Intrusion Data Research Articles

Three-dimensional spatial structure and heterogeneity characterization of #5 coal of Shanxi Formation in eastern Ordos basin

Based on macroscopic qualitative observation, microscopic quantitative analysis, low-temperature adsorption, high-pressure mercury intrusion and three-dimensional CT scanning, the spatial structure and heterogeneity of the #5 coal from the eastern Ordos Basin, Shanxi Formation, are quantitatively characterized. The following observation were studied: 1) The main body of the #5 coal was intact structural coal seam, with the density of cleats gradually increasing from north to south. 2) The microscopic composition of the #5 coal is dominated by desmocollinite, which contains small amounts of inertinite and inorganic minerals, with the inorganic component mainly composed of carbonate rocks and a small amount of clay. The carbon content and the content of its inorganic minerals in the #5 coal gradually decrease toward the inner basin, caused by transport distance and depositional environment. 3) At the micrometer scale, no significant pores were observed, and the #5 coal bed is primarily composed of micro-mesopores smaller than 40 nm, with a small number of 50∼100 nm macropores. Simultaneously, micro-fractures in the coal bed were well developed; by using mercury intrusion data of the bimodal feature of the pore throat, indicates the coexistence of micrometer-scale micro-fractures and nanometer-scale mesopores within the coal, with pronounced differences and interconnected complexity. 4) The porosity and permeability of microfractures, as well as the number of microfracture bars and permeability, indicate that the connectivity of coal bed is primarily caused by the microfracture system. By reconstructing the three-dimensional spatial structure of the #5 coal through multiple tests, the heterogeneous characteristics are quantitatively characterized.

Network Data Intrusion Detection and Data Feature Extraction of Electromechanical Facilities from Machine Learning

With the rapid development of Internet technology, network security issues have become more complex and changeable. Various intrusion methods threaten the information network environment in the Electromechanical Facility (EF) system. This paper focuses on EF to study the relevant detection methods and data feature extraction in complex network intrusions. Firstly, four common machine learning algorithms are used to calculate the data set. The advantages and disadvantages of each algorithm are analyzed after tuning and comparison. Secondly, a network intrusion detection algorithm is proposed based on Recursive Feature Elimination (RFE) principal component analysis. It uses RFE to reduce the number of features and improve the elimination judgment index to align with the detection requirements of information network datasets. Finally, a fault diagnosis method is proposed based on empirical pattern decomposition and support vector machine under Renyi entropy complexity measurement. This method trains and identifies the Renyi entropy of several basic pattern components obtained by decomposing empirical patterns as feature vectors. The results show that the RFE method judged by random forest removes irrelevant features, and the evaluation index is improved to align with the network dataset’s detection requirements. It reduces the data dimension, reduces the operation time, and improves the accuracy of a few attack types. The comprehensive final detection effect is better than other algorithms. Additionally, the embedded operating system construction method based on the protection mechanism realizes the separate storage of the operating system and key data. Also, it can prevent the network system from being maliciously invaded, ensuring the stability of the instrument operation under harsh working conditions.

HIDIM: A novel framework of network intrusion detection for hierarchical dependency and class imbalance

Deep learning-based network intrusion detection has been extensively explored as a data-driven approach. Therefore, paying attention to the data’s characteristics is essential. By analyzing the attribute dependence and sample distribution of intrusion data, there are the following problems: “hierarchical dependency omission” and “decision boundary discontinuity.” The former means the previous attribute embedding models failed to incorporate network protocol hierarchy. The latter indicates that the small disjuncts distribution leads to sub-concept fragmentation, exacerbating the difficulty in handling class imbalance. To address these problems, we propose a novel detection framework for Hierarchical Dependency and Class Imbalance (HIDIM). First, we treat semantic attributes as words and introduce the protocol hierarchy of attributes into a paragraph embedding model. Second, we design a synthetic oversampling method. It adopts a mutual nearest neighbour approach to determine the boundaries of each disjunct. Then, it synthesizes high-quality samples within those boundary areas by crossing or mutating features based on their importance. The experimental results on multiple real-world datasets demonstrate that the proposed framework is superior to other state-of-the-art models in terms of accuracy, F1-score, and false negative rate by 2.23%, 2.12%, and 1.43% on average, respectively.

