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Relevant bibliographies by topics / Network traffic detection

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Academic literature on the topic 'Network traffic detection'

Author: Grafiati

Published: 28 June 2021

Last updated: 29 July 2025

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Journal articles on the topic "Network traffic detection"

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Chadrack, Irabaruta, and Dr Nyesheja Muhire Enan. "AI Powered Network Traffic Detection." Journal of Information and Technology 5, no. 2 (2025): 53–65. https://doi.org/10.70619/vol5iss2pp53-65.

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This study presents an AI-powered network traffic detection framework capable of recognizing anomalies and addressing cyber threats in real-time. Traditional detection systems struggle to keep pace with evolving threats, necessitating more adaptive and intelligent approaches. To this end, the research integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models to enhance detection accuracy and operational efficiency. The framework is evaluated using benchmark datasets such as UNSW-NB15 and CICIDS2017, focusing on performance metrics including accuracy, precision, re

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Katuk, Norliza, Mohamad Sabri Sinal, Mohammed Gamal Ahmed Al-Samman, and Ijaz Ahmad. "An observational mechanism for detection of distributed denial-of-service attacks." International Journal of Advances in Applied Sciences 12, no. 2 (2023): 121. http://dx.doi.org/10.11591/ijaas.v12.i2.pp121-132.

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<span>This study proposes a continuous mechanism for detecting distributed denial of service (DDoS) attacks from network traffic data. The mechanism aims to systematically organise traffic data and prepare them for DDoS attack detection using convolutional deep-learning neural networks. The proposed mechanism contains ten phases covering activities, including data preprocessing, feature selection, data labelling, model building, model evaluation, DDoS detection, attack pattern identification, alert creation, notification delivery, and periodical data sampling. The evaluation results sugg

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Norliza, Katuk, Gamal Ahmed Al-Samman Mohammed, and Ahmad Ijaz. "An observational mechanism for detection of distributed denial-of-service attacks." International Journal of Advances in Applied Sciences (IJAAS) 12, no. 2 (2023): 132. https://doi.org/10.11591/ijaas.v12.i2.pp121-132.

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This study proposes a continuous mechanism for detecting distributed denial of service (DDoS) attacks from network traffic data. The mechanism aims to systematically organise traffic data and prepare them for DDoS attack detection using convolutional deep-learning neural networks. The proposed mechanism contains ten phases covering activities, including data preprocessing, feature selection, data labelling, model building, model evaluation, DDoS detection, attack pattern identification, alert creation, notification delivery, and periodical data sampling. The evaluation results suggested that t

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Jiang, Ding De, Cheng Yao, Zheng Zheng Xu, Peng Zhang, Zhen Yuan, and Wen Da Qin. "An Continuous Wavelet Transform-Based Detection Approach to Traffic Anomalies." Applied Mechanics and Materials 130-134 (October 2011): 2098–102. http://dx.doi.org/10.4028/www.scientific.net/amm.130-134.2098.

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Anomalous traffic often has a significant impact on network activities and lead to the severe damage to our networks because they usually are involved with network faults and network attacks. How to detect effectively network traffic anomalies is a challenge for network operators and researchers. This paper proposes a novel method for detecting traffic anomalies in a network, based on continuous wavelet transform. Firstly, continuous wavelet transforms are performed for network traffic in several scales. We then use multi-scale analysis theory to extract traffic characteristics. And these char

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Praveena, Nutakki, Dr Ujwal A. Lanjewar, and Chilakalapudi Meher Babu. "VIABLE NETWORK INTRUSION DETECTION ON WIRELESS ADHOC NETWORKS." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 5, no. 1 (2013): 29–34. http://dx.doi.org/10.24297/ijct.v5i1.4383.

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Control architecture for resource allocation in satellite networks is proposed, along with the specification of performance indexes and control strategies. The latter, besides being based on information on traffic statistics and network status, rely upon some knowledge of the fading conditions over the satellite network channels. The resource allocation problem consists of the assignment, by a master station, of a total available bandwidth among traffic earth stations in the presence of different traffic types. Traffic stations are assumed to measure continuously their signal fade level, but t

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Fu, Xingbing, Xuewen Zhang, Jianfeng Fu, Bingjin Wu, and Jianwu Zhang. "Deep metric learning based approach for network intrusion detection." Journal of Physics: Conference Series 2504, no. 1 (2023): 012037. http://dx.doi.org/10.1088/1742-6596/2504/1/012037.

