Journal articles: 'Analysis and filtering of network traffic'

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Rajaboevich, Gulomov Sherzod. "Comparative Analysis of Methods Content Filtering Network Traffic." International Journal of Emerging Trends in Engineering Research 8, no. 5 (May 25, 2020): 1561–69. http://dx.doi.org/10.30534/ijeter/2020/15852020.

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Kabala, Piotr, and Dariusz Laskowski. "Analysis of Network Traffic Filtering / Analiza Filtracji Ruchu Sieciowego." Journal of KONBiN 33, no. 1 (September 1, 2015): 41–60. http://dx.doi.org/10.1515/jok-2015-0004.

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Abstract The article consist of study on concept of Network Obstacle (firewall, FW) using PLD-Linux platform and available tools, implementation selected mechanism of security, also analysis the effectiveness of prepared Network Obstacle. Study on concept of Network Obstacle is based on Screened Subnet architecture. Testing effectiveness of network Obstacle was found in two fundamental parts. In the first part was examining the security of network obstacle, testing the resistance attempts of scanning port and the most common attacks. The second part of the research meant to show the influence of the Network Obstacle on time and delay of the realization of transaction for HTTP movement depending on level of the security and movement intensity generated by internal network users. Testing the effectiveness of FW was verify by testing the resistance in an attempt of scanning ports and attacks refusal of service (ICMP, SYN Flood), providing base of dispersed attacks DDoS. This part of the research had one aim, to show the influence FW on time and on delay of the realization the transaction for motion HTTP, depending on the security level and intensity of movement generated by the users of the internal network.

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Lu, Yao, Hanhong Jiang, Tao Liao, Chengcheng Xu, and Chen Deng. "Characteristic Analysis and Modeling of Network Traffic for the Electromagnetic Launch System." Mathematical Problems in Engineering 2019 (June 23, 2019): 1–7. http://dx.doi.org/10.1155/2019/2929457.

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The electromagnetic launch system (EMLS) is a kind of large-scale complex system with strong mechanical, electrical, and hydraulic coupling. The effective operation of the system requires the support of advanced and reliable health management system, and the function of the health management system relies on the network traffic to complete data transmission and interaction. Through protocol filtering and time-frequency characteristic analysis of network traffic, it is found that the system mainly includes CIP and TCP; traffic of CIP shows continuity and stability, while traffic of TCP shows sparsity and impact. Based on the analysis of traffic characteristics, an SVR-ARMA network traffic model is established, which improves the prediction accuracy and performance of network traffic and provides a reliable technical guarantee for the fault diagnosis and health management of the EMLS.

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Novakov, Stevan, Chung-Horng Lung, Ioannis Lambadaris, and Nabil Seddigh. "A Hybrid Technique Using PCA and Wavelets in Network Traffic Anomaly Detection." International Journal of Mobile Computing and Multimedia Communications 6, no. 1 (January 2014): 17–53. http://dx.doi.org/10.4018/ijmcmc.2014010102.

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Research into network anomaly detection has become crucial as a result of a significant increase in the number of computer attacks. Many approaches in network anomaly detection have been reported in the literature, but data or solutions typically are not freely available. Recently, a labeled network traffic flow dataset, Kyoto2006+, has been created and is publicly available. Most existing approaches using Kyoto2006+ for network anomaly detection apply various clustering techniques. This paper leverages existing well known statistical analysis and spectral analysis techniques for network anomaly detection. The first popular approach is a statistical analysis technique called Principal Component Analysis (PCA). PCA describes data in a new dimension to unlock otherwise hidden characteristics. The other well known spectral analysis technique is Haar Wavelet filtering analysis. It measures the amount and magnitude of abrupt changes in data. Both approaches have strengths and limitations. In response, this paper proposes a Hybrid PCA–Haar Wavelet Analysis. The hybrid approach first applies PCA to describe the data and then Haar Wavelet filtering for analysis. Based on prototyping and measurement, an investigation of the Hybrid PCA–Haar Wavelet Analysis technique is performed using the Kyoto2006+ dataset. The authors consider a number of parameters and present experimental results to demonstrate the effectiveness of the hybrid approach as compared to the two algorithms individually.

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Lee, Jae-Kook, Taeyoung Hong, and Guohua Li. "Traffic and overhead analysis of applied pre-filtering ACL firewall on HPC service network." Journal of Communications and Networks 23, no. 3 (June 2021): 192–200. http://dx.doi.org/10.23919/jcn.2021.000011.

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Privalov, Andrey, Vera Lukicheva, Igor Kotenko, and Igor Saenko. "Method of Early Detection of Cyber-Attacks on Telecommunication Networks Based on Traffic Analysis by Extreme Filtering." Energies 12, no. 24 (December 13, 2019): 4768. http://dx.doi.org/10.3390/en12244768.

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The paper suggests a method of early detection of cyber-attacks by using DDoS attacks as an example) using the method of extreme filtering in a mode close real time. The process of decomposition of the total signal (additive superposition of attacking and legitimate effects) and its decomposition using the method of extreme filtering is simulated. A profile model of a stochastic network is proposed. This allows to specify the influence of the intruder on the network using probabilistic-time characteristics. Experimental evaluation of metrics characterizing the cyber-attack is given. It is demonstrated how obtained values of metrics confirm the process of attack preparation, for instance the large-scaled telecommunication network, which includes the proposed method for early detection of attacks, has a recovery time of no more than 9 s, and the parameters of quality of service remain in an acceptable range.

