Research on vehicle network intrusion detection technology based on dynamic data set

Network Security Intrusion Detection Methods Combining Optimization Algorithms and Neural Networks

Internet of things intrusion detection model and algorithm based on cloud computing and multi-feature extraction extreme learning machine

With the popularization and development of network knowledge, network intruders are increasing, and the attack mode has been updated. Intrusion detection technology is a kind of active defense technology, which can extract the key information from the network system, and quickly judge and protect the internal or external network intrusion. Intrusion detection is a kind of active security technology, which provides real-time protection for internal attacks, external attacks and misuse, and it plays an important role in ensuring network security. However, with the diversification of intrusion technology, the traditional intrusion detection system cannot meet the requirements of the current network security. Therefore, the implementation of intrusion detection needs diversifying. In this context, we apply neural network technology to the network intrusion detection system to solve the problem. In this paper, on the basis of intrusion detection method, we analyze the development history and the present situation of intrusion detection technology, and summarize the intrusion detection system overview and architecture. The neural network intrusion detection is divided into data acquisition, data analysis, pretreatment, intrusion behavior detection and testing.

With the growing diverse demands for Internet applications, network security issues become more acute. To address the appropriate network security from network intrusion detection event has become an important research in network security. In this paper, user behavior features are extracted to create the model for the user transmission behavior. The demands for anomaly detection and the specific characteristics of audit data are studied. An intrusion detection algorithm and based on statistics variance method in user transmission behavior (IDSV) and the implementation framework are provided. And then, the IDSV algorithm is applied into ARP spoofing detection. The simulation results show that IDSV algorithm does well in detection rate of intrusion detection and has good detection performance of different application features. The IDSV algorithm can detect intrusion effectively under user behavior in different applications.

As the development of intelligent ships rapidly progresses, the importance of maritime network security escalates. Intrusion Detection Systems (IDS) serve as a pivotal defense mechanism against cyberattacks targeting these vessels. To augment the efficacy of IDS in detecting anomalous network traffic, this paper introduces a novel intrusion detection method utilizing both Transformer and Kolmogorov-Arnold Networks (KAN), designated as Transformer-KAN. This approach effectively mitigates the limitations of traditional intrusion detection algorithms, which include inadequate feature extraction capabilities and the excessive parameterization required to delineate complex patterns. The proposed method employs a Transformer encoder to discern long-range dependencies within input sequences, while KAN layers enhance the model’s ability to approximate complex patterns through non-linear transformations. This integrative strategy ensures elevated accuracy for the intrusion detection algorithm and proves particularly beneficial in intelligent ship environments, where training data may be sparse or computational resources limited. Experimental results confirm that the Transformer-KAN model attains exceptionally high accuracy in both binary and five-class classification tasks, outperforming the capabilities of standalone Transformer and KAN models. Comparative analyses with conventional algorithms—such as Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), Deep Belief Networks (DBN), and Support Vector Machines (SVM)-further substantiate the superior effectiveness of the proposed method. This research establishes a new theoretical framework and provides a direction for the practical implementation of network security measures in contemporary intelligent ships.

Intrusion detection has been widely used in many application domains; thus, it has caught significant attention in academic fields these years. Assembled with more and more sub-systems, the network is more vulnerable to multiple attacks aiming at the network security. Compared with the other issues such as complex environment and resources-constrained devices, network security has been the biggest challenge for Internet construction. To deal with this problem, a fundamental measure for safeguarding network security is to select an intrusion detection algorithm. As is known, it is less effective to determine the abnormal behavior as an intrusion and learn the entire scope of the normal behavior with the traditional anomaly-based algorithm for Internet intrusion detection. In this paper, we propose an intrusion-detecting algorithm of shadowed rough-fuzzy clustering based on Mahalanobis distance, named MSRFCM. It adopts dissimilarity measurement of Mahalanobis distance to identify the relevant variables that significantly influence the clustering performance and reduce the error rate in the process of partitioning clusters with high attribute correlation. And shadowed rough-fuzzy clustering (SRFCM) is applied to obtaining real value-approaching prototypes based on iteration and partitioning the data set into more meaningful clusters. Through simulation with the NSL-KDD intrusion data set and three other intrusion data sets, the Mahalanobis distance-based shadowed rough-fuzzy clustering algorithm has improved performance in intrusion detection.

Internet of Medical Things (IoMT) is network of interconnected medical devices (smart watches, pace makers, prosthetics, glucometer, etc.), software applications, and health systems and services. IoMT has successfully addressed many old healthcare problems. But it comes with its drawbacks essentially with patient’s information privacy and security related issues that comes from IoMT architecture. Using obsolete systems can bring security vulnerabilities and draw attacker’s attention emphasizing the need for effective solution to secure and protect the data traffic in IoMT network. Recently, intrusion detection system (IDS) is regarded as an essential security solution for protecting IoMT network. In the past decades, machines learning (ML) algorithms have demonstrated breakthrough results in the field of intrusion detection. Notwithstanding, to our knowledge, there is no work that investigates the power of machines learning algorithms for intrusion detection in IoMT network. This paper aims to fill this gap of knowledge investigating the application of different ML algorithms for intrusion detection in IoMT network. The investigation analysis includes ML algorithms such as K-nearest neighbor, Naïve Bayes, support vector machine, artificial neural network and decision tree. The benchmark dataset, Bot-IoT which is publicly available with comprehensive set of attacks was used to train and test the effectiveness of all ML models considered for investigation. Also, we used comprehensive set of evaluation metrics to compare the power of ML algorithms with regard to their detection accuracy for intrusion in IoMT networks. The outcome of the analysis provides a promising path to identify the best the machine learning approach can be used for building effective IDS that can safeguard IoMT network against malicious activities.

