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Journal articles on the topic 'Intrusion detection and analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 February 2022

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1

Li, You Guo. "Analysis of the Snort Building Code Based on IDS." Applied Mechanics and Materials 543-547 (March 2014): 2965–68. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.2965.

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Intrusion Detection system (IDS) is a important element of Defense in Depth,which is able to capture all data in the LAN and analyse them for finding intrusional behavior. This paper presents conception of intrusion detection system.Snort that is a network based intrusion detection tool is analyzed,from the aspect of system structure and collectivity flow.Finally,snort base intrusion detection system is constructed and validated by our experiment.The result proves that the intrusional behavior isdetected effectively by using the system.

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T, Krishnakaarthik. "Intrusion Detection and Vulnerability Analysis with Temporal Relationalship." International Journal of Psychosocial Rehabilitation 23, no. 4 (2019): 1205–16. http://dx.doi.org/10.37200/ijpr/v23i4/pr190447.

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Simavoryan, Simon Zhorzhevich, Arsen Rafikovich Simonyan, Georgii Aleksandrovich Popov, and Elena Ivanovna Ulitina. "The procedure of intrusions detection in information security systems based on the use of neural networks." Программные системы и вычислительные методы, no. 3 (March 2020): 1–9. http://dx.doi.org/10.7256/2454-0714.2020.3.33734.

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The subject of the research is the problem of identifying and countering intrusions (attacks) in information security systems (ISS) based on the system-conceptual approach, developed within the framework of the RFBR funded project No. 19-01-00383. The object of the research is neural networks and information security systems (ISS) of automated data processing systems (ADPS). The authors proceed from the basic conceptual requirements for intrusion detection systems - adaptability, learnability and manageability. The developed intrusion detection procedure considers both internal and external th

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Gu, Yue Sheng, Hong Yu Feng, and Jian Ping Wang. "Analysis of Intrusion Detection System." Key Engineering Materials 460-461 (January 2011): 451–54. http://dx.doi.org/10.4028/www.scientific.net/kem.460-461.451.

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Intrusion detection system is an important device of information security. This article describes intrusion detection technology concepts, classifications and universal intrusion detection model, and analysis of the intrusion detection systems weaknesses and limitations. Finally, some directions for future research are addressed.

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Chueh, Hao-En, Shun-Chuan Ho, Shih-Peng Chang, and Ping-Yu Hsu. "Online Intrusion Behaviors: Sequences and Time Intervals." Social Behavior and Personality: an international journal 38, no. 10 (2010): 1307–12. http://dx.doi.org/10.2224/sbp.2010.38.10.1307.

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In this study we model the sequences and time intervals of online intrusion behaviors. To maintain network security, intrusion detection systems monitor network environments; however, most existing intrusion detection systems produce too many intrusion alerts, causing network managers to investigate many potential intrusions individually to determine their validity. To solve this problem, we combined a clustering analysis of the time intervals of online users' behaviors with a sequential pattern analysis to identify genuine intrusion behaviors. Knowledge of the patterns generated by intruder b

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A. M., Riyad, M. S. Irfan Ahmed, and R. L. Raheemaa Khan. "An adaptive distributed Intrusion detection system architecture using multi agents." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 6 (2019): 4951. http://dx.doi.org/10.11591/ijece.v9i6.pp4951-4960.

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Intrusion detection systems are used for monitoring the network data, analyze them and find the intrusions if any. The major issues with these systems are the time taken for analysis, transfer of bulk data from one part of the network to another, high false positives and adaptability to the future threats. These issues are addressed here by devising a framework for intrusion detection. Here, various types of co-operating agents are distributed in the network for monitoring, analyzing, detecting and reporting. Analysis and detection agents are the mobile agents which are the primary detection m

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Song, Youngrok, Sangwon Hyun, and Yun-Gyung Cheong. "Analysis of Autoencoders for Network Intrusion Detection." Sensors 21, no. 13 (2021): 4294. http://dx.doi.org/10.3390/s21134294.