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Fernández, Diego, Francisco J. Nóvoa, Fidel Cacheda, and Víctor Carneiro. "Advancing Network Flow Information Using Collaborative Filtering." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 25, Suppl. 2 (December 2017): 97–112. http://dx.doi.org/10.1142/s021848851740013x.

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Collaborative Filtering algorithms are frequently employed in e-commerce. However, this kind of algorithms can also be useful in other domains. In an information system thousands of bytes are sent through the network every second. Analyzing this data can require too much time and many resources, but it is necessary for ensuring the right operation of the network. Results are used for profiling, security analysis, traffic engineering and many other purposes. Nowadays, as a complement to a deep inspection of the data, it is more and more common to monitor packet flows, since it consumes less resources and it allows to react faster to any network situation. In a typical ow monitoring system, flows are exported to a collector, which stores the information before being analyzed. However, many collectors work based on time slots, so they do not analyze the flows when they are just received, generating a delay. In this work we demonstrate how Collaborative Filtering algorithms can be applied to this new domain. In particular, using information about past flows, these algorithms can anticipate future flows before being captured. This way, time required for detecting and responding to different network situations is reduced.

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Jain, Sakshi, Mobin Javed, and Vern Paxson. "Towards Mining Latent Client Identifiers from Network Traffic." Proceedings on Privacy Enhancing Technologies 2016, no. 2 (April 1, 2016): 100–114. http://dx.doi.org/10.1515/popets-2016-0007.

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Abstract Websites extensively track users via identifiers that uniquely map to client machines or user accounts. Although such tracking has desirable properties like enabling personalization and website analytics, it also raises serious concerns about online user privacy, and can potentially enable illicit surveillance by adversaries who broadly monitor network traffic. In this work we seek to understand the possibilities of latent identifiers appearing in user traffic in forms beyond those already well-known and studied, such as browser and Flash cookies. We develop a methodology for processing large network traces to semi-automatically discover identifiers sent by clients that distinguish users/devices/browsers, such as usernames, cookies, custom user agents, and IMEI numbers. We address the challenges of scaling such discovery up to enterprise-sized data by devising multistage filtering and streaming algorithms. The resulting methodology reflects trade-offs between reducing the ultimate analysis burden and the risk of missing potential identifier strings. We analyze 15 days of data from a site with several hundred users and capture dozens of latent identifiers, primarily in HTTP request components, but also in non-HTTP protocols.

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shabtai, Asaf, Dennis Potashnik, Yuval Fledel, Robert Moskovitch, and Yuval Elovici. "Monitoring, analysis, and filtering system for purifying network traffic of known and unknown malicious content." Security and Communication Networks 4, no. 8 (July 26, 2010): 947–65. http://dx.doi.org/10.1002/sec.229.

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Tian, Zhao, Wei She, Shuang Li, You-Wei Wang, Wei Liu, Guang-Jun Zai, Li-Min Jia, Yong Qin, and Hong-Hui Dong. "Key links identification for urban road traffic network based on temporal-spatial distribution of traffic congestion." Modern Physics Letters B 33, no. 25 (September 10, 2019): 1950307. http://dx.doi.org/10.1142/s021798491950307x.

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Traffic congestion is now nearly ubiquitous in many urban areas. The improvement of road infrastructure is an effective way to ease traffic congestion, especially the key road links. So, it is a fundamental and important step to identify the key link for improving transportation efficiency. However, most approaches in the current literature use simulated data and need many assumption conditions. The result shows the low comprehensibility and the bad exactitude. This paper provides a new identification method of key links for urban road traffic network (URTN) based on temporal-spatial distribution of traffic congestion. The method involves identifying congestion state, computing time distribution of congestion state and determining key road link. By the cluster analysis of the history field data of URTN, the threshold to determine the traffic congestion of each link can be obtained. Then the time-interval of the traffic congestion can be computed by median filtering. At last, the time-interval coverage is defined and used to determine the target road link whether it is a key road link or not. The method is validated by a real-world case (Beijing road traffic network, BRTN). The result shows the feasibility and accuracy.

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Kumar, Dr T. Senthil. "Video based Traffic Forecasting using Convolution Neural Network Model and Transfer Learning Techniques." Journal of Innovative Image Processing 2, no. 3 (June 17, 2020): 128–34. http://dx.doi.org/10.36548/jiip.2020.3.002.

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The ideas, algorithms and models developed for application in one particular domain can be applied for solving similar issues in a different domain using the modern concept termed as transfer learning. The connection between spatiotemporal forecasting of traffic and video prediction is identified in this paper. With the developments in technology, traffic signals are replaced with smart systems and video streaming for analysis and maintenance of the traffic all over the city. Processing of these video streams requires lot of effort due to the amount of data that is generated. This paper proposed a simplified technique for processing such voluminous data. The large data set of real-world traffic is used for prediction and forecasting the urban traffic. A combination of predefined kernels are used for spatial filtering and several such transferred techniques in combination will convolutional artificial neural networks that use spectral graphs and time series models. Spatially regularized vector autoregression models and non‐spatial time series models are the baseline traffic forecasting models that are compared for forecasting the performance. In terms of training efforts, development as well as forecasting accuracy, the efficiency of urban traffic forecasting is high on implementation of video prediction algorithms and models. Further, the potential research directions are presented along the obstacles and problems in transferring schemes.