Exploring Generative Adversarial Networks for Augmenting Network Intrusion Detection Tasks

The advent of generative networks and their adoption in numerous domains and communities have led to a wave of innovation and breakthroughs in artificial intelligence and machine learning. Generative Adversarial Networks (GANs) have expanded the scope of what is possible with machine learning, allowing for new applications in areas such as computer vision, natural language processing, and creative AI. GANs, in particular, have been used for a wide range of tasks, including image and video generation, data augmentation, style transfer, and anomaly detection. They have also been used for medical imaging and drug discovery, where they can generate synthetic data to augment small datasets, reduce the need for expensive experiments, and lower the number of real patients that must be included in medical trials. Given these developments, we propose using the power of generative adversarial networks to create and augment flow-based network traffic datasets. We evaluate a series of GAN architectures, including Wasserstein, conditional, energy-based, gradient penalty, and LSTM GANs. We evaluate their performance on a set of flow-based network traffic data collected from 16 subjects who used their computers for home, work, and study purposes. The performance of these GAN architectures is described according to metrics that involve networking principles, data distribution among a collection of flows, and temporal data distribution. Given the tendency of network intrusion detection datasets to have a very imbalanced data distribution, i.e., a large number of samples in the “normal traffic” category and a comparatively low number of samples assigned to the “intrusion” categories, we test our GANs by augmenting the intrusion data and checking whether this helps intrusion detection neural networks in their task. We publish the resulting UPBFlow dataset and code on GitHub 1 .

Radial Basis Function Neural Networks (RBFNN) for Secure Intrusion Detection in the Internet of Drones (IoD)

With the rapid rise of the Internet of Things (IoT), drones are being used in various applications such as surveillance and delivery services. Despite this, there are serious security threats as well. This study proposes an innovative secure intrusion detection platform for the Internet of Drones (IoD) framework in order to address this problem. We use Radial Basis Function Neural Networks (RBFNN) and Non-Linear Partial Differential Equations (NL-PDEs) technique to enhance the intrusion detection capabilities. In addition the incorporation of block-chain technology fortifies the security and transparency of the system, ensuring the integrity of captured intrusion data. The proposed technique has been rigorously tested and evaluated to determine its accuracy in identifying malicious attacks and unauthorized access scenarios. Through this methodology, the Internet of Drones (IoD) security framework is strengthened, thereby mitigating potential risks derived from their widespread use. The main objective of this study is to make it easier for drone technology to be adopted and used safely and securely in a variety of contexts and industries.

Research on Network Intrusion Detection Technology Based on Artificial Intelligence

As network intrusion becomes more and more complicated and network characteristics become more and more diverse, the traditional network intrusion detection technology has some problems such as high false alarm rate, poor adaptability and low detection rate. The article provides an in-depth analysis of network intrusion detection techniques based on artificial intelligence to better solve these problems. In recent years, we have been witnessing great progress in image recognition, speech recognition, and natural language processing. The powerful advantage of artificial intelligence in processing large-scale and complex data provides new ideas for the processing of multi-attribute intrusion data. The introduction of AI technology in network intrusion detection can effectively improve the detection accuracy and reduce the false alarm rate and omission rate. Based on this, the article analyses the artificial intelligence network intrusion detection technology and discusses the network security management based on artificial intelligence for the reference of related personnel.

IoV-BCFL: An intrusion detection method for IoV based on blockchain and federated learning