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As network attacks are constantly and dramatically evolving, demonstrating new patterns, intelligent Network Intrusion Detection Systems (NIDS), using deep-learning techniques, have been actively studied to tackle these problems. Recently, various autoencoders have been used for NIDS in order to accurately and promptly detect unknown types of attacks (i.e., zero-day attacks) and also alleviate the burden of the laborious labeling task. Although the autoencoders are effective in detecting unknown types of attacks, it takes tremendous time and effort to find the optimal model architecture and hy

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8

Surasit Songma, Witcha Chimphlee, Kiattisak Maichalernnukul, and Parinya Sanguansat. "Intrusion Detection through Rule Induction Analysis." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 5, no. 11 (2013): 187–94. http://dx.doi.org/10.4156/aiss.vol5.issue11.23.

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9

ZHONG, SHI, TAGHI M. KHOSHGOFTAAR, and NAEEM SELIYA. "CLUSTERING-BASED NETWORK INTRUSION DETECTION." International Journal of Reliability, Quality and Safety Engineering 14, no. 02 (2007): 169–87. http://dx.doi.org/10.1142/s0218539307002568.

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Recently data mining methods have gained importance in addressing network security issues, including network intrusion detection — a challenging task in network security. Intrusion detection systems aim to identify attacks with a high detection rate and a low false alarm rate. Classification-based data mining models for intrusion detection are often ineffective in dealing with dynamic changes in intrusion patterns and characteristics. Consequently, unsupervised learning methods have been given a closer look for network intrusion detection. We investigate multiple centroid-based unsupervised cl

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10

Lee, Se-Yul, and Yong-Soo Kim. "Design and Analysis of Probe Detection Systems for TCP Networks." Journal of Advanced Computational Intelligence and Intelligent Informatics 8, no. 4 (2004): 369–72. http://dx.doi.org/10.20965/jaciii.2004.p0369.

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Advanced computer network technology enables the connectivity of computers in an open network environment. Despite the growing numbers of security threats to networks, most intrusion detection identifies security attacks mainly by detecting misuse using a set of rules based on past hacking patterns. This pattern matching has a high rate of false positives and cannot detect new hacking patterns, making it vulnerable to previously unidentified attack patterns and variations in attack and increasing false negatives. Intrusion detection and prevention technologies are thus required. We propose a n

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Korani, Ravinder, and Dr P. Chandra Sekhar Reddy. "Anomaly based Intrusion Detection by Heuristics to Predict Intrusion Scope of IOT Network Transactions." International Journal of Engineering & Technology 7, no. 2.7 (2018): 797. http://dx.doi.org/10.14419/ijet.v7i2.7.10982.

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Conventional intrusion detection mechanisms face serious limitations in identifying heterogeneous and distributed type of intrusions over the IoT environment. This is due to inadequate resources and open deployment environment of IoT. Accordingly, ensuring data security and privacy are tough challenges in the practical context. This manuscript discusses various aspects of networking security and related challenges along with the concepts of system architecture. Further, endeavored to define a machine learning model that outlines two heuristics called Intrusion Scope Heuristic ( ), and benign s

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Huang, Luo Guang, Li Min Meng, and Yong Hong Guo. "The Application of Network Intrusion Detection Technology in Instrument." Applied Mechanics and Materials 325-326 (June 2013): 1683–87. http://dx.doi.org/10.4028/www.scientific.net/amm.325-326.1683.

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The development of intrusion detection systems in the world are reviewed in this article first. On the basis of in-depth analysis of the characteristics of network attacks and intrusions we aim at to solving the problems mentioned above, the characteristics of survival of the fittest genetic algorithm is used to solve the problem. Second, a detection model based on genetic algorithms is established, and finally the model is simulated. The simulation results show that the model can solve its intrusion detection system, security issues, with a theoretical and practical application.

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13

Assad, Noureddine, Brahim Elbhiri, Moulay Ahmed Faqihi, Mohamed Ouadou, and Driss Aboutajdine. "Analysis of the Deployment Quality for Intrusion Detection in Wireless Sensor Networks." Journal of Computer Networks and Communications 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/812613.

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The intrusion detection application in a homogeneous wireless sensor network is defined as a mechanism to detect unauthorized intrusions or anomalous moving attackers in a field of interest. The quality of deterministic sensor nodes deployment can be determined sufficiently by a rigorous analysis before the deployment. However, when random deployment is required, determining the deployment quality becomes challenging. An area may require that multiple nodes monitor each point from the sensing area; this constraint is known ask-coverage wherekis the number of nodes. The deployment quality of se

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Fang, Shi Lin, Yue Bin Wang, Quan Feng Yan, Yi Li, and Wen Bin Li. "The Analysis and Research of Intrusion Detection System Based on IPv6 Protocol Analysis." Advanced Materials Research 760-762 (September 2013): 617–22. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.617.