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Wang, Hong Chun, and Wen Sheng Niu. "Design and Analysis of AFDX Network Based High-Speed Avionics System of Civil Aircraft." Advanced Materials Research 462 (February 2012): 445–51. http://dx.doi.org/10.4028/www.scientific.net/amr.462.445.

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Avionics Full Duplex Switched Ethernet (AFDX) standardized as ARINC 664 is a major upgrade for integrated avionics systems of civil aircraft. It becomes the current communication technology in the context of avionics and provides a backbone network for the civil avionics system. This paper focuses on features of AFDX network protocol. Architecture of AFDX switch based on shared memory is proposed to meet the requirements of avionics real-time system. In addition, frame filtering, traffic policing and frame schedule function are used to eliminate uncertainties in huge traffic flows. End System (ES) host-target architecture is also researched in this paper. Virtual link scheduler, redundancy management, and protocol stack in ES are designed to ensure determinism and reliability of data communication. AFDX switch and ES have been successfully developed, and configuration tool, ARINC 615A loader and simulation tool related to AFDX network are also provided as package solution to support avionics system construction. Finally, AFDX switch and ESes have passed ARINC 664 protocol conformance test and certification, the test results show that our AFDX products meet the requirements of real-time communication, determinism and reliability defined in ARINC 664

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Morzhov, Sergey V., and Valeriy A. Sokolov. "An Effective Algorithm for Collision Resolution in Security Policy Rules." Modeling and Analysis of Information Systems 26, no. 1 (March 15, 2019): 75–89. http://dx.doi.org/10.18255/1818-1015-2019-1-75-89.

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A firewall is the main classic tool for monitoring and managing the network traffic on a local network. Its task is to compare the network traffic passing through it with the established security rules. These rules, which are often also called security policy, can be defined both before and during the operation of the firewall. Managing the security policy of large corporate networks is a complex task. In order to properly implement it, firewall filtering rules must be written and organized neatly and without errors. In addition, the process of changing or inserting new rules should be performed only after a careful analysis of the relationship between the rules being modified or inserted, as well as the rules that already exist in the security policy. In this article, the authors consider the classification of relations between security policy rules and also give the definition of all sorts of conflicts between them. In addition, the authors present a new efficient algorithm for detecting and resolving collisions in firewall rules by the example of the Floodlight SDN controller.

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Melkov, Dmitrij, and Šarūnas Paulikas. "ANALYSIS OF LINUX OS SECURITY TOOLS FOR PACKET FILTERING AND PROCESSING." Mokslas - Lietuvos ateitis 13 (August 24, 2021): 1–5. http://dx.doi.org/10.3846/mla.2021.15180.

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Open-source software and its components are widely used in various products, solutions, and applications, even in closed-source. Majority of them are made on Linux or Unix based systems. Netfilter framework is one of the examples. It is used for packet filtering, load-balancing, and many other manipulations with network traffic. Netfilter based packet filter iptables has been most common firewall tool for Linux systems for more than two decades. Successor of iptables – nftables was introduced in 2014. It was designed to overcome various iptables limitations. However, it hasn’t received wide popularity and transition is still ongoing. In recent years researchers and developers around the world are searching for solution to increase performance of packet processing tools. For that purpose, many of them trying to utilize eBPF (Extended Berkeley Packet Filter) with XDP (Express Data Path) data path. This paper focused on analyzing Linux OS packet filters and comparing their performances in different scenarios.

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Malekzadeh, Mina, Abdul Azim Abdul Ghani, and Shamala Subramaniam. "Design of Cyberwar Laboratory Exercises to Implement Common Security Attacks against IEEE 802.11 Wireless Networks." Journal of Computer Systems, Networks, and Communications 2010 (2010): 1–15. http://dx.doi.org/10.1155/2010/218271.

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In wireless network communications, radio waves travel through free space; hence, the information reaches any receiving point with appropriate radio receivers. This aspect makes the wireless networks vulnerable to various types of attacks. A true understanding of these attacks provides better ability to defend the network against the attacks, thus eliminating potential threats from the wireless systems. This work presents a series of cyberwar laboratory exercises that are designed for IEEE 802.11 wireless networks security courses. The exercises expose different aspects of violations in security such as confidentiality, privacy, availability, and integrity. The types of attacks include traffic analysis, rogue access point, MAC filtering, replay, man-in-the-middle, and denial of service attacks. For each exercise, the materials are presented as open-source tools along with descriptions of the respective methods, procedures, and penetration techniques.

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Lapshichyov, Vitaly, and Oleg Makarevich. "Method for Detecting and Identification of Tor Network Data by Wireshark Analyzer." Voprosy kiberbezopasnosti, no. 4(44) (2021): 73–80. http://dx.doi.org/10.21681/2311-3456-2021-4-73-80.