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Abstract Today, intrusion detection systems are the way to defend network intrusion flows. In this paper, to categorize network traffic data, we proposed a novel method for detecting network intrusions. It builds intrusion detection models using a deep metric learning (DML) strategy that incorporates two multi-scale convolutional neural networks (MSCNN) and a Triplet network. During the phase of training MSCNN networks, the network traffic data are divided into attack network traffic data and normal data, and we train two distinct MSCNN networks on the basis of these two datasets. To determine

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Son, Vu Ngoc. "Optimizing Network Anomaly Detection Based on Network Traffic." International Journal of Emerging Technology and Advanced Engineering 11, no. 11 (2021): 53–60. http://dx.doi.org/10.46338/ijetae1121_07.

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Cyber-attack is a very hot topic today. Nowadays, systems must always be connected to the internet, and network infrastructure keeps growing in both scale and complexity. Therefore, the problem of detecting and warning cyber-attacks is now very urgent. To improve the effectiveness of detecting cyber-attacks, many methods and techniques were applied. In this paper, we propose to apply two methods of optimizing cyber-attack detection based on the IDS 2018 dataset using Principal Component Analysis (PCA) and machine learning algorithms. In the experimental section, we compare and evaluate the eff

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Pratomo, Baskoro A., Pete Burnap, and George Theodorakopoulos. "BLATTA: Early Exploit Detection on Network Traffic with Recurrent Neural Networks." Security and Communication Networks 2020 (August 4, 2020): 1–15. http://dx.doi.org/10.1155/2020/8826038.

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Detecting exploits is crucial since the effect of undetected ones can be devastating. Identifying their presence on the network allows us to respond and block their malicious payload before they cause damage to the system. Inspecting the payload of network traffic may offer better performance in detecting exploits as they tend to hide their presence and behave similarly to legitimate traffic. Previous works on deep packet inspection for detecting malicious traffic regularly read the full length of application layer messages. As the length varies, longer messages will take more time to analyse,

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Oh, Changhyeon, and Yuseok Ban. "Cross-Modality Interaction-Based Traffic Accident Classification." Applied Sciences 14, no. 5 (2024): 1958. http://dx.doi.org/10.3390/app14051958.

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Traffic accidents on the road lead to serious personal and material damage. Furthermore, preventing secondary accidents caused by traffic accidents is crucial. As various technologies for detecting traffic accidents in videos using deep learning are being researched, this paper proposes a method to classify accident videos based on a video highlight detection network. To utilize video highlight detection for traffic accident classification, we generate information using the existing traffic accident videos. Moreover, we introduce the Car Crash Highlights Dataset (CCHD). This dataset contains a

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Zhiwei Zhang, Zhiwei Zhang, Guiyuan Tang Zhiwei Zhang, Baoquan Ren Guiyuan Tang, Baoquan Ren Baoquan Ren, and Yulong Shen Baoquan Ren. "TV-ADS: A Smarter Attack Detection Scheme Based on Traffic Visualization of Wireless Network Event Cell." 網際網路技術學刊 25, no. 2 (2024): 301–11. http://dx.doi.org/10.53106/160792642024032502012.

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<p>To protect the increasing cyberspace assets, attack detection systems (ADSs) as well as intrusion detection systems (IDSs) have been equipped in various network environments. Recently, with the development of big data, machine learning, deep learning, neural networks and other artificial intelligence (AI) technologies, more and more ADSs/IDSs based on Artificial Intelligence are presented in academia and industry. Particularly, depending on the outstanding performance and efficiency in recognizing and classifying images, computer vision algorithms have been employed to detect maliciou

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Dissertations / Theses on the topic "Network traffic detection"

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Brauckhoff, Daniela. "Network traffic anomaly detection and evaluation." Aachen Shaker, 2010. http://d-nb.info/1001177746/04.

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Udd, Robert. "Anomaly Detection in SCADA Network Traffic." Thesis, Linköpings universitet, Programvara och system, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-122680.

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Critical infrastructure provides us with the most important parts of modern society, electricity, water and transport. To increase efficiency and to meet new demands from the customer remote monitoring and control of the systems is necessary. This opens new ways for an attacker to reach the Supervisory Control And Data Acquisition (SCADA) systems that control and monitors the physical processes involved. This also increases the need for security features specially designed for these settings. Anomaly-based detection is a technique suitable for the more deterministic SCADA systems. This thesis

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Yellapragada, Ramani. "Probabilistic Model for Detecting Network Traffic Anomalies." Ohio University / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1088538020.

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Zhang, Junjie. "Effective and scalable botnet detection in network traffic." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44837.