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In order to solve the security issue about network intrusion in IPv6 system, two modes of intrusion detection system based on IPv6 protocol analysis have been developed and designed. Based on these two modes, packet capture module, protocol analysis module, command parsing module and out-put processing module have been designed. The accuracy and efficiency of intrusion detection system based on IPv6 protocol analysis have been verified through the test focused on the key modules.

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15

Cui, Wen Qiang. "Simulation and Analysis of the Network Feed-Forward Intrusion Detection Model." Applied Mechanics and Materials 556-562 (May 2014): 2932–35. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.2932.

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Traditional single-packet inspection can only detect isolated intrusion. To detect intrusion of network feed-forward and multi-packet collaboration, a lot of prior knowledge or historical knowledge is required. There are many drawbacks of the traditional methods, such as low detection rates and high missing rate. The paper proposes a detection model considering classification intrusion identification, which builds the detection model by analyzing the intrusion feature. The experiment results show that the proposed method effectively solves the problems of intrusion of network feed-forward and

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16

Zhao, Dong Ming. "Research on the Performance Analysis of Network Intrusion Detection of AC Algorithm." Advanced Materials Research 886 (January 2014): 646–49. http://dx.doi.org/10.4028/www.scientific.net/amr.886.646.

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As the use of computers become more widely, its intrusion security has become a key issue. In order to solve this problem, this article proposes a analysis research on AC algorithm for the detection of network intrusion. Through the research on the patterns of network intrusion, the AC algorithm for the detection of network intrusion is proposed and analyzed. Simulation results show that the proposed AC algorithm of network intrusion detection has a good efficiency, higher accuracy and efficient use of resources.

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17

Atnafu, Surafel Mehari, and Prof (Dr ). Anuja Kumar Acharya. "Comparative Analysis of Intrusion Detection Attack Based on Machine Learning Classifiers." Indian Journal of Artificial Intelligence and Neural Networking 1, no. 2 (2021): 22–28. http://dx.doi.org/10.35940/ijainn.b1025.041221.

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In current day information transmitted from one place to another by using network communication technology. Due to such transmission of information, networking system required a high security environment. The main strategy to secure this environment is to correctly identify the packet and detect if the packet contains a malicious and any illegal activity happened in network environments. To accomplish this, we use intrusion detection system (IDS). Intrusion detection is a security technology that design detects and automatically alert or notify to a responsible person. However, creating an eff

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18

Bakeyalakshmi, P., and S. K. Mahendran. "Enhanced replica detection scheme for efficient analysis of intrusion detection in MANET." International Journal of Engineering & Technology 7, no. 1.1 (2017): 565. http://dx.doi.org/10.14419/ijet.v7i1.1.10169.

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Nowadays, detection scheme of intrusion is placing a major role for efficient access and analysis in Mobile Ad-hoc network (MANET). In the past, the detection scheme of Intrusion was used to identify the efficiency of the network and in maximum systems it performs with huge rate of false alarm. In this paper, an Effective approach of the Enhanced Replica Detection scheme (ERDS) based on Sequential Probability Ratio Test (SPRT) is proposed to detect the malicious actions and to have a secure path without claim in an efficient manner. Also, provides strategies to avoid attacker and to provide se

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19

Wange, Sonal, Shiv K. Sahu, and Amit Mishra. "A critical analysis on intrusion detection techniques." International Journal of Advanced Technology and Engineering Exploration 3, no. 19 (2016): 77–81. http://dx.doi.org/10.19101/ijatee.2016.319001.

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20

Tsuge, Yusuke. "Intrusion Detection System with Spectrum Quantification Analysis." International Journal of Cyber-Security and Digital Forensics 5, no. 4 (2016): 197–207. http://dx.doi.org/10.17781/p002219.

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21

Hanmante, Sumedh P., Uma Deshattiwar, and Renuka Pawar. "Multivariate correlation analysis for network intrusion detection." International Journal of Scientific & Engineering Research 7, no. 4 (2016): 1150–53. http://dx.doi.org/10.14299/ijser.2016.04.008.

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22

Lunt, Teresa. "Automated audit trail analysis for intrusion detection." Computers & Security 11, no. 5 (1992): 492. http://dx.doi.org/10.1016/0167-4048(92)90256-q.