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Purpose of the study: development of a method that allows detecting and identifying packets of the Tor network, including obfuscated packets on the local machine of the network user, by a Wireshark sniffer using the filter syntax based on the features of the Tor network packets characteristic of the TLS v1.2 and v1.3 encryption versions; studying the possibility of using the SSL Bump attack (decrypting https traffic on a virtual server using self-signed x.509 certificates) to overcome the obfuscation of Tor network packets. Method: software analysis of transmitted network packets, decomposition of the contents of data packets according to their size and belonging to encryption protocols, a comparative method in relation to different versions of the encryption protocol and resources, synthesis of filtering rules based on the syntax of the analyzer was used. Results: an applied method was developed that allows detecting and identifying packets of the Tor Network, including obfuscated packets on the local machine of the network user, by a Wireshark sniffer based on the filtering syntax based on the signs of encryption packets of the TLS v1.2 and v1.3 versions; data on the impossibility of using the SSL Bump attack to overcome the obfuscation of the Tor network was obtained.

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Nahar, Nivedita, Prerna Dewan, and Rakesh Kumar. "An Approach to Mitigate Malware Attacks Using Netfilter's Hybrid Frame in Firewall Security." International Journal of Open Source Software and Processes 9, no. 1 (January 2018): 32–61. http://dx.doi.org/10.4018/ijossp.2018010103.

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With the steady advancements in the technology, the network security is really important these days to protect information from attackers. In this research, the main focus is on designing strong firewall filtering rules so that detection of malicious code is achieved to an optimal level. A proposed framework is introduced to improve the performance parameters such as Server response time, Web content analysis, Bandwidth, and the performance of the Network traffic load. This research work defines a new set of IPtable rules achieved by modifying the kernel source code. This is done using OpenBSD kernel source code, which results in the formation of a mini-firewall. Therefore, a new hybrid approach is proposed by adding packet filtering rules and SNORT technology in mini-firewall for malicious activity detection. It is an efficient and practical technique which will be helpful to mitigate the malware attacks and secure LAMP server. Experimental analysis has been done to conclude that around 70-75% malicious activity can be reduced by using the proposed technique.

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Han, Zhen-Hui, Xing-Shu Chen, Xue-Mei Zeng, Yi Zhu, and Ming-Yong Yin. "Detecting Proxy User Based on Communication Behavior Portrait." Computer Journal 62, no. 12 (August 15, 2019): 1777–92. http://dx.doi.org/10.1093/comjnl/bxz065.

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Abstract Proxies can help users to bypass the network filtering system, leaving the network open to banned content, and can also enable users to anonymize themselves for terminal security protection. Proxies are widely used in the current network environment. However, certain spy proxies record user information for privacy theft. In addition, attackers can use such technologies to anonymize malicious behaviors and hide identities. Such behaviors have posed serious challenges to the internal defense and security threat assessment of an organization; however, the anonymity of the proxy makes it consistent with normal network communication, and general network traffic identification methods are not able to detect it. To accurately and effectively discover proxy users in the organization based on s, a proxy user detection method based on communication behavior portrait offers the following: (1) analysis of the communication behavior from the perspective of the portrait. Based on not abandoning the effective information of the traffic itself, the label system is established by introducing exogenous data to identify the difference between proxy communication and normal communication. (2) Construction of the portrait feature set of proxy user detection based on the traffic file and external data by studying the differences between the attribute sets of communication behavior labels for proxy users and non-proxy users. (3) Design and implementation a data-driven machine learning method to supply guidance for automatic recognition of such behavior. The experimental results show that, compared with state-of-the-art methods, the detection accuracy for the proxy user exceeds 95%, and that of real network traffic environment exceeds 85%. These results indicate that the detection method proposed in this paper can accurately distinguish proxy communication and normal communication and thus achieves precise proxy user detection.

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Huang, Yuanyuan, Lu Jiazhong, Haozhe Tang, and Xiaolei Liu. "A Hybrid Association Rule-Based Method to Detect and Classify Botnets." Security and Communication Networks 2021 (September 16, 2021): 1–9. http://dx.doi.org/10.1155/2021/1028878.

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Nowadays, botnet has become a threat in the area of cybersecurity, and, worse still, it is difficult to be detected in complex network environments. Thus, traffic analysis is adopted to detect the botnet since this kind of method is practical and effective; however, the false rate is very high. The reason is that normal traffic and botnet traffic are quite close to the border, making it so difficult to be recognized. In this paper, we propose an algorithm based on a hybrid association rule to detect and classify the botnets, which can calculate botnets’ boundary traffic features and receive effects in the identification between normal and botnet traffic ideally. First, after collecting the data of different botnets in a laboratory, we analyze botnets traffic features by processing a data mining on it. The suspicious botnet traffic is filtered through DNS protocol, black and white list, and real-time feature filtering methods. Second, we analyze the correlation between domain names and IP addresses. Combining with the advantages of the existing time-based detection methods, we do a global correlation analysis on the characteristics of botnets, to judge whether the detection objects can be botnets according to these indicators. Then, we calculate these parameters, including the support, trust, and membership functions for association rules, to determine which type of botnet it belongs to. Finally, we process the test by using the public dataset and it turns out that the accuracy of our algorithm is higher.

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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 (March 9, 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 transactions or the attributes of anomalies. To accomplish this, a current segment (clustering) has been used and a few new parts (filtering, aggregating and feature analysis) have been presented. Several logs from multiple sources are used as input and this data are processed by all the modules of the framework. As each segment is instrumented for a particular undertaking towards a definitive objective, the commitment of each segment towards abnormality recognition is estimated with various execution measurements. Ultimately, the framework is able to detect a broad range of intrusions exist in the logs without using either the attack knowledge or the traffic behavioural models. The result achieved shows the direction or pathway to design anomaly detectors that can utilize raw traffic logs collected from heterogeneous sources on the network monitored and correlate the events across the logs to detect intrusions.