In recent years, Internet of Vehicles (IoV) is in a booming stage. But at the same time, the methods of attack against IoV such as Denial of Service (DoS) and deception are great threats to personal and social security. Traditional IoV intrusion detection usually adopts a centralized detection model, which has the disadvantages of untimely detection results and is difficult to protect vehicle privacy in practical applications. Meanwhile, centralized computation requires a large amount of vehicle data transmission, which overloads the wireless bandwidth. Combined the distributed computing resources of Federated Learning (FL) and the decentralized features of blockchain, an IoV intrusion detection framework named IoV-BCFL is proposed, which is capable of distributed intrusion detection and reliable logging with privacy protection. FL is used for distributing training on vehicle nodes and aggregating the training models at Road Side Unit (RSU) to reduce data transmission, protect the privacy of training data, and ensure the security of the model. A blockchain-based intrusion logging mechanism is presented, which enhances vehicle privacy protection through Rivest-Shamir-Adleman (RSA) algorithm encryption and uses Inter Planetary File System (IPFS) to store the intrusion logs. The intrusion behavior can be faithfully recorded by logging smart contract after detecting the intrusion, which can be used to track intruders, analyze security vulnerabilities, and collect evidence. Experiments based on different open source datasets show that FL achieves a high detection rates on intrusion data and effectively reduce the communication overhead, the smart contract performs well on evaluation indicators such as sending rate, latency, and throughput.

An Enhanced Model for Intrusion Detection in a Cloud Computing Environment

Intrusion is an important issue in computer networks especially in cloud computing where all the services are served using the internet. The fully distributed and open structure of cloud computing and services has made it an even more attractive target for potential intruders. The more sophisticated hackers and attackers get, the more there is work for the defense to prevent such attacks. A cloud computing system can be exposed to threats which include the integrity, confidentiality, and availability of its resources, its data, and the virtualized infrastructure can be vulnerable. The problem becomes bigger when an internal intruder misuses a cloud with massive computing power and storage capacity as a malicious party. This research developed an enhanced model for intrusion detection that monitors and analyzes data in a cloud environment and detects intrusion in the system or network. The model can detect intrusions from external and malicious internal (authorized and unauthorized) users by normalizing and classifying all data packets using machine learning techniques. The developed system is an enhanced model of Zhang by combining it with two machine learning techniques: Support vector machine and Bayesian network to aid in the classification of normal data and intrusion data to detect intrusions. The developed model is evaluated and found to be able to make strong predictions, detect attacks, and still maintain the efficiency of the network. The system, when implemented, can detect intruders by classification of data packets and also improve the existing system in terms of providing more accurate and more efficient intrusion detection. It also provides worthwhile information about malicious network traffic, helping to identify the source of the incoming probes or attacks, collecting forensic evidence that can be used to identify intruders, and alerting security personnel that a network invasion may be in progress.

An Intrusion Detection Model Based on Extra Trees Algorithm with Dimensionality Reduction and Oversampling

With the advancement of the university information process, more and more application systems are running on the campus network, and the information system becomes larger and more complex. With the rapid growth of network users and the popularization and deepening of computer knowledge, the campus network has been transformed from an experimental network for education and scientific research into an operational network that attaches equal importance to education, scientific research, and service. As the most important transmission carrier of digital information, how to ensure its security has become an urgent issue for colleges and universities. Therefore, this paper uses advanced intrusion detection technology to design the corresponding model to solve the security problem of the campus network. When traditional machine learning algorithms train network intrusion data sets, they are prone to problems such as too many feature dimensions, overfitting, and imbalance of data sets, which lead to lower accuracy and low time efficiency of intrusion detection algorithms. In order to solve the above problems, this paper proposes an intrusion detection model based on Extra Trees, which uses linear discriminant analysis to reduce the dimension of data, then uses oversampling to reduce the influence of imbalance of sample categories of the network intrusion dataset, and finally uses Extra Trees algorithm to train the model. The experimental results show that after LDA reduction and oversampling, using the Extra Trees classification model can improve the overall recognition performance of imbalanced data sets under multi-classification and satisfy the network intrusion detection.

Cross-layer detection and defence mechanism against DDoS and DRDoS attacks in software-defined networks using P4 switches

This paper presents a comprehensive system for detecting and defending against distributed denial-of-service (DDoS) and distributed reflective denial-of-service (DRDoS) flood attacks in software-defined networking (SDN) environments using Programming Protocol-Independent Packet Processors (P4). The proposed system introduces a hybrid intrusion statistical threshold algorithm (HISTA) that analyzes intrusion data statistics to identify potential malicious attacks. Upon detection, the system utilises P4 programmability to implement a custom defence mechanism on the P4 switch. A novel Uruk protocol header collaborates with HISTA for enhanced cross-layer attack detection and categorisation. The HISTA-Uruk mechanism selectively discards malicious packets while ensuring uninterrupted communication for legitimate packets, effectively preventing DDoS/DRDoS attacks. Extensive experiments validate the system’s ability to efficiently detect and thwart various DDoS, DRDoS, and internal attacks, demonstrating its effectiveness in identifying threats and restoring impacted network connections across diverse attack scenarios.

