Bibliografie tematiche / Analysis of encrypted network flow

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Letteratura scientifica selezionata sul tema "Analysis of encrypted network flow"

Autore: Grafiati

Pubblicato: 16 novembre 2024

Ultima modifica: 31 luglio 2025

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Articoli di riviste sul tema "Analysis of encrypted network flow"

Yan, Xiaodan. "Deep Learning-Based Efficient Analysis for Encrypted Traffic." Applied Sciences 13, no. 21 (2023): 11776. http://dx.doi.org/10.3390/app132111776.

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To safeguard user privacy, critical Internet traffic is often transmitted using encryption. While encryption is crucial for protecting sensitive information, it poses challenges for traffic identification and poses hidden dangers to network security. As a result, the precise classification of encrypted network traffic has become a crucial problem in network security. In light of this, our paper proposes an encrypted traffic identification method based on the C-LSTM model for encrypted traffic recognition by leveraging the power of deep learning. This method can effectively extract spatial and

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Jiang, Ziyu. "Bidirectional Flow-Based Image Representation Method for Detecting Network Traffic Service Categories." Highlights in Science, Engineering and Technology 85 (March 13, 2024): 89–95. http://dx.doi.org/10.54097/mwyge502.

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Network traffic identification is crucial for network resource management and improving service quality. Traditional methods, such as port-based and deep packet inspection approaches, face challenges due to the increasing complexity of network environments, privacy concerns, and the emergence of encrypted traffic. This paper aims to address the issues of low accuracy and slow operation speed in encrypted traffic classification while ensuring the protection of user privacy. We propose a data processing method that transforms network traffic into images representing bidirectional flow packet arr

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Ma, Chencheng, Xuehui Du, and Lifeng Cao. "Improved KNN Algorithm for Fine-Grained Classification of Encrypted Network Flow." Electronics 9, no. 2 (2020): 324. http://dx.doi.org/10.3390/electronics9020324.

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The fine-grained classification of encrypted traffic is important for network security analysis. Malicious attacks are usually encrypted and simulated as normal application or content traffic. Supervised machine learning methods are widely used for traffic classification and show good performances. However, they need a large amount of labeled data to train a model, while labeled data is hard to obtain. Aiming at solving this problem, this paper proposes a method to train a model based on the K-nearest neighbor (KNN) algorithm, which only needs a small amount of data. Due to the fact that the i

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Meghdouri, Fares, Tanja Zseby, and Félix Iglesias. "Analysis of Lightweight Feature Vectors for Attack Detection in Network Traffic." Applied Sciences 8, no. 11 (2018): 2196. http://dx.doi.org/10.3390/app8112196.

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The consolidation of encryption and big data in network communications have made deep packet inspection no longer feasible in large networks. Early attack detection requires feature vectors which are easy to extract, process, and analyze, allowing their generation also from encrypted traffic. So far, experts have selected features based on their intuition, previous research, or acritically assuming standards, but there is no general agreement about the features to use for attack detection in a broad scope. We compared five lightweight feature sets that have been proposed in the scientific lite

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Afzal, Asmara, Mehdi Hussain, Shahzad Saleem, M. Khuram Shahzad, Anthony T. S. Ho, and Ki-Hyun Jung. "Encrypted Network Traffic Analysis of Secure Instant Messaging Application: A Case Study of Signal Messenger App." Applied Sciences 11, no. 17 (2021): 7789. http://dx.doi.org/10.3390/app11177789.

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Instant messaging applications (apps) have played a vital role in online interaction, especially under COVID-19 lockdown protocols. Apps with security provisions are able to provide confidentiality through end-to-end encryption. Ill-intentioned individuals and groups use these security services to their advantage by using the apps for criminal, illicit, or fraudulent activities. During an investigation, the provision of end-to-end encryption in apps increases the complexity for digital forensics investigators. This study aims to provide a network forensic strategy to identify the potential art

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Ravi, V., and A. S. Poornima. "SecMa: A Novel Multimodal Autoencoder Framework for Encrypted IoT Traffic Analysis and Attack Detection." Engineering, Technology & Applied Science Research 15, no. 3 (2025): 23020–26. https://doi.org/10.48084/etasr.10336.