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Privalov, Andrey, Vera Lukicheva, Igor Kotenko, and Igor Saenko. "Increasing the Sensitivity of the Method of Early Detection of Cyber-Attacks in Telecommunication Networks Based on Traffic Analysis by Extreme Filtering." Energies 13, no. 11 (June 1, 2020): 2774. http://dx.doi.org/10.3390/en13112774.

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The paper proposes a method for improving the accuracy of early detection of cyber attacks with a small impact, in which the mathematical expectation is a fraction of the total, and the pulse repetition period is quite long. Early detection of attacks against telecommunication networks is based on traffic analysis using extreme filtering. The algorithm of fuzzy logic for deciding on the results of extreme filtering is suggested. The results of an experimental evaluation of the proposed method are presented. They demonstrate that the method is sensitive even with minor effects. In order to eliminate the redundancy of the analyzed parameters, it is enough to use the standard deviation and the correlation interval for decision making.

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Dhaya, R. "Construction of reliable image captioning system for web camera based traffic analysis on road transport application." June 2021 3, no. 2 (July 17, 2021): 114–30. http://dx.doi.org/10.36548/jtcsst.2021.2.004.

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The automated captioning of natural images with appropriate descriptions is an intriguing and complicated task in the field of image processing. On the other hand, Deep learning, which combines computer vision with natural language, has emerged in recent years. Image emphasization is a record file representation that allows a computer to understand the visual information of an image in one or more words. When it comes to connecting high-quality images, the expressive process not only requires the credentials of the primary item and scene but also the ability to analyse the status, physical characteristics, and connections. Many traditional algorithms substitute the image to the front image. The image characteristics are dynamic depending on the ambient condition of natural photographs. Image processing techniques fail to extract several characteristics from the specified image. Nonetheless, four properties from the images are accurately described by using our proposed technique. Based on the various filtering layers in the convolutional neural network (CNN), it is an advantage to extract different characteristics. The caption for the image is based on long short term memory (LSTM), which comes under recurrent neural network. In addition, the precise subtitling is compared to current conventional techniques of image processing and different deep learning models. The proposed method is performing well in natural images and web camera based images for traffic analysis. Besides, the proposed algorithm leverages good accuracy and reliable image captioning.

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Pavlyuk. "Transfer Learning: Video Prediction and Spatiotemporal Urban Traffic Forecasting." Algorithms 13, no. 2 (February 13, 2020): 39. http://dx.doi.org/10.3390/a13020039.

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Transfer learning is a modern concept that focuses on the application of ideas, models, and algorithms, developed in one applied area, for solving a similar problem in another area. In this paper, we identify links between methodologies in two fields: video prediction and spatiotemporal traffic forecasting. The similarities of the video stream and citywide traffic data structures are discovered and analogues between historical development and modern states of the methodologies are presented and discussed. The idea of transferring video prediction models to the urban traffic forecasting domain is validated using a large real-world traffic data set. The list of transferred techniques includes spatial filtering by predefined kernels in combination with time series models and spectral graph convolutional artificial neural networks. The obtained models’ forecasting performance is compared to the baseline traffic forecasting models: non-spatial time series models and spatially regularized vector autoregression models. We conclude that the application of video prediction models and algorithms for urban traffic forecasting is effective both in terms of observed forecasting accuracy and development, and training efforts. Finally, we discuss problems and obstacles of transferring methodologies and present potential directions for further research.

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Nie, Yunhui. "Integrated Management System for Coal Mine Locomotive Transportation." Electronics Science Technology and Application 7, no. 4 (January 21, 2021): 109. http://dx.doi.org/10.18686/esta.v7i4.165.

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<p>Combined with the reconstruction project of underground locomotive transportation system in Guqiao Coal Mine of Huainan Mining Group, the dissertation studies the comprehensive management system of coal mine locomotive transportation. The accuracy of ZigBee wireless positioning technology is improved through Gaussian filtering to make it applicable to more complex underground coal mine environment. Frequency conversion control is used in the motor control circuit to realize real-time calculation of the running speed of the locomotive. By analyzing the control scheme of the turnout and traffic lights, the explosion isolation and essential safety control sub-station suitable for gas mine are designed. Communication platform construction of the system is realized through wireless base station of Benan mine and industrial network of coal mine. System relevant data can be uploaded to the ground dispatching center in real time, so that the scheduling staff can reasonably arrange the scheduling tasks in a timely manner. The optimized logistics management and storage management system makes the material dispatching and transportation more reasonable and scientific. Through the analysis of the running data, the construction and personnel management of the locomotives are realized.</p>

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Mukhtar, Shaika, and Gh Rasool Begh. "Performance Analysis of Filtered OFDM Based Downlink and Uplink NOMA System over Nakagami-m Fading Channel." Journal of Telecommunications and Information Technology 2 (March 30, 2021): 11–23. http://dx.doi.org/10.26636/jtit.2021.148020.