Data Privacy Assurance in Management Information System through Cyber Security Measures

In the digital age, safeguarding data within Management Information Systems (MIS) is critical. This study investigates the influence of cyber security measures on data privacy assurance in MIS. Drawing from the Information System Theory, the research examines encryption techniques, firewalls, and intrusion detection systems. The literature underscores the importance of robust cyber security measures. Encryption techniques, firewalls, and intrusion detection systems play pivotal roles in data privacy assurance. Hypotheses are formulated based on theoretical underpinnings and prior research. Methodologically, a stratified random sampling technique gathers data from diverse organizations, resulting in 67 responses. SMART PLS 4 analyzes data considering the exploratory nature of the study. Results reveal the significant impact of encryption techniques and intrusion detection systems on data privacy assurance. Encryption techniques exhibit a positive relationship, while firewalls contribute modestly. A counterintuitive negative relationship between intrusion detection systems and data privacy assurance prompts further investigation. These findings emphasize the multifaceted nature of data privacy assurance in MIS. Organizations are encouraged to leverage encryption techniques and enhance firewall strategies. The unexpected negative impact of intrusion detection systems highlights the complex dynamics of security. This study contributes to discussions on data privacy and security in organizational contexts. Future research should explore underlying mechanisms behind counterintuitive relationships and the intersection of technology and security in MIS

Network Intrusion Detection Using Optimal Perception with Cuckoo Algorithm

ABSTRACT To safeguard computer networks from intruders, intrusion detection systems have been created. These systems operate in conjunction with firewalls and other security measures to guarantee the safety and efficiency of the computer system. An intrusion detection system is a tool designed to detect and pinpoint attacks and vulnerabilities within a network or computer system. It subsequently notifies the system administrator of them. The primary challenge with intrusion detection systems is enhancing their speed and precision in detecting intruders. This article explores a novel technique for identifying attempts to infiltrate computer systems. The system utilizes a hybrid approach involving the cuckoo algorithm and perceptron neural network. This novel approach can detect intrusion data more accurately than previous methods and enhance the detection rate by over 1%. The system utilizes the cuckoo method to choose a subset of characteristics, which are then analyzed based on the frequency of various attribute types in intrusive and normal data using an optimum perceptron. The system has been evaluated and the implementation has yielded a detection accuracy of 89.8%, representing a substantial enhancement compared to earlier approaches.

Lower limits of physical properties and classification evaluation criteria of the tight reservoir in the Ahe Formation in the Dibei Area of the Kuqa depression

Abstract The Ahe Formation in the Dibei Area is a key natural gas exploration formation in the northern structural belt. Based on geological data such as formation tests and physical properties, the tight reservoirs were determined using the empirical statistics method, distribution function method, oil-bearing occurrence method, and bound fluid saturation method reasonably. The lower limit of the oil-bearing physical properties of the layer was further analyzed for the differences between the methods. The pore-throat structure of tight sandstone was characterized by high-pressure mercury intrusion data, and the classification and evaluation standard of tight sandstone in the Ahe Formation in the Dibei Area was established combining fractal theory and physical property data. The results show that the lower physical limit porosity of the tight reservoir of the Ahe Formation in the Dibei Area is 2.4%, and the lower permeability limit is 0.021 × 10−3 μm2. According to the fractal dimension characteristics of the mercury injection curve, the different structural characteristics of four types of pores (fracture, macropore, mesopore, and small pore) can be divided with the boundary values are 3,000, 1,000, and 100 nm; the tight reservoir of the Ahe Formation in Dibei Area can be classified into four categories: (a) type I reservoir (Ф > 7%), mainly composed of fracture and macropore; (b) type II reservoir (4% < Ф < 7%), mainly made up of macropore and mesopore; (c) type III reservoir (2% < Ф < 4%), mainly formed by mesopore; and (d) type IV reservoir (Ф < 2%) dominated by small pore, followed by mesopore.

