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Статті в журналах з теми "Encrypted domain traffic classification"
Akbari, Iman, Mohammad A. Salahuddin, Leni Ven, Noura Limam, Raouf Boutaba, Bertrand Mathieu, Stephanie Moteau, and Stephane Tuffin. "Traffic classification in an increasingly encrypted web." Communications of the ACM 65, no. 10 (October 2022): 75–83. http://dx.doi.org/10.1145/3559439.
Повний текст джерелаAkbari, Iman, Mohammad A. Salahuddin, Leni Ven, Noura Limam, Raouf Boutaba, Bertrand Mathieu, Stephanie Moteau, and Stephane Tuffin. "A Look Behind the Curtain: Traffic Classification in an Increasingly Encrypted Web." ACM SIGMETRICS Performance Evaluation Review 49, no. 1 (June 22, 2022): 23–24. http://dx.doi.org/10.1145/3543516.3453921.
Повний текст джерелаAkbari, Iman, Mohammad A. Salahuddin, Leni Ven, Noura Limam, Raouf Boutaba, Bertrand Mathieu, Stephanie Moteau, and Stephane Tuffin. "A Look Behind the Curtain: Traffic Classification in an Increasingly Encrypted Web." Proceedings of the ACM on Measurement and Analysis of Computing Systems 5, no. 1 (February 18, 2021): 1–26. http://dx.doi.org/10.1145/3447382.
Повний текст джерелаIliyasu, Auwal Sani, Ibrahim Abba, Badariyya Sani Iliyasu, and Abubakar Sadiq Muhammad. "A Review of Deep Learning Techniques for Encrypted Traffic Classification." Computational Intelligence and Machine Learning 3, no. 2 (October 14, 2022): 15–21. http://dx.doi.org/10.36647/ciml/03.02.a003.
Повний текст джерелаBakhshi, Taimur, and Bogdan Ghita. "Anomaly Detection in Encrypted Internet Traffic Using Hybrid Deep Learning." Security and Communication Networks 2021 (September 21, 2021): 1–16. http://dx.doi.org/10.1155/2021/5363750.
Повний текст джерелаDeng, Guoqiang, Min Tang, Yuhao Zhang, Ying Huang, and Xuefeng Duan. "Privacy-Preserving Outsourced Artificial Neural Network Training for Secure Image Classification." Applied Sciences 12, no. 24 (December 14, 2022): 12873. http://dx.doi.org/10.3390/app122412873.
Повний текст джерелаMeng, Yitong, and Jinlong Fei. "Hidden Service Website Response Fingerprinting Attacks Based on Response Time Feature." Security and Communication Networks 2020 (November 30, 2020): 1–21. http://dx.doi.org/10.1155/2020/8850472.
Повний текст джерелаHu, 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 (December 9, 2021): 8231. http://dx.doi.org/10.3390/s21248231.
Повний текст джерелаBoldyrikhin, N. V., D. A. Korochentsev, and F. A. Altunin. "CLASSIFICATION FEATURES OF ENCRYPTED NETWORK TRAFFIC." IZVESTIYA SFedU. ENGINEERING SCIENCES, no. 3 (October 19, 2020): 89–98. http://dx.doi.org/10.18522/2311-3103-2020-3-89-98.
Повний текст джерелаLu, Bei, Nurbol Luktarhan, Chao Ding, and Wenhui Zhang. "ICLSTM: Encrypted Traffic Service Identification Based on Inception-LSTM Neural Network." Symmetry 13, no. 6 (June 17, 2021): 1080. http://dx.doi.org/10.3390/sym13061080.
Повний текст джерелаДисертації з теми "Encrypted domain traffic classification"
Areström, Erik. "Flow Classification of Encrypted Traffic Streams using Multi-fractal Features." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-148725.
Повний текст джерелаŠuhaj, Peter. "Rozšíření NetFlow záznamů pro zlepšení možností klasifikace šifrovaného provozu." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2020. http://www.nusl.cz/ntk/nusl-417291.
Повний текст джерелаChebudie, Abiy Biru. "Monitoring of Video Streaming Quality from Encrypted Network Traffic : The Case of YouTube Streaming." Thesis, Blekinge Tekniska Högskola, Institutionen för kommunikationssystem, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-13336.
Повний текст джерелаPacheco, Fannia. "Classification techniques for the management of the ``Quality of Service'' in Satellite Communication systems." Thesis, Pau, 2019. http://www.theses.fr/2019PAUU3026.
