Network Security Intrusion Research Articles

Campus Network Security Intrusion Detection Based on Feature Segmentation and Deep Learning

At present, the secure campus network strategy adopts technical means such as distinguishing applications and limiting them separately, but they have triggered other new problems, greatly affecting the unity of network resources and data. The relatively dispersed network architecture will inevitably limit the further development and expansion of the campus network. Therefore, when universities plan their networks, they must consider whether the network is safe, complete, smooth, and sustainable for smooth upgrading and development. In order to improve the effect of campus network security intrusion detection, this paper combines feature segmentation and deep learning technology to construct a campus network security intrusion detection model. To reduce the transmission time of query requirements in the grid, this paper improves the replica management mechanism and requires the information server to cache the Bloom Filter structure of nodes in its successor node list. Moreover, this paper uses the Compressed Bloom Filter algorithm to compress the Bloom Filter structure that needs to be transmitted, therefore reducing the network traffic generated during the update process of the Bloom Filter structure copy and avoiding network congestion. It also constructs a campus network security intrusion detection model based on feature segmentation and deep learning. Through experimental verification, the effectiveness of the system in intrusion detection, user evaluation, information processing, and other aspects is verified, and it has certain advantages compared to traditional algorithms. Through experimental research, it can be seen that the campus network security intrusion detection model based on feature segmentation and deep learning proposed in the paper can effectively improve the effect of campus network security monitoring. The method proposed in this article can not only be applied to campus network security, but also to the network security management of enterprises and other units, with certain scalability

Computer network big data detection based on internet of things technology

In order to solve the problems of traditional network security intrusion detection system in detection accuracy and missed detection rate, this paper proposes a computer network data detection based on Internet of things technology. This paper designs an intrusion detection system based on data clustering analysis algorithm. The hardware part of the intrusion detection system is composed of six parts: detection module, adaptive module, control management module, risk early warning module, control access module and data acquisition module. Each module is closely connected and updated with the rule base and database in real time; In the software flow design of the system, based on the data clustering analysis algorithm, the collected original data are classified and processed, and the Mings distance is determined, revealing the difference between normal data and abnormal data, and finally realizing the accurate detection of cloud computing network intrusion behavior. The experimental results show that the proposed intrusion detection system has a higher detection accuracy for malicious data, and the average missed detection rate can be controlled below 0.3 %. Conclusionthe detection system designed in this paper has more advantages in stability performance.

Network security intrusion target detection system in the cloud

Network security intrusion target detection system in the cloud

Network security intrusion target detection system in the cloud

Network security intrusion target detection system in the cloud

A comparative study of cyber security intrusion detection in healthcare systems

Due to the proliferation of network devices and the presence of sensitive information, healthcare systems have become prime targets for cyber attackers. Therefore, it is crucial to design an efficient and accurate intrusion detection system (IDS) specifically tailored for healthcare systems. In this regard, we conducted a comprehensive comparative study on network security intrusion detection in healthcare systems. In order to tackle the challenges arising from information redundancy and noise in feature selection, we developed the Maximum Information Coefficient (MIC) method to effectively analyse the nonlinear relationships among traffic features. This method was utilized in a comparative analysis involving ten models on three datasets. The experiments demonstrated that the detection models using MIC-based feature selection outperformed other feature selection approaches, especially when applied to the WUSTL-EHMS-2020 dataset, which includes patients' biometric features. The MIC-enhanced Extreme Gradient Boosting detection model achieved remarkable results, attaining an accuracy of 95.01%, precision of 94.94%, and recall of 95.01%. These findings underscore the efficacy of our comparative study in safeguarding healthcare systems against cyber attacks. Furthermore, our study highlights the importance of feature selection and the incorporation of patient biometric features in healthcare IDS. It is imperative for medical managers to consider these factors when making informed decisions regarding cyber security measures.

A Combined Multi-Classification Network Intrusion Detection System Based on Feature Selection and Neural Network Improvement

Feature loss in IoT scenarios is a common problem. This situation poses a greater challenge in terms of real-time and accuracy for the security of intelligent edge computing systems, which also includes network security intrusion detection systems (NIDS). Losing some packet information can easily confuse NIDS and cause an oversight of security systems. We propose a novel network intrusion detection framework based on an improved neural network. The new framework uses 23 subframes and a mixer for multi-classification work, which improves the parallelism of NIDS and is more adaptable to edge networks. We also incorporate the K-Nearest Neighbors (KNN) algorithm and Genetic Algorithm (GA) for feature selection, reducing parameters, communication, and memory overhead. We named the above system as Combinatorial Multi-Classification-NIDS (CM-NIDS). Experiments demonstrate that our framework can be more flexible in terms of the parameters of binary classification, has a fairly high accuracy in multi-classification, and is less affected by feature loss.