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23

Lunt, Teresa. "Automated audit trail analysis for intrusion detection." Computer Audit Update 1992, no. 4 (1992): 2–8. http://dx.doi.org/10.1016/0960-2593(92)90034-k.

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24

Xing, Bowen, Yafeng Jiang, Yuqing Liu, and Shouqi Cao. "Risk Data Analysis Based Anomaly Detection of Ship Information System." Energies 11, no. 12 (2018): 3403. http://dx.doi.org/10.3390/en11123403.

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Due to the vulnerability and high risk of the ship environment, the Ship Information System (SIS) should provide 24 hours of uninterrupted protection against network attacks. Therefore, the corresponding intrusion detection mechanism is proposed for this situation. Based on the collaborative control structure of SIS, this paper proposes an anomaly detection pattern based on risk data analysis. An intrusion detection method based on the critical state is proposed, and the corresponding analysis algorithm is given. In the Industrial State Modeling Language (ISML), risk data are determined by all

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25

Wang, Zong Jiang, and Xiao Bo Li. "An Improved Intrusion Detection Model Design." Advanced Materials Research 710 (June 2013): 682–86. http://dx.doi.org/10.4028/www.scientific.net/amr.710.682.

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In order to improve the design of intrusion detection model, this paper according to the analysis of generic intrusion detection model and intrusion detection model based on data mining, design for intrusion detection intrusion detection systems based on improved fuzzy C-means algorithm, In the model, the design of each module, Detailed description of the various parts and the parts functions of the model, and finally the feasibility of the model were analyzed. This method is effective to solve the problem of false detection rate in intrusion detection system, so that the performance of intrus

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26

Yaokumah, Winfred, and Isaac Wiafe. "Analysis of Machine Learning Techniques for Anomaly-Based Intrusion Detection." International Journal of Distributed Artificial Intelligence 12, no. 1 (2020): 20–38. http://dx.doi.org/10.4018/ijdai.2020010102.

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Determining the machine learning (ML) technique that performs best on new datasets is an important factor in the design of effective anomaly-based intrusion detection systems. This study therefore evaluated four machine learning algorithms (naive Bayes, k-nearest neighbors, decision tree, and random forest) on UNSW-NB 15 dataset for intrusion detection. The experiment results showed that random forest and decision tree classifiers are effective for detecting intrusion. Random forest had the highest weighted average accuracy of 89.66% and a mean absolute error (MAE) value of 0.0252 whereas deci

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N., Zafer Ahmed. "Analysis and Determination of Recent Developments on Intrusion Detection Schemes in Cloud Environment." Journal of Advanced Research in Dynamical and Control Systems 24, no. 4 (2020): 174–78. http://dx.doi.org/10.5373/jardcs/v12i4/20201430.

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Athavale, Nachiket, Shubham Deshpande, Vikash Chaudhary, Jatin Chavan, and S. S. Barde. "Framework for Threat Analysis and Attack Modelling of Network Security Protocols." International Journal of Synthetic Emotions 8, no. 2 (2017): 62–75. http://dx.doi.org/10.4018/ijse.2017070105.

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Nowadays everything is computerized including banking and personal records. Also, to boost business profits, businessmen have changed their way of operations from physical way to electronic way, for example Flipkart. But as these developments benefit the developer they also increase the chance of exposing all of customer's personal details to malicious users. Hackers can enter into the system and can steal crucial or sensitive information about other authentic users and in case of banks leads to frauds. Security thus, becomes an important issue for all companies and banks. Intrusion detection

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29

Ramaki, Ali Ahmadian, Abbas Rasoolzadegan, and Abbas Ghaemi Bafghi. "A Systematic Mapping Study on Intrusion Alert Analysis in Intrusion Detection Systems." ACM Computing Surveys 51, no. 3 (2018): 1–41. http://dx.doi.org/10.1145/3184898.

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Hajamydeen, Asif Iqbal, and Nur Izura Udzir. "A Detailed Description on Unsupervised Heterogeneous Anomaly Based Intrusion Detection Framework." Scalable Computing: Practice and Experience 20, no. 1 (2019): 113–60. http://dx.doi.org/10.12694/scpe.v20i1.1465.