Повний текст джерелаThe Internet has become indispensable for the daily activities of human beings. Nowadays, this network system serves as a platform for communication, transaction, and entertainment, among others. This communication system is characterized by terrestrial and Satellite components that interact between themselves to provide transmission paths of information between endpoints. Particularly, Satellite Communication providers’ interest is to improve customer satisfaction by optimally exploiting on demand available resources and offering Quality of Service (QoS). Improving the QoS implies to reduce errors linked to information loss and delays of Internet packets in Satellite Communications. In this sense, according to Internet traffic (Streaming, VoIP, Browsing, etc.) and those error conditions, the Internet flows can be classified into different sensitive and non-sensitive classes. Following this idea, this thesis project aims at finding new Internet traffic classification approaches to improving customer satisfaction by improving the QoS.Machine Learning (ML) algorithms will be studied and deployed to classify Internet traffic. All the necessary elements, to couple an ML solution over a well-known Satellite Communication and QoS management architecture, will be evaluated. In this architecture, one or more monitoring points will intercept Satellite Internet traffic, which in turn will be treated and marked with predefined classes by ML-based classification techniques. The marked traffic will be interpreted by a QoS management architecture that will take actions according to the class type.To develop this ML-based solution, a rich and complete set of Internet traffic is required; however, historical labeled data is hardly publicly available. In this context, binary packets should be monitored and stored to generate historical data. To do so, an emulated cloud platform will serve as a data generation environment in which different Internet communications will be launched and captured. This study is escalated to a Satellite Communication architecture. Moreover, statistical-based features are extracted from the packet flows. Some statistical-based computations will be adapted to achieve accurate Internet traffic classification for encrypted and unencrypted packets in the historical data. Afterward, a proposed classification system will deal with different Internet communications (encrypted, unencrypted, and tunneled). This system will process the incoming traffic hierarchically to achieve a high classification performance. Besides, to cope with the evolution of Internet applications, a new method is presented to induce updates over the original classification system. Finally, some experiments in the cloud emulated platform validate our proposal and set guidelines for its deployment over a Satellite architecture
Chou, Chi-Bin, and 周祺彬. "Tunnel Sniper: P2P Traffic Classification in Encrypted Tunnels." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/76888851513433662628.
Повний текст джерела逢甲大學
資訊工程學系
101
In recent years, users have begun using encrypted tunnels to transport data in order to protect transmitted messages. However, it is very difficult for network engineers to manage the quality of network traffic in encrypted tunnels. Therefore, the issue of how to classify encrypted tunnels becomes more important and it has been studied so far. However, these studies are usually based on the assumption that encrypted tunnels include only one application traffic. In fact, encrypted tunnels may include more than one kind of application traffic. Therefore, this paper proposes a solution to identify whether encrypted tunnels include one specific P2P application traffic or more. In addition, our proposed system can be trained by plain-text traffic. In the experimental results, the system can accurately classify encrypted tunnels when they include more than one kinds of application traffic.
Qian, Cheng. "Classification of encrypted cloud computing service traffic using data mining techniques." Thesis, 2011. http://hdl.handle.net/2152/ETD-UT-2011-12-4836.
Повний текст джерелаtext
Cheng, Yi-Chi, and 鄭伊騏. "Implicit Classification and Bandwidth Management for Encrypted Internet Voice Traffic: Case study of Skype." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/66894822755056826014.
Повний текст джерела國立中正大學
通訊工程研究所
94
With the rapid growth of Internet, various applications and information has developed dramatically in the last couple of years. VoIP, being a significant portion of the network traffic today, constitutes a highly desirable class for identification. Accurate classification of proprietary VoIP traffic is a challenging problem, and becomes even more challenging when we are constrained to use only transport-layer header information and encrypted packets. In this paper, we present a new approach for proprietary VoIP traffic identification that uses fundamental characteristics of proprietary VoIP protocols, such as constant bandwidth consumption and frequent sending rate. We do not use any application specific information and still could identify proprietary VoIP protocols in a simple and efficient way. A bandwidth management system is also built to handle the traffic and guarantee the QoS in bandwidth limited network environment. Finally, this mechanism is implemented and evaluated on a network processor board. Based on the network traffic of department electrical engineering of Chung Cheng University, the evolution result shows our system can recognize 90% Skype sessions. It could be transplanted to identify other encrypted Internet voice traffic easily.
Carvalho, José Miguel Mateus Maurício. "Traffic Surveillance using Visual Domains Adaptation." Master's thesis, 2017. http://hdl.handle.net/10316/82950.