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Botnets represent one of the most serious threats against Internet security since they serve as platforms that are responsible for the vast majority of large-scale and coordinated cyber attacks, such as distributed denial of service, spamming, and information stolen. Detecting botnets is therefore of great importance and a number of network-based botnet detection systems have been proposed. However, as botnets perform attacks in an increasingly stealthy way and the volume of network traffic is rapidly growing, existing botnet detection systems are faced with significant challenges in terms of

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Babaie, Tahereh Tara. "New Methods for Network Traffic Anomaly Detection." Thesis, The University of Sydney, 2014. http://hdl.handle.net/2123/12032.

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In this thesis we examine the efficacy of applying outlier detection techniques to understand the behaviour of anomalies in communication network traffic. We have identified several shortcomings. Our most finding is that known techniques either focus on characterizing the spatial or temporal behaviour of traffic but rarely both. For example DoS attacks are anomalies which violate temporal patterns while port scans violate the spatial equilibrium of network traffic. To address this observed weakness we have designed a new method for outlier detection based spectral decomposition of the Hankel m

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Vu, Hong Linh. "DNS Traffic Analysis for Network-based Malware Detection." Thesis, KTH, Kommunikationssystem, CoS, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-93842.

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Botnets are generally recognized as one of the most challenging threats on the Internet today. Botnets have been involved in many attacks targeting multinational organizations and even nationwide internet services. As more effective detection and mitigation approaches are proposed by security researchers, botnet developers are employing new techniques for evasion. It is not surprising that the Domain Name System (DNS) is abused by botnets for the purposes of evasion, because of the important role of DNS in the operation of the Internet. DNS provides a flexible mapping between domain names and

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Dandurand, Luc. "Detection of network infrastructure attacks using artificial traffic." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/mq44906.pdf.

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Gupta, Vikas. "File Detection in Network Traffic Using Approximate Matching." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for telematikk, 2013. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-22696.

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Virtually every day data breach incidents are reported in the news. Scammers, fraudsters, hackers and malicious insiders are raking in millions with sensitive business and personal information. Not all incidents involve cunning and astute hackers. The involvement of insiders is ever increasing. Data information leakage is a critical issue for many companies, especially nowadays where every employee has an access to high speed internet.In the past, email was the only gateway to send out information but with the advent of technologies like SaaS (e.g. Dropbox) and other similar services, possible

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Brauckhoff, Daniela [Verfasser]. "Network Traffic Anomaly Detection and Evaluation / Daniela Brauckhoff." Aachen : Shaker, 2010. http://d-nb.info/1122546610/34.

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Taggart, Benjamin T. "Incorporating neural network traffic prediction into freeway incident detection." Morgantown, W. Va. : [West Virginia University Libraries], 1999. http://etd.wvu.edu/templates/showETD.cfm?recnum=723.

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Thesis (M.S.)--West Virginia University, 1999.<br>Title from document title page. Document formatted into pages; contains viii, 55 p. : ill. (some col.) Vita. Includes abstract. Includes bibliographical references (p. 52-55).

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Books on the topic "Network traffic detection"

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Bhuyan, Monowar H., Dhruba K. Bhattacharyya, and Jugal K. Kalita. Network Traffic Anomaly Detection and Prevention. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65188-0.

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Thancanamootoo, S. Automatic detection of traffic incidents on a signal-controlled road network. University of Newcastle upon Tyne, Transport Operations Research Group, 1988.

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Biersack, Ernst. Data Traffic Monitoring and Analysis: From Measurement, Classification, and Anomaly Detection to Quality of Experience. Springer Berlin Heidelberg, 2013.

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Rajchel, Brett. Unsupervised Learning of Network Traffic Behaviors for Insider Threat Detection. Independently Published, 2021.

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Kalita, Jugal K., Monowar H. Bhuyan, and Dhruba K. Bhattacharyya. Network Traffic Anomaly Detection and Prevention: Concepts, Techniques, and Tools. Springer, 2018.

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Kalita, Jugal K., Monowar H. Bhuyan, and Dhruba K. Bhattacharyya. Network Traffic Anomaly Detection and Prevention: Concepts, Techniques, and Tools. Springer, 2017.

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Tari, Zahir, Adil Fahad, Abdulmohsen Almalawi, and Xun Yi. Network Classification for Traffic Management: Anomaly detection, feature selection, clustering and classification. Institution of Engineering and Technology, 2020. http://dx.doi.org/10.1049/pbpc032e.

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Tari, Zahir, Adil Fahad, Abdulmohsen Almalawi, and Xun Yi. Network Classification for Traffic Management: Anomaly Detection, Feature Selection, Clustering and Classification. Institution of Engineering & Technology, 2020.