A Comprehensive Examination of Literature Exploring the Implementation of Machine Learning to Network Security's Intrusion Detection Systems

The Internet and telecommunication technologies have developed quickly, the amount of data transferred has greatly increased. Attackers are continually devising new tactics to steal or modify these data because they are so highly desired. The threat these attacks pose to the security of our systems is growing. It is among the most tough issues to resolve for detection of intrusions. An idss is a programme that attempts to analyse network traffic in order to detect intrusions. Despite the fact that many researchers have examined and developed novel IDS systems, IDS even now must be enhanced in order to achieve satisfactory detection capability while reducing number of false alarms. Furthermore, numerous intrusion detection systems have difficulty detecting nil attacks. Machine learning techniques had also recently become popular among scholars as a quick and accurate method of detecting network infiltration. This article offers a taxonomy of machine learning approaches as well as an explanation of IDS. In addition to a list of current IDS that include machine learning and a discussion of the essential components for IDS analysis, this article also outlines the advantages and disadvantages of each machine learning approach. The veracity of the findings from the evaluated study is then discussed after specifics of the various datasets used in the studies are given. The preceding part looks at the results, study obstacles, and projected future trends.

Network Security Intrusion Detection Methods Combining Optimization Algorithms and Neural Networks

Due to the diverse and volatile nature of today's network attacks, it is difficult to achieve satisfactory results using a single detection method. This project plans to study the network intrusion detection algorithm based on Semi-Supervised Learning and active learning to improve its accuracy and effectively intercept various network attacks. First, network attack data is collected, attack characteristics are extracted, and Semi-Supervised Learning method is used to cluster attack data. Secondly, by studying the clustering data, a network intrusion detection classifier based on BP neural network was constructed, and the construction process of the classifier was improved. On this basis, the effectiveness of the algorithm was verified through simulation experiments using standard datasets. The experimental results show that the combination rate of the optimized algorithm for network intrusion detection and neural network methods is over 95%, which can meet the actual accuracy requirements of network security protection. This is because the method proposed by the author combines semi supervised technology with BP neural network, which can effectively identify different types of network intrusion behavior, thereby reducing the missed detection rate and false alarm rate of network intrusion detection, and has significant advantages.

Design of Computer Network Security Intrusion Prevention Strategy and Evaluation Algorithm Analysis Technology

With the continuous improvement of global informatization, computer networks have basically reached complete popularization. In today's society, it has become a major application and provides corresponding services for various industries. Therefore, it is necessary to effectively protect various information in the network. Therefore, this article provided a detailed discussion on the problems existing in computer network information security. These issues included their own reasons, hacker intrusion, and spam. Based on this, this article explored computer network information security protection strategies such as deploying data encryption, timely patching system vulnerabilities, and installing firewalls and antivirus software. The experimental results showed that the network output values of the clustering algorithm were: 0.888 for level 1, 0.725 for level 2, 0.678 for level 3, 0.461 for level 4, and 0.211 for level 5. These were all within the scope.

Association Rule Mining Frequent-Pattern-Based Intrusion Detection in Network

In the network security system, intrusion detection plays a significant role. The network security system detects the malicious actions in the network and also conforms the availability, integrity and confidentiality of data information resources. Intrusion identification system can easily detect the false positive alerts. If large number of false positive alerts are created then it makes intrusion detection system as difficult to differentiate the false positive alerts from genuine attacks. Many research works have been done. The issues in the existing algorithms are more memory space and need more time to execute the transactions of records. This paper proposes a novel framework of network security Intrusion Detection System (IDS) using Modified Frequent Pattern (MFP-Tree) via K-means algorithm. The accuracy rate of Modified Frequent Pattern Tree (MFPT)-K means method in finding the various attacks are Normal 94.89%, for DoS based attack 98.34%, for User to Root (U2R) attacks got 96.73%, Remote to Local (R2L) got 95.89% and Probe attack got 92.67% and is optimal when it is compared with other existing algorithms of K-Means and APRIORI.

Computer Network Intrusion Anomaly Detection Based on Rough Fourier Fast Algorithm

Since there is a close relationship between network information security attack events and time complexity, it is necessary to count the degree of correlation between the current connection record and the connection record within a certain period of time before. Only in this way can the relationship between network connection data and network information security attack events be better reflected. In this paper, a rough Fourier fast algorithm based on rough set theory is proposed. Based on the characteristic attributes of the intrusion detection data set with the most value of character attributes as the data division basis, the computer network intrusion anomaly detection data set is intelligently divided into small data sets, so as to carry out attribute reduction. The network intrusion detection rule update experiment adopts the misuse detection method, extracts some samples from the KDD99 data set for training, obtains the computer network intrusion detection rules in the hierarchical decision table, and uses the incremental learning algorithm to update the rules, compared with the intrusion detection rules expressed by the decision table to test the feasibility and effectiveness of the rule update, and compared with the improved RSDB, RE-RFE algorithm, and KNN algorithm to evaluate the effect of the rough Fourier fast detection model applied to the problem of network intrusion anomaly detection. Using the attribute subset reduced by the rough Fourier algorithm to perform classification and modeling of computer network intrusion anomaly detection is significantly reduced, and the average time is reduced by 0.09 seconds, and this paper lays a good foundation for the application of network security intrusion detection algorithm.