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Efficient consumption of available resources and fulfillment of increasing demands are the two main challenges which are addressed by exploring advanced multiple access schemes along with efficient modulation techniques. To this end, non-orthogonal multiple access (NOMA) is discussed as a promising scheme for future 5G traffic. NOMA enables the users to share same resource block, permitting certain level of interference. In this paper, we propose filtered OFDM (F-OFDM) as a transmission waveform for NOMA systems, as it offers all the advantages of OFDM with the additional provision of sub-band filtering to satisfy the diverse services of the users. We examine F-OFDM in both downlink and uplink NOMA systems. Error-related performances of both downlink and uplink F-OFDM NOMA systems are analyzed and compared with conventional OFDM NOMA system over Nakagami-m fading channel. The results show that the error performance of F-OFDM NOMA is better than that of OFDM NOMA. An improvement of about 2 dB and 1 dB in bit error rate is achieved in downlink and uplink F-OFDM NOMA, respectively. Monte Carlo simulations are conducted for different values of fading parameter m, supporting the obtained analytical results

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Van Der Zijpp, Nanne J. "Dynamic Origin-Destination Matrix Estimation from Traffic Counts and Automated Vehicle Identification Data." Transportation Research Record: Journal of the Transportation Research Board 1607, no. 1 (January 1997): 87–94. http://dx.doi.org/10.3141/1607-13.

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The problem of estimating time-varying origin-destination matrices from time series of traffic counts is extended to allow for the use of partial vehicle trajectory observations. These may be obtained by using automated vehicle identification (AVI), for example, automated license plate recognition, but they may also originate from floating car data. The central problem definition allows for the use of data from induction loops and AVI equipment at arbitrary (but fixed) locations and allows for the presence of random error in traffic counts and misrecognition at the AVI stations. Although the described methods may be extended to more complex networks, the application addressed involves a single highway corridor in which no route choice alternatives exist. Analysis of the problem leads to an expression for the mutual dependencies between link volume observations and AVI data and the formulation of an estimation problem with inequality constraints. A number of traditional estimation procedures such as discounted constrained least squares (DCLS) and the Kalman filter are described, and a new procedure referred to as Bayesian updating is proposed. The advantage of this new procedure is that it deals with the inequality constraints in an appropriate statistical manner. Experiments with a large number of synthetic data sets indicate in all cases a reduction of the error of estimation due to usage of trajectory counts and, compared with the traditional DCLS and Kalman filtering methods, a superior performance of the Bayesian updating procedure.

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Antoniou, I., Vi V. Ivanov, Va V. Ivanov, and P. V. Zrelov. "Wavelet filtering of network traffic measurements." Physica A: Statistical Mechanics and its Applications 324, no. 3-4 (June 2003): 733–53. http://dx.doi.org/10.1016/s0378-4371(03)00062-1.

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Rui, Li, Xie Xiaoyu, and Duan Xueyan. "Fatigue Load Spectrum of Highway Bridge Vehicles in Plateau Mountainous Area Based on Wireless Sensing." Mobile Information Systems 2021 (April 26, 2021): 1–8. http://dx.doi.org/10.1155/2021/9955988.

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In Yunnan and other plateau mountainous areas, hydropower and mineral resources are abundant, and there are relatively many vehicles used for the transportation of large hydropower facilities. The widespread phenomenon of vehicle overload causes severe fatigue among the drivers. However, there is no reference vehicle load spectrum for fatigue analysis in the existing research. The application of wireless sensing technology to bridge health monitoring is favorable for the entire monitoring system’s low-cost and intelligent development. In this study, wireless sensors are used to collect sensing data in the measured area and perform preliminary filtering processing. The data collected by the sensing layer is aggregated at the TD gateway layer to realize local short-term storage of monitoring data, and 3G wireless transmission is used for the effective processing of the data. The clustering method is used to classify the vehicle models based on investigating the most representative expressway traffic flow information in Yunnan Province. Moreover, the weighted probability distribution model of different vehicle models is established through statistical analysis, which simplifies the composition’s fatigue intensity spectrum model. The selection of five vehicles of the equivalent model followed by a six-axle vehicle has the most significant impact on bridge damage as the standard fatigue vehicle. The research results establish a basis for the fatigue design of highway bridges in plateau and mountainous areas and provide data to establish vehicle fatigue load spectra in national highway regions.

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Baiocchi, Andrea, Gianluca Maiolini, Annachiara Mingo, and Daniele Goretti. "Adaptive Conflict-Free Optimization of Rule Sets for Network Security Packet Filtering Devices." Journal of Computer Networks and Communications 2015 (2015): 1–17. http://dx.doi.org/10.1155/2015/872326.

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Packet filtering and processing rules management in firewalls and security gateways has become commonplace in increasingly complex networks. On one side there is a need to maintain the logic of high level policies, which requires administrators to implement and update a large amount of filtering rules while keeping them conflict-free, that is, avoiding security inconsistencies. On the other side, traffic adaptive optimization of large rule lists is useful for general purpose computers used as filtering devices, without specific designed hardware, to face growing link speeds and to harden filtering devices against DoS and DDoS attacks. Our work joins the two issues in an innovative way and defines a traffic adaptive algorithm to find conflict-free optimized rule sets, by relying on information gathered with traffic logs. The proposed approach suits current technology architectures and exploits available features, like traffic log databases, to minimize the impact of ACO development on the packet filtering devices. We demonstrate the benefit entailed by the proposed algorithm through measurements on a test bed made up of real-life, commercial packet filtering devices.