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The exponential growth of encrypted Internet of Things (IoT) traffic has introduced significant cybersecurity challenges, including the complexity of analyzing encrypted payload data, managing heterogeneous device behavior, and addressing resource constraints. Traditional methods achieve low detection rates (45-60%) and struggle to balance accuracy, efficiency, and privacy. This paper proposes SecMa, a novel multimodal autoencoder framework designed to address these limitations in encrypted IoT traffic analysis and attack detection. SecMa processes three complementary feature modalities—networ

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Oh, Chaeyeon, Joonseo Ha, and Heejun Roh. "A Survey on TLS-Encrypted Malware Network Traffic Analysis Applicable to Security Operations Centers." Applied Sciences 12, no. 1 (2021): 155. http://dx.doi.org/10.3390/app12010155.

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Recently, a majority of security operations centers (SOCs) have been facing a critical issue of increased adoption of transport layer security (TLS) encryption on the Internet, in network traffic analysis (NTA). To this end, in this survey article, we present existing research on NTA and related areas, primarily focusing on TLS-encrypted traffic to detect and classify malicious traffic with deployment scenarios for SOCs. Security experts in SOCs and researchers in academia can obtain useful information from our survey, as the main focus of our survey is NTA methods applicable to malware detect

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Haywood, Gregor Tamati, and Saleem Noel Bhatti. "Defence against Side-Channel Attacks for Encrypted Network Communication Using Multiple Paths." Cryptography 8, no. 2 (2024): 22. http://dx.doi.org/10.3390/cryptography8020022.

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As more network communication is encrypted to provide data privacy for users, attackers are focusing their attention on traffic analysis methods for side-channel attacks on user privacy. These attacks exploit patterns in particular features of communication flows such as interpacket timings and packet sizes. Unsupervised machine learning approaches, such as Hidden Markov Models (HMMs), can be trained on unlabelled data to estimate these flow attributes from an exposed packet flow, even one that is encrypted, so it is highly feasible for an eavesdropper to perform this attack. Traditional defen

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Hu, Xinyi, Chunxiang Gu, Yihang Chen, and Fushan Wei. "CBD: A Deep-Learning-Based Scheme for Encrypted Traffic Classification with a General Pre-Training Method." Sensors 21, no. 24 (2021): 8231. http://dx.doi.org/10.3390/s21248231.

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With the rapid increase in encrypted traffic in the network environment and the increasing proportion of encrypted traffic, the study of encrypted traffic classification has become increasingly important as a part of traffic analysis. At present, in a closed environment, the classification of encrypted traffic has been fully studied, but these classification models are often only for labeled data and difficult to apply in real environments. To solve these problems, we propose a transferable model called CBD with generalization abilities for encrypted traffic classification in real environments

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Vizitiu, Anamaria, Cosmin-Ioan Nita, Radu Miron Toev, Tudor Suditu, Constantin Suciu, and Lucian Mihai Itu. "Framework for Privacy-Preserving Wearable Health Data Analysis: Proof-of-Concept Study for Atrial Fibrillation Detection." Applied Sciences 11, no. 19 (2021): 9049. http://dx.doi.org/10.3390/app11199049.

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Medical wearable devices monitor health data and, coupled with data analytics, cloud computing, and artificial intelligence (AI), enable early detection of disease. Privacy issues arise when personal health information is sent or processed outside the device. We propose a framework that ensures the privacy and integrity of personal medical data while performing AI-based homomorphically encrypted data analytics in the cloud. The main contributions are: (i) a privacy-preserving cloud-based machine learning framework for wearable devices, (ii) CipherML—a library for fast implementation and deploy

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Tesi sul tema "Analysis of encrypted network flow"

Toure, Almamy. "Collection, analysis and harnessing of communication flows for cyber-attack detection." Electronic Thesis or Diss., Valenciennes, Université Polytechnique Hauts-de-France, 2024. http://www.theses.fr/2024UPHF0023.