A novel data-driven integrated detection method for network intrusion classification based on multi-feature imbalanced data

The multi-feature and imbalanced nature of network data has always been a challenge to be overcome in the field of network intrusion detection. The redundant features in data could reduce the overall quality of network data and the accuracy of detection models, because imbalance could lead to a decrease in the detection rate for minority classes. To improve the detection accuracy for imbalanced intrusion data, we develop a data-driven integrated detection method, which utilizes Recursive Feature Elimination (RFE) for feature selection, and screens out features that are conducive to model recognition for improving the overall quality of data analysis. In this work, we also apply the Adaptive Synthetic Sampling (ADASYN) method to generate the input data close to the original dataset, which aims to eliminate the data imbalance in the studied intrusion detection model. Besides, a novel VGG-ResNet classification algorithm is also proposed via integrating the convolutional block with the output feature map size of 128 from the Visual Geometry Group 16 (VGG16) of the deep learning algorithm and the residual block with output feature map size of 256 from the Residual Network 18 (ResNet18). Based on the numerical results conducted on the well-known NSL-KDD dataset and UNSW-NB15 dataset, it illustrates that our method can achieve the accuracy rates of 86.31% and 82.56% in those two test datasets, respectively. Moreover, it can be found that the present algorithm can achieve a better accuracy and performance in the experiments of comparing our method with several existing algorithms proposed in the recent three years.

A Deep Learning Approach of Intrusion Detection and Tracking with UAV-Based 360° Camera and 3-Axis Gimbal

Intrusion detection is often used in scenarios such as airports and essential facilities. Based on UAVs equipped with optical payloads, intrusion detection from an aerial perspective can be realized. However, due to the limited field of view of the camera, it is difficult to achieve large-scale continuous tracking of intrusion targets. In this study, we proposed an intrusion target detection and tracking method based on the fusion of a 360° panoramic camera and a 3-axis gimbal, and designed a detection model covering five types of intrusion targets. During the research process, the multi-rotor UAV platform was built. Then, based on a field flight test, 3043 flight images taken by a 360° panoramic camera and a 3-axis gimbal in various environments were collected, and an intrusion data set was produced. Subsequently, considering the applicability of the YOLO model in intrusion target detection, this paper proposes an improved YOLOv5s-360ID model based on the original YOLOv5-s model. This model improved and optimized the anchor box of the YOLOv5-s model according to the characteristics of the intrusion target. It used the K-Means++ clustering algorithm to regain the anchor box that matches the small target detection task. It also introduced the EIoU loss function to replace the original CIoU loss function. The target bounding box regression loss function made the intrusion target detection model more efficient while ensuring high detection accuracy. The performance of the UAV platform was assessed using the detection model to complete the test flight verification in an actual scene. The experimental results showed that the mean average precision (mAP) of the YOLOv5s-360ID was 75.2%, which is better than the original YOLOv5-s model of 72.4%, and the real-time detection frame rate of the intrusion detection was 31 FPS, which validated the real-time performance of the detection model. The gimbal tracking control algorithm for intrusion targets is also validated. The experimental results demonstrate that the system can enhance intrusion targets’ detection and tracking range.

Active Security Defense Model for New Power Systems

This article proposes a C-PDR active dynamic security model based on the WPDRRC network dynamic security model, combined with the new power system security characteristics, starting from pre judgment, in-process detection, and post response. This model constructs a comprehensive defense in depth through boundary defense, network security protection, host intrusion prevention, application and data intrusion prevention, and can fundamentally address the network threats faced by the power system.