Повний текст джерелаOver the past decade, traffic surveillance systems development have attracted the interest of many in the computer vision community. Mainly due to possible improvement of drivers security such as implementations in systems capable of predict real-time accidents, detection of infractions on roads or even time and fuel reduction by selecting the right way of traveling. The use of computer vision techniques to monitoring traffic as proven to be a non-invasive, cost effective, automated option when it comes to traffic surveillance. The challenge today is to efficiently develop an Intelligent Transportation System capable of real-time detecting roads with high affluence of traffic and for example, sending that information so that drivers can choose another way in advance, make an efficient and autonomous management of traffic lights, or in extreme scenarios, like a car accident, a system that automatically notifies authorities to provide quicker medical assistance.\\The purpose of this work is to implement some visual domain adaptation based approaches when it comes to identify the existence or not of a vehicle in an intersection. To accomplish the purpose of adapting dynamic events on traffic surveillance, or similar tasks, we conducted along this thesis several approaches with holistic classification exploring domain adaptation of evolutionary events to some GIST features extracted from the dataset images and also apply the same approaches on AlexNet neural networks features of the same dataset images. This approaches are being implemented in order to be used on situations where a dynamic evolution of domains is needed and where we have an unlabeled target data.
Durante a última decada, o desenvolvimento de sistemas direcionados para controlo e manutenção de trafego tem desplotado imenso interesse na comunidade de visão por computadores. Isto deve-se muito ao facto do grande número de oportunidades no melhoramento de técnicas para segurança dos condutores, como por exemplo, predição em tempo real de acidentes, deteção de comportamentos ilegais nas estradas ou até aplicar estas técnicas a aplicações que permitam poupar tempo e combustível ao escolher o melhor caminho. A utilização destas técnicas para monitorização de trafego tem provado ser uma opção não invasiva, barata e autonoma. Hoje em dia é bastante desejado um sistema inteligente capaz de monitorizar em tempo real densidade de tráfego para que com antecedência se possam calcular novas rotas para que condutores evitem tráfego indesejado, outra aplicação será a gestão automática de semáforos, ou até em casos mais extremos, fazer a predição de acidentes e em caso de acidente notifique as autoridades para que possam as pessoas envolvidas possam receber cuidados médicos o mais rápido possível.\\Este trabalho tem como propósito a implementação de abordagens de adaptação de domínios visuais para a detecção de veículos em imagens na aproximação de um cruzamento. Para atingir os nossos objectivo de adaptar dinamicamente eventos relacionados com monitorização de trafego, implementámos algumas abordagens baseadas em classificação holística para explicar a adaptação evolutiva de domínios, inicialmente aplicadas a caracteristicas GIST extraídas das imagens incluídas no dataset utilizado. Posteriormente aplicamos as mesmas abordagens a características extraídas com a ajuda da rede neuronal AlexNet. Estas abordagens que estamos a implementar pretendem ser aplicadas em situações onde se seja necessária uma evolução dinâmica de domínios e onde temos dados sem labels no treino.
Friedrich, Maik. "Designing a workplace in the aviation domain: The transition to a remote air traffic control workplace by analysing the human-computer interaction." 2018. https://monarch.qucosa.de/id/qucosa%3A34160.
