisioning. We use ID to evaluate several modern network datasets empirically. Showing that, for network and device-level data, generated using IoT methodologies, the ID of the data fits into a low dimensional representation. Additionally we explore network data complexity at the sample level using Local Intrinsic Dimensionality (LID) and propose a novel unsupervised intrusion detection technique, the Weighted Hamming LID Estimator. We show that the algortihm performs better on IoT network datasets than the Autoencoder, KNN, and Isolation Forests. Finally, we propose the use of synthetic data as an additional tool for both network data measurement as well as intrusion detection. Synthetically generated data can aid in building a more robust network dataset, while also helping in downstream tasks such as machine learning based intrusion detection models. We explore the effects of synthetic data on ID measurements, as well as its role in intrusion detection systems.

Binary Arithmetic Optimization Algorithm with Machine Learning based Intrusion Detection System

Intrusion Detection Systems (IDS) are significant for preventing and identifying malicious actions in computer networks. Machine Learning (ML) approaches are extremely executed for recognizing intrusion since it is investigating huge volumes of network traffic data and recognize designs indicative of intrusions. But, the performance of these ML approaches is greatly dependent upon the choice of relevant features which efficiently represent the network traffic data. Feature Selection (FS) is the procedure of recognizing the most informative and discriminative aspects in a given database. As part of Intrusion Detection (ID) utilizing ML, FS purposes for identifying the subset of features that are efficiently differentiated between normal network behaviour and malicious activities. This article proposes a Binary Arithmetic Optimization Algorithm with Machine Learning based Intrusion Detection System (BAOA-MLIDS) technique. The BAOA-MLIDS technique employs FS with an optimal ML classifier for the ID process. To accomplish this, the BAOA-MLIDS technique performs data preprocessing to scale the input data. Besides, the BAOA-MLIDS technique comprises BAOA based FS approach to choose optimal features. Moreover, Extreme Learning Machine (ELM) approach is utilized for the identification of the intrusions. Furthermore, Hunger Games Search Optimization (HGSO) approach was employed for the hyperparameter optimization of the ELM approach. The performance assessment of the BAOA-MLIDS model was examined on a standard dataset and the outputs outperformed the advancement of the BAOA-MLIDS model in the ID process.

A framework for detection of cyber attacks by the classification of intrusion detection datasets

Recognition of the consequence for advanced tools and techniques to secure the network infrastructure from the security risks has prompted the advancement of many machine learning-based intrusion detection strategies. However, it is a big challenge for the researchers to make improvements in an Intrusion Detection System with desired advantages and constraints. This paper has developed a proficient soft computing framework using Grey Wolf Optimization and Entropy-Based Graph (GWO-EBG) to classify intrusion detection datasets to reduce the false rate. In the proposed scheme, initially, the input data is preprocessed by the data transformation and normalization procedure. After the preprocessing, optimal features have been chosen for the dimension reduction from the preprocessed data using the grey wolf optimization (GWO) algorithm. Then, the Entropy value has estimated from the idyllically selected features. Lastly, an Entropy-Based Graph (EBG) has been constructed to classify data into intrusion or normal data. The experimental results demonstrate that the developed method outperforms other existing methods in various performance measures. The detection rate of the developed GWO-EBG is found to be 94.6%, which is higher than 91.24 % of EBG, 75.60 % K-Nearest Neighbors (KNN), 73.36 % of Support Vector Machine (SVM), and 74.88 % of Generalized Regression Neural Network (GRNN) on 5000 connection vectors data obtained from KDD CUP’99 testing dataset. The false-positive rate of developed strategy (GWO-EBG) is 0.35 %%, which is lower than 2.18 % of EBG, 7.32 % KNN, 8.15 % of SVM, and 8.13 % of GRNN with 5000 testing datasets.

Machine Learning-Based Intrusion Detection System for Cyber Attacks in Private and Public Organizations

Cyber-attacks have proven to be a force for hacking groups and state-sponsored organizations seeking to level the playing field with competitors. The hacker threat paired with the enormously hazardous and costly danger of fraud or intellectual property theft by insiders has created a volatile situation in private and public organizations. While a majority of internal breaches are due to employee negligence or human error, attacks by malicious insiders with access to sensitive company information have increased dramatically in recent years. Threats of financial loss, theft of sensitive information, and destruction to critical sectors have made cybersecurity a top security priority around the globe. Whereas the increase in frequency and complexity of attacks on the industry has increased the danger of being unprepared, it also has influenced the cost of preventing and recovering from cyber-attacks. To construct a machine learning bases instruction detection system is capable of detecting Cyber-attacks in the private and public sectors in Nigeria and the whole world. The results show that Random Forest and Random Tree algorithms outperform the other algorithms in their level of precision and F-measure as they are above 99% and 98% respectively, while the Random Forest outperforms the others by its detection rate. However, the Random Forest and Random Tree algorithms are more efficient in performing classification exercise on the Test datasets