Comparative Evaluation of VAEs, VAE-GANs and AAEs for Anomaly Detection in Network Intrusion Data

With cyberattacks growing in frequency and sophistication, effective anomaly detection is critical for securing networks and systems. This study provides a comparative evaluation of deep generative models for detecting anomalies in network intrusion data. The key objective is to determine the most accurate model architecture. Variational autoencoders (VAEs), VAE-GANs, and adversarial autoencoders (AAEs) are tested on the NSL-KDD dataset containing normal traffic and different attack types. Results show that AAEs significantly outperform VAEs and VAE-GANs, achieving AUC scores up to 0.96 and F1 scores of 0.76 on novel attacks. The adversarial regularization of AAEs enables superior generalization capabilities compared to standard VAEs. VAE-GANs exhibit better accuracy than VAEs, demonstrating the benefits of adversarial training. However, VAE-GANs have higher computational requirements. The findings provide strong evidence that AAEs are the most effective deep anomaly detection technique for intrusion detection systems. This study delivers novel insights into optimizing deep learning architectures for cyber defense. The comparative evaluation methodology and results will aid researchers and practitioners in selecting appropriate models for operational network security.

Update Algorithm of Secure Computer Database Based on Deep Belief Network

In order to ensure the security of large-scale data transmission in a short time and in a wide range during online database updating, this paper presents a secure computer database updating algorithm based on DBN (Deep Belief Network). In this paper, the model adopts multi-layer depth structure for unsupervised feature learning, maps high-dimensional and nonlinear intrusion data to low-dimensional space, establishes the relationship mapping between high-dimensional and low-dimensional, and then uses fine-tuning algorithm to transform the model to achieve the best expression of features. At the same time, this method improves the data processing and method model without destroying the learned knowledge of the model and seriously affecting the real-time performance of detection. In order to overcome the problem of system instability caused by fixed empirical learning rate, this paper proposes a learning rate optimization strategy based on energy change. In the process of feature extraction, the features of different hidden layers are extracted to form combined features. Experiments show that the detection rate of this method can reach 95.31%, and the false alarm rate is 2.14%. This verifies the effectiveness of the secure computer database updating algorithm in this paper. Which can ensure the online update of the secure computer database.

Hough Transform based Deep Belief Network and Improved Homomorphic Encryption for Cloud Security based Intrusion Discovery

The enlarge development in information technology is cloud computing, which offers minimized infrastructure cost, lower maintenance, greater flexibility and scalability. Nowadays, the network security plays vital role in enterprises and organizations. The influence vulnerabilities were occurred due to attackers based on network configuration. Because of cloud and IoT growth, enlarge amount of data obtained from IoT sensor and devices are transmitted to cloud data centers. Several security issues like focused web servers in the cloud and information collection mishandling are faced by storage and cloud-based computing when offering us considerable convenience. For that reason, this article proposes a deep learning-based cloud security oriented intrusion discovery. Primarily, the input dataset is pre-processed by using normalization techniques followed by the features are selected using an Adaptive White Shark Optimization (AWSO) algorithm. The normal and intrusion data is classified by using Hough Transform based Deep Belief Network (HT-DBN) after that the sensitive data are secured with the help of an Improved Homomorphic Encryption (IHE) model. The simulation tool of MATLAB is been used to simulate the proposed implementation part and the experimental results outperformed the detection accuracy of 97% than other previous approaches.

Hough Transform based Deep Belief Network and Improved Homomorphic Encryption for Cloud Security based Intrusion Discovery

The enlarge development in information technology is cloud computing, which offers minimized infrastructure cost, lower maintenance, greater flexibility and scalability. Nowadays, the network security plays vital role in enterprises and organizations. The influence vulnerabilities were occurred due to attackers based on network configuration. Because of cloud and IoT growth, enlarge amount of data obtained from IoT sensor and devices are transmitted to cloud data centers. Several security issues like focused web servers in the cloud and information collection mishandling are faced by storage and cloud-based computing when offering us considerable convenience. For that reason, this article proposes a deep learning-based cloud security oriented intrusion discovery. Primarily, the input dataset is pre-processed by using normalization techniques followed by the features are selected using an Adaptive White Shark Optimization (AWSO) algorithm. The normal and intrusion data is classified by using Hough Transform based Deep Belief Network (HT-DBN) after that the sensitive data are secured with the help of an Improved Homomorphic Encryption (IHE) model. The simulation tool of MATLAB is been used to simulate the proposed implementation part and the experimental results outperformed the detection accuracy of 97% than other previous approaches.