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Observing network traffic flow for anomalies is a common method in Intrusion Detection. More effort has been taken in utilizing the data mining and machine learning algorithms to construct anomaly based intrusion detection systems, but the dependency on the learned models that were built based on earlier network behaviour still exists, which restricts those methods in detecting new or unknown intrusions. Consequently, this investigation proposes a structure to identify an extensive variety of abnormalities by analysing heterogeneous logs, without utilizing either a prepared model of system tra

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31

Li, Ke Wei. "The Simulation and Analysis of the Large-Scale Intrusion Detection Model in Shuffle Networks." Applied Mechanics and Materials 556-562 (May 2014): 2878–81. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.2878.

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There are some issues for the shuffle network intrusion detection, such as high loss detection rates and time-consuming procedures. This paper proposes a shuffle network intrusion detection method fusing the misuse behavior analysis and analyzes the network misuse behavior procedures. According to the damaged data flow balance features by network misuse behavior, the paper applies the hypothesis test in probability theory to evaluate whether the confidence interval excesses 0. If the confidence interval does not contain zero, it indicates the presence of feed-forward network intrusion; otherwi

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32

Zhao, Yi Lin, and Qing Lei Zhou. "Intrusion Detection Method Based on LEGClust Algorithm." Applied Mechanics and Materials 263-266 (December 2012): 3025–33. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.3025.

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Clustering analysis is a typical unsupervised learning technology in data mining, which can improve the efficiency of intrusion detection system. LEGClust cluster algorithm is a new clustering analysis technique and it can effectively find the arbitrary shape clusters hidden in the data. We apply this algorithm to the intrusion detection field and present an intrusion detection method. We introduce the real dissimilarity among data into the determination of data connection relationship. Experiment results on KDD CUP1999 Dataset show that LEGClust algorithm is an effective technique for intrusi

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33

Zhang GuoYin, and Li Heng. "Research on Cluster Analysis for Intrusion Detection System." International Journal of Digital Content Technology and its Applications 6, no. 8 (2012): 98–106. http://dx.doi.org/10.4156/jdcta.vol6.issue8.12.

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Abdullah, Azween Bin, and Long Zheng Cai. "Improving Intrusion Detection using Genetic Linear Discriminant Analysis." International Journal of Intelligent Systems and Applications in Engineering 3, no. 1 (2015): 34. http://dx.doi.org/10.18201/ijisae.37262.

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Gunale, Miss Sayali, Miss Renuka Tanksale, and Prof Mr U. K. Raut. "Intrusion Detection using Machine Learning and log analysis." IJARCCE 8, no. 4 (2019): 306–9. http://dx.doi.org/10.17148/ijarcce.2019.8450.

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Akbar, Shaik, Dr K. Nageswara Rao, and Dr J. A. Chandulal. "Intrusion Detection System Methodologies Based on Data Analysis." International Journal of Computer Applications 5, no. 2 (2010): 10–20. http://dx.doi.org/10.5120/892-1266.

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S, Ponmaniraj. "Web Intrusion Detection System through Crawler’s Event Analysis." International Journal of Advanced Trends in Computer Science and Engineering 9, no. 3 (2020): 2503–7. http://dx.doi.org/10.30534/ijatcse/2020/03932020.

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Nisha, T. N., and Dhanya Pramod. "Sequential pattern analysis for event-based intrusion detection." International Journal of Information and Computer Security 11, no. 4/5 (2019): 476. http://dx.doi.org/10.1504/ijics.2019.10023475.

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Lee, Yo-Seob. "Design and Analysis of Multiple Intrusion Detection Model." Journal of the Korea institute of electronic communication sciences 11, no. 6 (2016): 619–26. http://dx.doi.org/10.13067/jkiecs.2016.11.6.619.

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Nisha, T. N., and Dhanya Pramod. "Sequential pattern analysis for event-based intrusion detection." International Journal of Information and Computer Security 11, no. 4/5 (2019): 476. http://dx.doi.org/10.1504/ijics.2019.101936.

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S., Apurva, and Deepak R. "Post-Attack Intrusion Detection using Log Files Analysis." International Journal of Computer Applications 127, no. 18 (2015): 19–21. http://dx.doi.org/10.5120/ijca2015906731.

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Dhall, A., J. K. Chhabra, and N. S. Aulakh. "INTRUSION DETECTION SYSTEM BASED ON SPECKLE PATTERN ANALYSIS." Experimental Techniques 29, no. 1 (2005): 25–31. http://dx.doi.org/10.1111/j.1747-1567.2005.tb00200.x.