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England, Highways. Design Manual for Roads and Bridges : Vol. 9 : Network - Traffic Control and Communications, Section 1 : Detection Technology, Part 2: Detection on the Motorway and Trunk Road Network. Stationery Office, The, 2017.

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England, Highways. Design Manual for Roads and Bridges : Vol. 9 : Network - Traffic Control and Communications, Section 1 : Detection Technology, Part 2: Detection on the Motorway and Trunk Road Network Annex F. Stationery Office, The, 2018.

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Book chapters on the topic "Network traffic detection"

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Yao, Danfeng Daphne, Xiaokui Shu, Long Cheng, and Salvatore J. Stolfo. "Anomaly Detection on Network Traffic." In Anomaly Detection as a Service. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-031-02354-5_6.

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Cherukuri, Aswani Kumar, Sumaiya Thaseen Ikram, Gang Li, and Xiao Liu. "Detection of Anomalous Encrypted Traffic." In Encrypted Network Traffic Analysis. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-62909-9_4.

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Cherukuri, Aswani Kumar, Sumaiya Thaseen Ikram, Gang Li, and Xiao Liu. "Artificial Intelligence-Based Approaches for Anomaly Detection." In Encrypted Network Traffic Analysis. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-62909-9_5.

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Liu, ChenHuan, QianKun Liu, ShanShan Hao, CongXiao Bao, and Xing Li. "IPv6-Darknet Network Traffic Detection." In Lecture Notes in Computer Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78612-0_19.

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Krzysztoń, Mateusz, Marcin Lew, and Michał Marks. "NAD: Machine Learning Based Component for Unknown Attack Detection in Network Traffic." In Cybersecurity of Digital Service Chains. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04036-8_4.

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AbstractDetection of unknown attacks is challenging due to the lack of exemplary attack vectors. However, previously unknown attacks are a significant danger for systems due to a lack of tools for protecting systems against them, especially in fast-evolving Internet of Things (IoT) technology. The most widely used approach for malicious behaviour of the monitored system is detecting anomalies. The vicious behaviour might result from an attack (both known and unknown) or accidental breakdown. We present a Net Anomaly Detector (NAD) system that uses one-class classification Machine Learning tech

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Cui, Qian, Guy-Vincent Jourdan, Gregor V. Bochmann, and Iosif-Viorel Onut. "Proactive Detection of Phishing Kit Traffic." In Applied Cryptography and Network Security. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78375-4_11.

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Coluccia, Angelo, Alessandro D’Alconzo, and Fabio Ricciato. "Distribution-Based Anomaly Detection in Network Traffic." In Data Traffic Monitoring and Analysis. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36784-7_9.

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Wang, Hanyang, Sirui Zhou, Honglei Li, et al. "Deep Learning Network Intrusion Detection Based on Network Traffic." In Lecture Notes in Computer Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06791-4_16.

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Bialas, Andrzej, Marcin Michalak, and Barbara Flisiuk. "Anomaly Detection in Network Traffic Security Assurance." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19501-4_5.

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de la Puerta, José Gaviria, Iker Pastor-López, Borja Sanz, and Pablo G. Bringas. "Network Traffic Analysis for Android Malware Detection." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29859-3_40.

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Conference papers on the topic "Network traffic detection"

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Žada, Frane, and Martina Antonić. "Network Traffic Intrusion Detection." In 2024 International Conference on Software, Telecommunications and Computer Networks (SoftCOM). IEEE, 2024. http://dx.doi.org/10.23919/softcom62040.2024.10721884.

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Deepika, D., Deepika Pogiri, Lokesh Raj Pandravisham, Yashwanth Kumar Prudvi, and Sathvik Reddy Ramannagari. "Anomaly Network Traffic Detection of Wireless Network System." In 2024 5th International Conference on Electronics and Sustainable Communication Systems (ICESC). IEEE, 2024. http://dx.doi.org/10.1109/icesc60852.2024.10689805.

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Korniszuk, Krzysztof, and Bartosz Sawicki. "Autoencoder-Based Anomaly Detection in Network Traffic." In 2024 25th International Conference on Computational Problems of Electrical Engineering (CPEE). IEEE, 2024. http://dx.doi.org/10.1109/cpee64152.2024.10720411.

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Sharma, Jatin, Kanwarpartap Singh Gill, Deepak Upadhyay, and Swati Devliyal. "Revolutionizing Network Safety: Convolutional Neural Networks for Traffic Anomaly Detection." In 2024 Asian Conference on Intelligent Technologies (ACOIT). IEEE, 2024. https://doi.org/10.1109/acoit62457.2024.10939940.