Review on intrusion detection using feature selection with machine learning techniques

With the rapid development of Internet technology, network attacks have become more frequent and complex, and intrusion detection has also played an increasingly important role in network security. Intrusion detection is real‐time and proactive, and it is an indispensable technology under the diversified trend of network security issues. In terms of network security, neural networks have the characteristics of self‐learning, self‐adaptation, and parallel computing, which are very important in intrusion detection. This paper combines back propagation neural network (BPNN) and elite clone artificial bee colony (ECABC) to propose a new ECABC‐BPNN, which updates and optimizes the settings of traditional BPNN weights and thresholds. Then, apply ECABC‐BPNN to network intrusion detection. Use the attack data samples of KDD CUP 99 and water pipe for attack classification experiments using GA‐BPNN, PSO‐BPNN, and ECABC‐BPNN. The results show that the ECABC‐BPNN proposed in this paper has an accuracy rate of 98.08% on KDD 99 and 99.76% on water pipe data. ECABC‐BPNN effectively improves the accuracy of network intrusion classification and reduces classification errors. In addition, the time complexity of using ECABC‐BPNN to classify network attacks is relatively low. Therefore, ECABC‐BPNN has superior performance in network intrusion detection and classification.

Network intrusion detection is of great significance for network security in Local Area Network (LAN). Traditional methods such as firewalls do not completely protect against attacks on the LAN due to lack of continuous learning. Recently, the ability of convolutional neural networks (CNN) to extract features in the field of computer vision has received extensive attention. CNN can automatically extract effective complex features to adapt to constantly changing environments, which is especially important in network intrusion detection. In this paper, we focus on network security in the LAN. We propose an approach based on CNN to implement intrusion detection in LAN. This approach can effectively identify network attacks and has an accuracy of 98.34% on the KDD99 dataset. The experimental results show that the proposed approach based on the CNN has high accuracy in intrusion detection.

Design and performance evaluation of a multi-agent-based dynamic lifetime security scheme for AODV routing protocol

With the rapid development of Internet technology, computer has become an indispensable part of modern society. However, with the popularization of computer technology, the problem of network security is becoming increasingly prominent. In recent years, China's network security incidents occur frequently. As a means of attack, computer deception has seriously threatened national information security, social stability and people's life. Therefore, it is of great practical significance to study the role of computer deception in network security. Its purpose is to mislead and deceive users or systems by various means to achieve the purpose of illegally obtaining information, damaging the stability of the system or carrying out other illegal activities. Based on this, this paper aims to explore the role of computer deception in network security. Firstly, the concept, content and present situation of computer network security are expounded, and then the necessity of computer network security management and protection is analyzed. Secondly, the related technology of computer deception is introduced. Finally, the application of computer spoofing in network security is studied, and the effectiveness of computer spoofing in preventing and detecting network attacks is revealed. In addition, the ethical issues of computer deception and its potential in future cybersecurity strategies are discussed. Research shows that computer deception can not only enhance the defense mechanism, but also improve the organization's cognition and response ability to network threats, and become an indispensable security strategy. By evaluating current technologies and methods, this paper provides practical recommendations for researchers and practitioners to effectively use computer spoofing techniques to improve network security.

The nature of wireless sensor networks (WSNs) makes them very vulnerable to adversary's malicious attacks. Therefore, network security is an important issue to WSNs. Due to the constraints of WSN, intrusion detection in WSNs is a challengeable task. In this paper, we present a novel intrusion detection mechanism for WSNs, which is composed of a secure data communication algorithm and an intrusion detection algorithm. The major contribution of this paper is that we propose an original secure mechanism to defend WSNs against malicious attacks by using the information generated during data communication. The approach is able to protect the data communication in a WSN even if some sensor nodes are compromised by adversary. The proposed approach is easy to be implemented and performed in resource-constrained WSN. We also evaluate the proposed approach by a simulation experiment and analyze the simulation results in detail.

Intrusion detection is a major research problem in network security. Intrusion Detection Systems (IDS), analyses information from the network trying to identify suspicious behaviors and detect intentions to attack the system. Intrusion attempts are nonlinear with an unpredictable behavior on the network traffic. The process of selecting the key features that allows discriminate attacks from normal traffic, is a crucial task in information security to obtain an effective IDS. We propose in this paper to use an Evolutionary Algorithm and an evaluation function from a classifier, to automatically select key features from a data set before defining a Decision Tree that can be used to discriminate among the network data type. The purpose of this study, is to propose an intrusion detection technique that selects key features using a specially designed evolutionary algorithm with individual evaluations done using C4.5, a wellknown classifier that discriminate data using decision trees. We report very encouraging results of our approach using NSL-KDD intrusion detection benchmark data sets.

The modern world technology is civilized, globalized and modernized. The technological development of social networks and e-commerce applications produce larger data. This data communication is major task, because device to device communication need network terminal. This data transmission is not safe because of different types of tools and software available to destroy the existing network. In the field of network security during data transfer from one particular node to other node some security vulnerability is happened this is the one of the critical issue in this sector. The reason for this network security is different types of data attacks are happen in day to day life. It is easy to establish a new network but protecting the entire network is a big issue. This network security is generally two parameter first one is communication and second one is data automation. The network security field is directly or indirectly linked with the concept of data encryption. The development in this network security has taken us to a level that from signature again we came back to thumb print. For example maintain the data secure we use the lock system which is a finger print type. This technology helps us to protect the physical data theft, but logical data theft is still problem for data transmission. This article will brief about the network security it also presents the various network security types. Those types are wired and wireless network security. Apart from the network security the following topics is also discussed in this article. Those are network security protocols and simulation tools in network security. The research problems in network security are privacy and vulnerability of data.