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La complexité croissante des cyberattaques, caractérisée par une diversification des techniques d'attaque, une expansion des surfaces d'attaque et une interconnexion croissante d'applications avec Internet, rend impérative la gestion du trafic réseau en milieu professionnel. Les entreprises de tous types collectent et analysent les flux réseau et les journaux de logs pour assurer la sécurité des données échangées et prévenir la compromission des systèmes d'information. Cependant, les techniques de collecte et de traitement des données du trafic réseau varient d'un jeu de données à l'autre, et

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Izadinia, Vafa Dario. "Fingerprinting Encrypted Tunnel Endpoints." Diss., University of Pretoria, 2005. http://hdl.handle.net/2263/25351.

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Operating System fingerprinting is a reconnaissance method used by Whitehats and Blackhats alike. Current techniques for fingerprinting do not take into account tunneling protocols, such as IPSec, SSL/TLS, and SSH, which effectively `wrap` network traffic in a ciphertext mantle, thus potentially rendering passive monitoring ineffectual. Whether encryption makes VPN tunnel endpoints immune to fingerprinting, or yields the encrypted contents of the VPN tunnel entirely indistinguishable, is a topic that has received modest coverage in academic literature. This study addresses these question by ta

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Heller, Mark D. "Behavioral analysis of network flow traffic." Thesis, Monterey, California. Naval Postgraduate School, 2010. http://hdl.handle.net/10945/5108.

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Approved for public release, distribution unlimited<br>Network Behavior Analysis (NBA) is a technique to enhance network security by passively monitoring aggregate traffic patterns and noting unusual action or departures from normal operations. The analysis is typically performed offline, due to the huge volume of input data, in contrast to conventional intrusion prevention solutions based on deep packet inspection, signature detection, and real-time blocking. After establishing a benchmark for normal traffic, an NBA program monitors network activity and flags unknown, new, or unusual patt

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McClenney, Walter O. "Analysis of the DES, LOKI, and IDEA algorithms for use in an encrypted voice PC network." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1995. http://handle.dtic.mil/100.2/ADA297919.

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Kattadige, Chamara Manoj Madarasinghe. "Network and Content Intelligence for 360 Degree Video Streaming Optimization." Thesis, The University of Sydney, 2023. https://hdl.handle.net/2123/29904.

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In recent years, 360° videos, a.k.a. spherical frames, became popular among users creating an immersive streaming experience. Along with the advances in smart- phones and Head Mounted Devices (HMD) technology, many content providers have facilitated to host and stream 360° videos in both on-demand and live stream- ing modes. Therefore, many different applications have already arisen leveraging these immersive videos, especially to give viewers an impression of presence in a digital environment. For example, with 360° videos, now it is possible to connect people in a remote meeting in an

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Dandachi, Najib H. "Network flow method for power system analysis." Thesis, Imperial College London, 1989. http://hdl.handle.net/10044/1/47398.

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Martin, Kevin M. "A geographic and functional network flow analysis tool." Thesis, Monterey, California: Naval Postgraduate School, 2014. http://hdl.handle.net/10945/42679.

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Approved for public release; distribution is unlimited<br>Critical infrastructure systems, such as water and electricity, are important for society and national defense. There is a need for network analysis tools that allow analysts to study potential scenarios to discover vulnerabilities, assess consequences, and evaluate effective solutions to overcome network weaknesses. In order to be useful, models of critical infrastructure systems need to be realistic, both geospatially and functionally. The objective of this thesis is to bridge the gap between geospatial and functional network analysis

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Zickel, Michael J. "Using ecosystem network analysis to quantify fluid flow." College Park, Md. : University of Maryland, 2005. http://hdl.handle.net/1903/2987.

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Thesis (M.S.) -- University of Maryland, College Park, 2005.<br>Thesis research directed by: Marine, Estuarine, Environmental Sciences Graduate Program. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.

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Herbert, Alan. "Bolvedere: a scalable network flow threat analysis system." Thesis, Rhodes University, 2019. http://hdl.handle.net/10962/71557.

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Since the advent of the Internet, and its public availability in the late 90’s, there have been significant advancements to network technologies and thus a significant increase of the bandwidth available to network users, both human and automated. Although this growth is of great value to network users, it has led to an increase in malicious network-based activities and it is theorized that, as more services become available on the Internet, the volume of such activities will continue to grow. Because of this, there is a need to monitor, comprehend, discern, understand and (where needed) respo

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Glockner, Gregory D. "Dynamic network flow with uncertain arc capacities." Diss., Georgia Institute of Technology, 1997. http://hdl.handle.net/1853/30734.