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Gu, Yonghao, Xiaoqing Zhang, Hao Xu, and Tiejun Wu. "DyGCN: Dynamic Graph Convolution Network-based Anomaly Network Traffic Detection." In 2024 IEEE 23rd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). IEEE, 2024. https://doi.org/10.1109/trustcom63139.2024.00253.

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Koumar, Josef, Jaroslav Pesek, Kamil Jerabek, and Tomáš Čejka. "Towards Building Network Outlier Detection System for Network Traffic Monitoring." In NOMS 2025-2025 IEEE Network Operations and Management Symposium. IEEE, 2025. https://doi.org/10.1109/noms57970.2025.11073727.

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Jerabek, Kamil, Josef Koumar, Jiří Setinský, and Jaroslav Pesek. "Explainable Anomaly Detection in Network Traffic Using LLM." In NOMS 2025-2025 IEEE Network Operations and Management Symposium. IEEE, 2025. https://doi.org/10.1109/noms57970.2025.11073574.

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Luo, Yongsheng. "Using Graph Neural Networks to Improve Network Traffic Anomaly Detection Performance." In 2025 4th International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE). IEEE, 2025. https://doi.org/10.1109/icdcece65353.2025.11035232.

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Miani, Rodrigo Sanches, Regina Melo Silveira, Rafael Pasquini, Luciana Arantes, and Pierre Sens. "Network Slice Monitoring System For Disaster Detection." In Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos. Sociedade Brasileira de Computação, 2025. https://doi.org/10.5753/sbrc.2025.6415.

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The use of monitoring systems for risk areas has increased significantly, especially with the use of IoT applications, which has driven Early Warning Systems (EWS). However, risk situations due to natural disasters can cause malfunction of communication networks making such monitoring unfeasible. In this paper we propose an unorthodox method for disaster detection, based on sliced network traffic monitoring. In the presented system the network traffic is monitored by a Monitoring Agent module that uses a Random Forests method to identify anomalous network traffic events. Simulation results sho

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Goel, Avnish, Apoorv Kashyap, B. Devesha Reddy, Rochak Kaushik, S. Nagasundari, and Prasad B. Honnavali. "Detection of VPN Network Traffic." In 2022 IEEE Delhi Section Conference (DELCON). IEEE, 2022. http://dx.doi.org/10.1109/delcon54057.2022.9753621.

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Reports on the topic "Network traffic detection"

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De Lucia, Michael, Daniel Krych, Stephen Raio, and Jason Ellis. An Empirical Investigation of Packet Header-Only Network Traffic Anomaly Detection and Classification. DEVCOM Army Research Laboratory, 2023. http://dx.doi.org/10.21236/ad1194755.

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Bardhan, Shuvo, Mitsuhiro Hatada, James Filliben, Douglas Montgomery, and Alexander Jia. An Evaluation Design for Comparing Netflow Based Network Anomaly Detection Systems Using Synthetic Malicious Traffic. National Institute of Standards and Technology, 2021. http://dx.doi.org/10.6028/nist.tn.2142.

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Pasupuleti, Murali Krishna. Quantum Intelligence: Machine Learning Algorithms for Secure Quantum Networks. National Education Services, 2025. https://doi.org/10.62311/nesx/rr925.

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Abstract: As quantum computing and quantum communication technologies advance, securing quantum networks against emerging cyber threats has become a critical challenge. Traditional cryptographic methods are vulnerable to quantum attacks, necessitating the development of AI-driven security solutions. This research explores the integration of machine learning (ML) algorithms with quantum cryptographic frameworks to enhance Quantum Key Distribution (QKD), post-quantum cryptography (PQC), and real-time threat detection. AI-powered quantum security mechanisms, including neural network-based quantum

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Albrecht, Jochen, Andreas Petutschnig, Laxmi Ramasubramanian, Bernd Resch, and Aleisha Wright. Comparing Twitter and LODES Data for Detecting Commuter Mobility Patterns. Mineta Transportation Institute, 2021. http://dx.doi.org/10.31979/mti.2021.2037.

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Local and regional planners struggle to keep up with rapid changes in mobility patterns. This exploratory research is framed with the overarching goal of asking if and how geo-social network data (GSND), in this case, Twitter data, can be used to understand and explain commuting and non-commuting travel patterns. The research project set out to determine whether GSND may be used to augment US Census LODES data beyond commuting trips and whether it may serve as a short-term substitute for commuting trips. It turns out that the reverse is true and the common practice of employing LODES data to e

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