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Boranbayev, S., and D. Kuanyshev. "NETWORK TRAFFIC ANALYSIS TOOLS." EurasianUnionScientists 5, no. 12(81) (January 18, 2021): 35–38. http://dx.doi.org/10.31618/esu.2413-9335.2020.5.81.1171.

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The article studies network traffic analysis tools. Various aspects of network traffic analysis are considered algorithms and approaches to network traffic analysis, as well as software and hardware tools for effectively solving this problem. The article studies the current state of this area. It is given recommendations on what to pay attention when using network traffic analysis tools.

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Chemouil, P., and J. Filipiak. "Kalman Filtering of Traffic Fluctuations for Real-Time Network Management." IFAC Proceedings Volumes 20, no. 9 (August 1987): 827–32. http://dx.doi.org/10.1016/s1474-6670(17)55811-5.

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Zuurbier, Frank, Hans van Lint, and Victor Knoop. "TRAFFIC NETWORK STATE ESTIMATION USING EXTENDED KALMAN FILTERING AND DSMART." IFAC Proceedings Volumes 39, no. 12 (January 2006): 37–42. http://dx.doi.org/10.3182/20060829-3-nl-2908.00007.

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Chemouil, Prosper, and Janusz Filipiak. "Kalman filtering of traffic fluctuations for real-time network management." Annales Des Télécommunications 44, no. 11-12 (November 1989): 633–40. http://dx.doi.org/10.1007/bf02999677.

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Marinescu, Marin, Radu Vilau, Marian Truta, Valentin Vinturis, and Octavian Fieraru. "Theoretical and Data-Based Mathematical Model of a Special Vehicle Breaking System." Advanced Materials Research 837 (November 2013): 428–33. http://dx.doi.org/10.4028/www.scientific.net/amr.837.428.

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Military vehicles have to travel, at least from time to time, on the national road network. Whenever that happens, the A military vehicle doesnt have to meet all the requirements of a civilian vehicle. Moreover, its lifecycle is much longer than a civilian vehicles one. This is the reason a military vehicle doesnt have to meet all the requirements that a civilian one has to. Nevertheless, traffic security asks for periodical checks of the braking, steering and other systems. In this respect, we have been contracted to develop a method to assess the braking systems parameter of a certain class of military vehicles. When an assessment is involved, we usually develop a basic mathematical model to be the basic feature for further investigation. Since for the old vehicles there are no database, we have created two models, one by numerical simulation and the other one by processing the experimental data. Should be mentioned that prior to use one of them for further analysis, we had to check for the models accuracy. Data have been achieved using a very up-to-date measuring system. Moreover, the data was subject to filtering procedures, taking into account the noises of the system and different other measuring errors. We have tested several vehicles that were in good technical condition. All in all, we have tried to create a reliable database that should stand back from a trusting point of view. The working characteristic features of a process can be synthetically expressed as a mathematical model. For the technical systems, this model can be obtained either based on the mathematical expressions that describe the working way of the systems components or on data-base grounds that have been previously achieved throughout experimental research. As far as the validation of the mathematical model is concerned, the modelling error should be kept under tight control. Therefore, the researcher has to permanently determine the difference between either the simulated and measured output parameters. Eventually, the validated mathematical model can be used to analyse the dynamic performances of the system or a proper way to improve them, as far as the actual evolution of the system is accurately enough described. Key words: vehicles, braking system, theoretical models, data based models, modelling error

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Akritas, P., P. G. Akishin, I. Antoniou, A. Yu Bonushkina, I. Drossinos, V. V. Ivanov, Yu L. Kalinovsky, V. V. Korenkov, and P. V. Zrelov. "Nonlinear analysis of network traffic." Chaos, Solitons & Fractals 14, no. 4 (September 2002): 595–606. http://dx.doi.org/10.1016/s0960-0779(01)00253-3.

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36

Islam, Rafiqul, Wanlei Zhou, Yang Xiang, and Abdun Naser Mahmood. "Spam filtering for network traffic security on a multi-core environment." Concurrency and Computation: Practice and Experience 21, no. 10 (July 2009): 1307–20. http://dx.doi.org/10.1002/cpe.1435.

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Salva-Garcia, Pablo, Jose M. Alcaraz-Calero, Qi Wang, Jorge Bernal Bernabe, and Antonio Skarmeta. "5G NB-IoT: Efficient Network Traffic Filtering for Multitenant IoT Cellular Networks." Security and Communication Networks 2018 (December 10, 2018): 1–21. http://dx.doi.org/10.1155/2018/9291506.

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Internet of Things (IoT) is a key business driver for the upcoming fifth-generation (5G) mobile networks, which in turn will enable numerous innovative IoT applications such as smart city, mobile health, and other massive IoT use cases being defined in 5G standards. To truly unlock the hidden value of such mission-critical IoT applications in a large scale in the 5G era, advanced self-protection capabilities are entailed in 5G-based Narrowband IoT (NB-IoT) networks to efficiently fight off cyber-attacks such as widespread Distributed Denial of Service (DDoS) attacks. However, insufficient research has been conducted in this crucial area, in particular, few if any solutions are capable of dealing with the multiple encapsulated 5G traffic for IoT security management. This paper proposes and prototypes a new security framework to achieve the highly desirable self-organizing networking capabilities to secure virtualized, multitenant 5G-based IoT traffic through an autonomic control loop featured with efficient 5G-aware traffic filtering. Empirical results have validated the design and implementation and demonstrated the efficiency of the proposed system, which is capable of processing thousands of 5G-aware traffic filtering rules and thus enables timely protection against large-scale attacks.