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Libri sul tema "Analysis of encrypted network flow"

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

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Jensen, Paul A. Network flow programming. R.E. Krieger Pub. Co., 1987.

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Williams-Sether, Tara. Analysis of the peak-flow gaging network in North Dakota. U.S. Dept. of the Interior, U.S. Geological Survey, 1996.

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Williams-Sether, Tara. Analysis of the peak-flow gaging network in North Dakota. U.S. Dept. of the Interior, U.S. Geological Survey, 1996.

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Williams-Sether, Tara. Analysis of the peak-flow gaging network in North Dakota. U.S. Dept. of the Interior, U.S. Geological Survey, 1996.

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Tara, Williams-Sether. Analysis of the peak-flow gaging network in North Dakota. U.S. Dept. of the Interior, U.S. Geological Survey, 1996.

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Tara, Williams-Sether. Analysis of the peak-flow gaging network in North Dakota. U.S. Dept. of the Interior, U.S. Geological Survey, 1996.

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Williams-Sether, Tara. Analysis of the peak-flow gaging network in North Dakota. U.S. Dept. of the Interior, U.S. Geological Survey, 1996.

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Tara, Williams-Sether. Analysis of the peak-flow gaging network in North Dakota. U.S. Dept. of the Interior, U.S. Geological Survey, 1996.

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Tara, Williams-Sether. Analysis of the peak-flow gaging network in North Dakota. U.S. Dept. of the Interior, U.S. Geological Survey, 1996.

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Capitoli di libri sul tema "Analysis of encrypted network flow"

Cherukuri, Aswani Kumar, Sumaiya Thaseen Ikram, Gang Li, and Xiao Liu. "Encrypted Network Traffic Analysis." In Encrypted Network Traffic Analysis. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-62909-9_2.

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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. "Classification of Encrypted Network Traffic." In Encrypted Network Traffic Analysis. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-62909-9_3.

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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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Cherukuri, Aswani Kumar, Sumaiya Thaseen Ikram, Gang Li, and Xiao Liu. "Introduction." In Encrypted Network Traffic Analysis. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-62909-9_1.

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Rennels, Donald C., and Hobart M. Hudson. "Network Analysis." In Pipe Flow. John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118275276.ch5.

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Tian, Yu-Chu, and Jing Gao. "Traffic Flow Analysis." In Network Analysis and Architecture. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-5648-7_4.

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Hublikar, Shivaraj, and N. Shekar V. Shet. "Hybrid Malicious Encrypted Network Traffic Flow Detection Model." In Computer Networks and Inventive Communication Technologies. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3035-5_28.

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Gonen, Serkan, Gokce Karacayilmaz, Harun Artuner, Mehmet Ali Bariskan, and Ercan Nurcan Yilmaz. "Cyber Attack Detection with Encrypted Network Connection Analysis." In Lecture Notes in Mechanical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-6062-0_57.

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Kolaczyk, Eric D., and Gábor Csárdi. "Analysis of Network Flow Data." In Use R! Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44129-6_9.

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Atti di convegni sul tema "Analysis of encrypted network flow"

Weber, Julia, Srivathsan Thirumurugan, Riyad Alshammari, et al. "Encrypted Network Traffic Analysis (ENTA) Platform: IMA VoIP Traffic Identification." In NOMS 2025-2025 IEEE Network Operations and Management Symposium. IEEE, 2025. https://doi.org/10.1109/noms57970.2025.11073738.

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Francesco Gentile, Antonio, Emilio Greco, and Domenico Luca Carnì. "A Real Network Performance Analysis Testbed for Encrypted MQTT in DMS." In 2024 IEEE International Workshop on Metrology for Living Environment (MetroLivEnv). IEEE, 2024. http://dx.doi.org/10.1109/metrolivenv60384.2024.10615766.

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Khan Tanolli, Nabeel Ahmed, Ahmed Raza, and Asif Gulraiz. "Graph Neural Network Analysis Techniques for Power Flow Analysis." In 2025 International Conference on Emerging Technologies in Electronics, Computing, and Communication (ICETECC). IEEE, 2025. https://doi.org/10.1109/icetecc65365.2025.11070259.