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Pin Ren, Yan Gao, Zhichun Li, Yan Chen, and B. Watson. "IDGraphs: intrusion detection and analysis using stream compositing." IEEE Computer Graphics and Applications 26, no. 2 (2006): 28–39. http://dx.doi.org/10.1109/mcg.2006.36.

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Dong, Yuhong, Sam Hsu, Saeed Rajput, and Bing Wu. "Experimental analysis of application-level intrusion detection algorithms." International Journal of Security and Networks 5, no. 2/3 (2010): 198. http://dx.doi.org/10.1504/ijsn.2010.032218.

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Ponnusamy, Vasaki, Aun Yichiet, NZ Jhanjhi, Mamoona humayun, and Maram Fahhad Almufareh. "IoT Wireless Intrusion Detection and Network Traffic Analysis." Computer Systems Science and Engineering 40, no. 3 (2022): 865–79. http://dx.doi.org/10.32604/csse.2022.018801.

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Venkatachalam, K., P. Prabu, B. Saravana Balaji, Byeong-Gwon Kang, Yunyoung Nam, and Mohamed Abouhawwash. "Cross-Layer Hidden Markov Analysis for Intrusion Detection." Computers, Materials & Continua 70, no. 2 (2022): 3685–700. http://dx.doi.org/10.32604/cmc.2022.019502.

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47

Jiao, Ya Bing. "Analysis and Research of Intrusion Detection System Based on Association Rule." Advanced Materials Research 709 (June 2013): 628–31. http://dx.doi.org/10.4028/www.scientific.net/amr.709.628.

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A model of intrusion detection system based on the technology data mining is presented on the basis of introduction on the concept and the technical method of the intrusion detection system. In this model, the two methods of the technology data mining association rule and the classified analysis cooperate with each other and the detection efficiency will be greatly enhanced.

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Laqtib, Safaa, Khalid El Yassini, and Moulay Lahcen Hasnaoui. "A technical review and comparative analysis of machine learning techniques for intrusion detection systems in MANET." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 3 (2020): 2701. http://dx.doi.org/10.11591/ijece.v10i3.pp2701-2709.

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Machine learning techniques are being widely used to develop an intrusion detection system (IDS) for detecting and classifying cyber attacks at the network-level and the host-level in a timely and automatic manner. However, Traditional Intrusion Detection Systems (IDS), based on traditional machine learning methods, lacks reliability and accuracy. Instead of the traditional machine learning used in previous researches, we think deep learning has the potential to perform better in extracting features of massive data considering the massive cyber traffic in real life. Generally Mobile Ad Hoc Net

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Li, Zhi Gang, Ling Yun Chen, Ming Ji Wang, and Yu Shuang Li. "Simulation Study on Multi-Frequency Signal Detection in Perimeter Intrusion Detection System by Matlab." Applied Mechanics and Materials 530-531 (February 2014): 293–96. http://dx.doi.org/10.4028/www.scientific.net/amm.530-531.293.

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Multi-domain perimeter intrusion detection system by one machine is based on leaky cable. To solving the problem that the multi-frequency electromagnetic disturbance signal detection circuit is complex and repeated, a method is raised, which is without processing by different frequency, sending the intrusion disturbance signal from multisport to processing circuit to perform FFT, contrasting it with signal spectra without disturbance by frequency analysis, and determining if intrusion happened, the number of intrusion case, detailed place and domain. Simulation study is performing by Matlab, w

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Radivilova, Tamara, Lyudmyla Kirichenko, Maksym Tawalbeh, Petro Zinchenko, and Vitalii Bulakh. "THE LOAD BALANCING OF SELF-SIMILAR TRAFFIC IN NETWORK INTRUSION DETECTION SYSTEMS." Cybersecurity: Education, Science, Technique 3, no. 7 (2020): 17–30. http://dx.doi.org/10.28925/2663-4023.2020.7.1730.

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The problem of load balancing in intrusion detection systems is considered in this paper. The analysis of existing problems of load balancing and modern methods of their solution are carried out. Types of intrusion detection systems and their description are given. A description of the intrusion detection system, its location, and the functioning of its elements in the computer system are provided. Comparative analysis of load balancing methods based on packet inspection and service time calculation is performed. An analysis of the causes of load imbalance in the intrusion detection system ele

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