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Wang, Jian Ling, and Hong Bo Lai. "Congestion Analysis on Urban Traffic Network." Advanced Materials Research 756-759 (September 2013): 1635–38. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.1635.

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this paper explores the urban traffic congestion problems, from four angles: the discrimination of the traffic congestion, cause of traffic congestion, congestion characteristics of urban traffic network, measures to solve traffic congestion, to conduct in-depth analysis of traffic congestion.

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Fatkieva, Rosa Ravilievna. "Correlation analysis of abnormal network traffic." SPIIRAS Proceedings 4, no. 23 (March 17, 2014): 93. http://dx.doi.org/10.15622/sp.23.5.

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Husni. "Spektral Analysis of Honeypot Network Traffic." SPIIRAS Proceedings, no. 7 (March 17, 2014): 177. http://dx.doi.org/10.15622/sp.7.17.

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Ayedemir, M., L. Bottomley, M. Coffin, C. Jeffries, P. Kiessler, K. Kumar, W. Ligon, et al. "Two tools for network traffic analysis." Computer Networks 36, no. 2-3 (July 2001): 169–79. http://dx.doi.org/10.1016/s1389-1286(00)00188-2.

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Sang, Aimin, and San-qi Li. "A predictability analysis of network traffic." Computer Networks 39, no. 4 (July 2002): 329–45. http://dx.doi.org/10.1016/s1389-1286(01)00304-8.

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Lakhina, Anukool, Konstantina Papagiannaki, Mark Crovella, Christophe Diot, Eric D. Kolaczyk, and Nina Taft. "Structural analysis of network traffic flows." ACM SIGMETRICS Performance Evaluation Review 32, no. 1 (June 2004): 61–72. http://dx.doi.org/10.1145/1012888.1005697.

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Dinh Dang, Trang, Sándor Molnár, and István Maricza. "Performance analysis of multifractal network traffic." European Transactions on Telecommunications 15, no. 2 (March 2004): 63–78. http://dx.doi.org/10.1002/ett.955.

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Miao, Yuantian, Zichan Ruan, Lei Pan, Jun Zhang, and Yang Xiang. "Comprehensive analysis of network traffic data." Concurrency and Computation: Practice and Experience 30, no. 5 (July 7, 2017): e4181. http://dx.doi.org/10.1002/cpe.4181.

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Shin, Seon-Ho, and Myungkeun Yoon. "Virtual vectors and network traffic analysis." IEEE Network 26, no. 1 (January 2012): 22–26. http://dx.doi.org/10.1109/mnet.2012.6135852.

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Fotiadou, Konstantina, Terpsichori-Helen Velivassaki, Artemis Voulkidis, Dimitrios Skias, Sofia Tsekeridou, and Theodore Zahariadis. "Network Traffic Anomaly Detection via Deep Learning." Information 12, no. 5 (May 19, 2021): 215. http://dx.doi.org/10.3390/info12050215.

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Network intrusion detection is a key pillar towards the sustainability and normal operation of information systems. Complex threat patterns and malicious actors are able to cause severe damages to cyber-systems. In this work, we propose novel Deep Learning formulations for detecting threats and alerts on network logs that were acquired by pfSense, an open-source software that acts as firewall on FreeBSD operating system. pfSense integrates several powerful security services such as firewall, URL filtering, and virtual private networking among others. The main goal of this study is to analyse the logs that were acquired by a local installation of pfSense software, in order to provide a powerful and efficient solution that controls traffic flow based on patterns that are automatically learnt via the proposed, challenging DL architectures. For this purpose, we exploit the Convolutional Neural Networks (CNNs), and the Long Short Term Memory Networks (LSTMs) in order to construct robust multi-class classifiers, able to assign each new network log instance that reaches our system into its corresponding category. The performance of our scheme is evaluated by conducting several quantitative experiments, and by comparing to state-of-the-art formulations.

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Gazis, Denos, and Chiu Liu. "Kalman filtering estimation of traffic counts for two network links in tandem." Transportation Research Part B: Methodological 37, no. 8 (September 2003): 737–45. http://dx.doi.org/10.1016/s0191-2615(02)00059-0.

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Zilberman, Polina, Rami Puzis, and Yuval Elovici. "On Network Footprint of Traffic Inspection and Filtering at Global Scrubbing Centers." IEEE Transactions on Dependable and Secure Computing 14, no. 5 (September 1, 2017): 521–34. http://dx.doi.org/10.1109/tdsc.2015.2494039.

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Deng, Jing, Richard Han, and Shivakant Mishra. "Decorrelating wireless sensor network traffic to inhibit traffic analysis attacks." Pervasive and Mobile Computing 2, no. 2 (April 2006): 159–86. http://dx.doi.org/10.1016/j.pmcj.2005.12.003.

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Journal articles on the topic 'Analysis and filtering of network traffic'

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