Повний текст джерелаThe efficient usage of all available resources is a central interest of our time. In air traffic management, the topic of remote tower operations has increased in importance over the last 10 years. Herein, the design of a remote tower workplace plays a key role in the successful implementation of remote tower operations. Less dependency on building and maintaining airport control towers, an improved human research planning (especially for small airports) and an increase in available information to the conventional tower workplace are central advantages of remote tower operations. However, a potential challenge for this approach is an HCI model that supports the transition by describing the influence on the operator task. This dissertation focuses on the application and improvement of an HCI approach to redesign a workplace by changing the interface without influencing the task of the operator. The presented model focuses on the flow of information rather than the presentation of technical possibilities. It consists of three parts that each individually measure and analyse the influence that a redesigned interface has on the flow in information. This model is specified and applied to remote tower operations. Prior to this dissertation, there were only a few publications connected to the strategies that air traffic control officers (ATCO) in the tower use to control traffic and virtually no publications connected to the practical implications for working at a remote tower workplace. Therefore, the goal was to provide a well-founded contribution to the development of psychology in the area of human-computer interaction by applying the psychological theories and extension of the methodology. The main difference between the conventional and the remote tower workplaces is the replacement of out-the-window view and binoculars by camera systems. Based on what influences the human performance in connection with new systems developed in air traffic control, most changes afflict the general workstation and equipment, the user interface, and human resource management. The challenge for the factor workstation and equipment is to identify the information decrease at the remote tower workplace and its replacement with additional information whilst simultaneously ensuring that this information can be tested in a standardised manor throughout a variety of research projects and several different prototypes. The challenge for the factor user interface was the analysis of the dynamic information presented at the remote tower workplace. The challenge for the human resource management is to identify how workload and situation awareness influence performance. In sum, all challenges are analysed in detail. For the factor workstation and equipment, two analyses showed a large variety of indicators that are applicable to evaluate the difference in the flow of information between the conventional and the remote tower workplace. The first analysis of the windsock indicator provided an example of how the different metrics can be applied. The second analysis showed that the weather remote tower metrics extend the existing remote tower metrics and thereby complete the aspects of the monitoring that an ATCO has to perform. For the factor user interface an advanced gaze analysis, called Integration Guideline for Dynamic Areas of Interest (IGDAI) was developed. This allows for a detailed analysis of the dynamic information presented at the remote tower workplace. For the factor human resource management, a detailed analysis shows how situation awareness and workload influence performance within low and high task load phases. By applying IGDAI, the existence of two control strategies for the Air Traffic Control (ATC) environment that are each related to the task load phases could be identified as well as the extent to which these might afflict remote tower operations. The provided model of redesigning only the interface presents a detailed approach for a special case in HCI. The transition from the conventional to the remote tower operations is an ongoing process that will be continued. The development in the domain of remote tower operations seems to be stable and necessary to keep up with the challenges of future air traffic management. Therefore, the analysed constructs, developed methodologies and presented results from this dissertation provide a seminal basis for the necessary future research.:Table of Contents I Synopsis 1 1 Introduction 2 2 Research framework and goals 4 2.1 Human-computer interaction 4 2.2 Remote Tower Operations 4 2.3 Remote Tower Research 6 2.4 Embedding into HCI 7 2.5 Research goals of the dissertation 8 3 The development of a new workplace 9 3.1 Redesign of a workplace 9 3.2 Design factors in Aviation 10 3.3 Remote Tower Metrics 11 3.4 Dynamic Areas of Interests 11 3.5 Adaptation and strategy shifts 11 4 Methodological aspects of the dissertation 13 4.1 Identify and evaluate remote tower metrics 13 4.2 Evaluate dynamic areas of interest 13 4.3 Measuring Situation Awareness 14 5 Discussion and implications 16 5.1 Summarising the findings 16 5.2 Theoretical implications 17 5.3 Implications for the application 19 5.4 Critical reflection of the methodology 20 5.5 Revenue for psychological research 22 6 Literature 24 II Article 1: How to Evaluate Remote Tower Metrics in Connection to Weather Observations. An Extension of the Existing Metrics 28 III Article 2: A Guideline for Integrating Dynamic Areas of Interests in Existing Set-up for Capturing Eye Movement: Looking at Moving Aircraft 53 IV Article 3: The Influence of Task Load on Situation Awareness and Control Strategy in the ATC Tower Environment. 84 V Contributions to conferences 116 VI Curriculum vitae and publications 117
Частини книг з теми "Encrypted domain traffic classification"
Ma, Yuxiang, Yulei Wu, and Jingguo Ge. "Encrypted Traffic Classification." In Accountability and Privacy in Network Security, 27–39. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6575-5_3.
Повний текст джерелаBar - Yanai, Roni, Michael Langberg, David Peleg, and Liam Roditty. "Realtime Classification for Encrypted Traffic." In Experimental Algorithms, 373–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13193-6_32.
Повний текст джерелаCao, Zigang, Gang Xiong, Yong Zhao, Zhenzhen Li, and Li Guo. "A Survey on Encrypted Traffic Classification." In Applications and Techniques in Information Security, 73–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-45670-5_8.
Повний текст джерелаMo, Shuang, Yifei Wang, Ding Xiao, Wenrui Wu, Shaohua Fan, and Chuan Shi. "Encrypted Traffic Classification Using Graph Convolutional Networks." In Advanced Data Mining and Applications, 207–19. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65390-3_17.
Повний текст джерелаCao, Zigang, Shoufeng Cao, Gang Xiong, and Li Guo. "Progress in Study of Encrypted Traffic Classification." In Trustworthy Computing and Services, 78–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35795-4_10.
Повний текст джерелаWang, Yu, Chencheng Wang, Gang Xiong, and Zhen Li. "Multi-scene Classification of Blockchain Encrypted Traffic." In Communications in Computer and Information Science, 329–37. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-7993-3_25.