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Prashal, Garima, Parasuraman Sumathi, and Narayana Prasad Padhy. "Interpretable Deep Bayesian Neural Network for Probabilistic Power Flow Analysis." In 2024 IEEE Power & Energy Society General Meeting (PESGM). IEEE, 2024. http://dx.doi.org/10.1109/pesgm51994.2024.10689085.

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Jang, Junseo, and Jaehyun Hwang. "Impact Analysis of NUMA-Aware Flow Placement Based on Network Flow Size in Multicore Systems." In 2024 15th International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 2024. https://doi.org/10.1109/ictc62082.2024.10826670.

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Kim, Dongeon, Jihun Han, Jinwoo Lee, Heejun Roh, and Wonjun Lee. "Poster: Feasibility of Malware Traffic Analysis through TLS-Encrypted Flow Visualization." In 2020 IEEE 28th International Conference on Network Protocols (ICNP). IEEE, 2020. http://dx.doi.org/10.1109/icnp49622.2020.9259387.

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Fu, Chuanpu, Qi Li, and Ke Xu. "Detecting Unknown Encrypted Malicious Traffic in Real Time via Flow Interaction Graph Analysis." In Network and Distributed System Security Symposium. Internet Society, 2023. http://dx.doi.org/10.14722/ndss.2023.23080.

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Shahbar, Khalid, and A. Nur Zincir-Heywood. "How far can we push flow analysis to identify encrypted anonymity network traffic?" In NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium. IEEE, 2018. http://dx.doi.org/10.1109/noms.2018.8406156.

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Talkington, Josh, Ram Dantu, and Kirill Morozov. "Verifying OAuth Implementations Through Encrypted Network Analysis." In SACMAT '19: The 24th ACM Symposium on Access Control Models and Technologies. ACM, 2019. http://dx.doi.org/10.1145/3322431.3326449.

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Liu, Chang, Longtao He, Gang Xiong, Zigang Cao, and Zhen Li. "FS-Net: A Flow Sequence Network For Encrypted Traffic Classification." In IEEE INFOCOM 2019 - IEEE Conference on Computer Communications. IEEE, 2019. http://dx.doi.org/10.1109/infocom.2019.8737507.

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Rapporti di organizzazioni sul tema "Analysis of encrypted network flow"

Bethel, E. Wes. Query-Driven Network Flow Data Analysis and Visualization. Office of Scientific and Technical Information (OSTI), 2006. http://dx.doi.org/10.2172/888963.

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Browning, D. W., and J. B. Thomas. A Numerical Analysis of a Queue with Network Access Flow Control,. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada157526.

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Bonnett, Michaela, Chimdi Ezeigwe, Meaghan Kennedy, and Teri Garstka. Using Social Network Analysis to Link Community Health and Network Strength. Orange Sparkle Ball, 2023. http://dx.doi.org/10.61152/scsf6662.

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Social network analysis (SNA) is a technique used to analyze social networks, whether it be composed of people, organizations, physical locations, or objects. It is being increasingly applied across a variety of sectors to gain insight into patterns of behavior and connectivity, the flow of information and behaviors, and to track and predict the effectiveness of interventions or programs. A key area associated with network strength using SNA is the health and wellness of individuals and communities. Both network strength and health and wellness are measured in many ways, which can obfuscate th

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Patel, Reena. Complex network analysis for early detection of failure mechanisms in resilient bio-structures. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41042.

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Bio-structures owe their remarkable mechanical properties to their hierarchical geometrical arrangement as well as heterogeneous material properties. This dissertation presents an integrated, interdisciplinary approach that employs computational mechanics combined with flow network analysis to gain fundamental insights into the failure mechanisms of high performance, light-weight, structured composites by examining the stress flow patterns formed in the nascent stages of loading for the rostrum of the paddlefish. The data required for the flow network analysis was generated from the finite ele

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Zhu, Zhihong, Yue Zhuo, Haitao Jin, Boyu Wu, and Zhijie Li. Chinese Medicine Therapies for Neurogenic Bladder after Spinal Cord Injury: A protocol for systematic review and network meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2021. http://dx.doi.org/10.37766/inplasy2021.8.0084.