Electricity Network Security Monitoring Based on Bee Colony Algorithm

In order to effectively detect and discover network threats in the initial stage, this study proposes an electricity network security intrusion detection method based on feature selection. A heuristic feature selection algorithm based on the bee colony algorithm is proposed to overcome the shortcomings of existing feature evaluation methods. The algorithm uses average mutual information to measure the importance of features and more truly reflects the relationship between the selected features, the selected features, and the classification labels. Aiming at the problem that the algorithm is easy to fall into local optimization, a heuristic random search algorithm is proposed, which iteratively optimizes to generate smaller feature subsets, and improves the speed and accuracy of intrusion detection. The experimental results show that compared with the traditional algorithm, the proposed method can effectively evaluate the risk of attack path on the selected experimental data set, and the gap between the generation strategy and the optimal strategy is reduced by 71.3%, which enhances the practicability of the attack graph analysis method in a large-scale network environment. Conclusion. This method has good scalability and can be applied to large-scale network environments. It can effectively obtain attack paths that are more in line with the real threat situation in an acceptable time, so as to effectively find the network threats.

Study on network security intrusion target detection method in big data environment

In view of the traditional network security intrusion target detection method cannot effectively estimate the trend of the intrusion target, resulting in poor detection accuracy, a new network secu...

Study on network security intrusion target detection method in big data environment

Study on network security intrusion target detection method in big data environment

Research on Attributes Reduction Method of Intrusion Detection Data Based on Rough Set Theory

With the huge amount of network connection data, high data dimensions and complex attack types, it is difficult for traditional data analysis techniques to obtain satisfactory results in the Network Security Intrusion Detection System. This paper proposed a RSDB method based on the Rough Set Theory and the Data Deblock Thought, which can help us to reduce the data conditional attributes and remix the data. The method considers two factors: the relationship between conditional attributes and decision attribute, and the data integrity. Through the experiment, this method can solve these difficult above problems effetely, while ensuring the original data is not distorted. Through many experiments and their experimental results, RSDB can not only make the classification accuracy over than 90%, but also its average detection time is in the millisecond level.

In-Memory Computing with Memristor Content Addressable Memories for Pattern Matching.

The dramatic rise of data-intensive workloads has revived application-specific computational hardware for continuing speed and power improvements, frequently achieved by limiting data movement and implementing "in-memory computation". However, conventional complementary metal oxide semiconductor (CMOS) circuit designs can still suffer low power efficiency, motivating designs leveraging nonvolatile resistive random access memory (ReRAM), and with many studies focusing on crossbar circuit architectures. Another circuit primitive-content addressable memory (CAM)-shows great promise for mapping a diverse range of computational models for in-memory computation, with recent ReRAM-CAM designs proposed but few experimentally demonstrated. Here, programming and control of memristors across an 86 × 12 memristor ternary CAM (TCAM) array integrated with CMOS are demonstrated, and parameter tradeoffs for optimizing speed and search margin are evaluated. In addition to smaller area, this memristor TCAM results in significantly lower power due to very low programmable conductance states, motivating CAM use in a wider range of computational applications than conventional TCAMs are confined to today. Finally, the first experimental demonstration of two computational models in memristor TCAM arrays is reported: regular expression matching in a finite state machine for network security intrusion detection and definable inexact pattern matching in a Levenshtein automata for genomic sequencing.

Network Intrusion Monitoring Technology Based on Decision Tree Method

In order to solve the problem of poor real-time network security intrusion detection, this paper proposes a network intrusion monitoring technology based on decision tree method. Firstly, the network intrusion monitoring module is set up in the existing network topology model, then the network information collection, information processing and information analysis modules are combined. Finally, the decision tree method is used to identify the abnormal data, effectively realize different types of abnormal data monitoring, and finally realize the network intrusion monitoring. Experimental results show that the proposed intrusion monitoring method can better monitor network intrusion behavior, and has higher monitoring accuracy than other methods.

Campus Network Security Program Based on Snort Network Security Intrusion Detection System

This paper studies the security problems of campus network and summarizes the current on the current security risks and threats that campus network faces, focusing on analysis of attack-defense strategies on DOS network layer, proposing the security program of campus network which uses firewall as well as network security intrusion detection system snort. This paper analyzes the functional advantages of the program and presents in details the setup deployment and collocation methods of network security intrusion detection system based on snort in the campus network, and its application results are also summarized.