Повний текст джерелаSeyed Tabatabaei, Talieh, Mostafa Adel, Fakhri Karray, and Mohamed Kamel. "Machine Learning-Based Classification of Encrypted Internet Traffic." In Machine Learning and Data Mining in Pattern Recognition, 578–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31537-4_45.
Повний текст джерелаGupta, Neha, Vinita Jindal, and Punam Bedi. "Encrypted Traffic Classification Using eXtreme Gradient Boosting Algorithm." In Advances in Intelligent Systems and Computing, 225–32. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3071-2_20.
Повний текст джерелаMori, Tatsuya, Takeru Inoue, Akihiro Shimoda, Kazumichi Sato, Keisuke Ishibashi, and Shigeki Goto. "SFMap: Inferring Services over Encrypted Web Flows Using Dynamical Domain Name Graphs." In Traffic Monitoring and Analysis, 126–39. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-17172-2_9.
Повний текст джерелаZhang, Meng, Hongli Zhang, Bo Zhang, and Gang Lu. "Encrypted Traffic Classification Based on an Improved Clustering Algorithm." In Trustworthy Computing and Services, 124–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35795-4_16.
Повний текст джерелаТези доповідей конференцій з теми "Encrypted domain traffic classification"
Balhwan, Suman, Noble Kumari, and A. K. Mohapatra. "Encrypted Web Traffic Classification." In 2018 3rd International Conference on Contemporary Computing and Informatics (IC3I). IEEE, 2018. http://dx.doi.org/10.1109/ic3i44769.2018.9007264.
Повний текст джерелаMa, Qianli, Wei Huang, Yanliang Jin, and Jianhua Mao. "Encrypted Traffic Classification Based on Traffic Reconstruction." In 2021 4th International Conference on Artificial Intelligence and Big Data (ICAIBD). IEEE, 2021. http://dx.doi.org/10.1109/icaibd51990.2021.9459072.
Повний текст джерелаTzong-Jye Liu, Chi-Bin Chou, and Chuan-Mu Tseng. "P2P traffic classification in encrypted tunnels." In 2013 19th Asia-Pacific Conference on Communications (APCC). IEEE, 2013. http://dx.doi.org/10.1109/apcc.2013.6766018.
Повний текст джерелаBader, Ofek, Adi Lichy, Chen Hajaj, Ran Dubin, and Amit Dvir. "MalDIST: From Encrypted Traffic Classification to Malware Traffic Detection and Classification." In 2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC). IEEE, 2022. http://dx.doi.org/10.1109/ccnc49033.2022.9700625.
Повний текст джерелаPradhan, Ayush, Sidharth Behera, and Ratnakar Dash. "Hybrid RBFN Based Encrypted SSH Traffic Classification." In 2018 5th International Conference on Signal Processing and Integrated Networks (SPIN). IEEE, 2018. http://dx.doi.org/10.1109/spin.2018.8474059.
Повний текст джерелаAnderson, Blake, and David McGrew. "Machine Learning for Encrypted Malware Traffic Classification." In KDD '17: The 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3097983.3098163.
Повний текст джерелаChen, Yige, Tianning Zang, Yongzheng Zhang, Yuan Zhou, Linshu Ouyang, and Peng Yang. "Incremental Learning for Mobile Encrypted Traffic Classification." In ICC 2021 - IEEE International Conference on Communications. IEEE, 2021. http://dx.doi.org/10.1109/icc42927.2021.9500619.
Повний текст джерелаAceto, Giuseppe, Domenico Ciuonzo, Antonio Montieri, and Antonio Pescape. "Mobile Encrypted Traffic Classification Using Deep Learning." In 2018 Network Traffic Measurement and Analysis Conference (TMA). IEEE, 2018. http://dx.doi.org/10.23919/tma.2018.8506558.
Повний текст джерелаAlshammari, Riyad, and A. Nur Zincir-Heywood. "Investigating Two Different Approaches for Encrypted Traffic Classification." In 2008 Sixth Annual Conference on Privacy, Security and Trust (PST). IEEE, 2008. http://dx.doi.org/10.1109/pst.2008.15.
Повний текст джерелаChoorod, Pitpimon, and George Weir. "Tor Traffic Classification Based on Encrypted Payload Characteristics." In 2021 National Computing Colleges Conference (NCCC). IEEE, 2021. http://dx.doi.org/10.1109/nccc49330.2021.9428874.
Повний текст джерела