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Neurogenic bladder (NB), a refractory disease, is characterized by voiding dysfunction of bladder and/or urethra, and spinal cord injury (SCI) is a common cause. Chinese medicine therapies have been applied extensively in the treatment of neurogenic bladder, especially in China, and the results are promising but varying. Thus, the aim of this work is to assess the efficacy and safety of various Chinese medicine therapies for neurogenic bladder after spinal cord injury. Condition being studied: Chinese medicine therapies; Neurogenic bladder after spinal cord injury. Main outcome(s): The primary

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Loso, Michael, and Michael Loso. Glacier monitoring in the Central Alaska Network: Protocol narrative, version 1.0. National Park Service, 2025. https://doi.org/10.36967/2308209.

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Two of the three national parks that comprise the National Park Service Inventory & Monitoring Program?s Central Alaska Network (CAKN) are covered by large quantities of glacier ice. Wrangell-St. Elias National Park and Preserve has 3,121 glaciers and Denali National Park and Preserve has 881. These glaciers cover significant portions of the parks and exert substantial influence over the surrounding landscapes with impacts on weather, vegetation, animal migration, river flow, aquatic life, and even downstream marine ecosystems. They also provide a significant attraction?and hazard?for visi

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Weissinger, Rebecca, Mary Moran, Steve Monroe, and Helen Thomas. Springs and seeps monitoring protocol for park units in the Northern Colorado Plateau Network, Version 1.1. National Park Service, 2023. http://dx.doi.org/10.36967/2299467.

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Because of the scarcity of water on the Colorado Plateau and the disproportionately high use by flora and fauna, springs and seeps were quickly identified as an ecosystem of concern for the NCPN. Following the determination of network-wide vital signs, parks were asked to select their top priorities for monitoring. Four parks have implemented springs and seeps monitoring: Arches and Canyonlands national parks, and Hovenweep and Natural Bridges national monuments. This monitoring protocol consists of a protocol narrative and 11 standard operating procedures (SOPs) for monitoring springs, seeps,

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Candelaria, Christopher, Sergey Borisov, Galina Hale, and Julián Caballero. Bank Linkages and International Trade. Inter-American Development Bank, 2013. http://dx.doi.org/10.18235/0011522.

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This paper shows that bank linkages have a positive effect on international trade. A global banking network (GBN) is constructed at the bank level, using individual syndicated loan data from Loan Analytics for 1990-2007. Network distance between bank pairs is computed and aggregated to country pairs as a measure of bank linkages between countries. Data on bilateral trade from IMF DOTS are used as the subject of the analysis and data on bilateral bank lending from BIS locational data are used to control for financial integration and financial flows. Using a gravity approach to modeling trade wi

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Russo, David, Daniel M. Tartakovsky, and Shlomo P. Neuman. Development of Predictive Tools for Contaminant Transport through Variably-Saturated Heterogeneous Composite Porous Formations. United States Department of Agriculture, 2012. http://dx.doi.org/10.32747/2012.7592658.bard.

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The vadose (unsaturated) zone forms a major hydrologic link between the ground surface and underlying aquifers. To understand properly its role in protecting groundwater from near surface sources of contamination, one must be able to analyze quantitatively water flow and contaminant transport in variably saturated subsurface environments that are highly heterogeneous, often consisting of multiple geologic units and/or high and/or low permeability inclusions. The specific objectives of this research were: (i) to develop efficient and accurate tools for probabilistic delineation of dominant geol

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Siebenaler. L52272 Detection of Small Leaks in Liquid Pipelines - Gap Study of Available Methods. Pipeline Research Council International, Inc. (PRCI), 2007. http://dx.doi.org/10.55274/r0010662.

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The integrity of the liquid pipeline network in the United States is of concern to the pipeline operators, the surrounding communities, and the government. During a recent Office of Pipeline Safety industry forum, concerns regarding the detection of small liquid leaks were discussed. The important topic areas related to small leak detection included identifying the available real-time monitoring and detection systems, assessing the need for new detection technologies, and understanding the performance of currently available systems. The objective of the project was to assess the gap between wh

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