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Добірка наукової літератури з теми "Analysis of encrypted network flow"

Автор: Grafiati

Опубліковано: 16 листопада 2024

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Статті в журналах з теми "Analysis of encrypted network flow"

Yan, Xiaodan. "Deep Learning-Based Efficient Analysis for Encrypted Traffic." Applied Sciences 13, no. 21 (October 27, 2023): 11776. http://dx.doi.org/10.3390/app132111776.

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Анотація:

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 temporal features from encrypted traffic, enabling accurate identification of traffic types. Through rigorous testing and evaluation, our system has achieved an impressive accuracy rate of 96.4% on the widely used ISCXVPN2016 dataset. This achievement demonstrates the effectiveness and reliability of our method in accurately classifying encrypted network traffic. By addressing the challenges posed by encrypted traffic identification, our research contributes to enhancing network security and privacy protection.

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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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Анотація:

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 arrival timestamps and packet sizes. By employing this data processing approach and utilizing deep recognition algorithms, the study conducts service analysis on network traffic. Experimental results demonstrate that the bidirectional flow-based image representation method achieves a 90.9% accuracy rate for the traffic analysis task on a TOR-encrypted imbalanced dataset, surpassing the accuracy of the unidirectional flow image method. Furthermore, the method also shows improvements in operation speed, enabling online network traffic detection.

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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 (February 13, 2020): 324. http://dx.doi.org/10.3390/electronics9020324.

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Анотація:

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 importance of different traffic features varies, and traditional KNN does not highlight the importance of different features, this study introduces the concept of feature weight and proposes the weighted feature KNN (WKNN) algorithm. Furthermore, to obtain the optimal feature set and the corresponding feature weight set, a feature selection and feature weight self-adaptive algorithm for WKNN is proposed. In addition, a three-layer classification framework for encrypted network flows is established. Based on the improved KNN and the framework, this study finally presents a method for fine-grained classification of encrypted network flows, which can identify the encryption status, application type and content type of encrypted network flows with high accuracies of 99.3%, 92.4%, and 97.0%, respectively.

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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 (November 9, 2018): 2196. http://dx.doi.org/10.3390/app8112196.

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Анотація:

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 literature for the last few years, and evaluated them with supervised machine learning. For our experiments, we use the UNSW-NB15 dataset, recently published as a new benchmark for network security. Results showed three remarkable findings: (1) Analysis based on source behavior instead of classic flow profiles is more effective for attack detection; (2) meta-studies on past research can be used to establish satisfactory benchmarks; and (3) features based on packet length are clearly determinant for capturing malicious activity. Our research showed that vectors currently used for attack detection are oversized, their accuracy and speed can be improved, and are to be adapted for dealing with encrypted traffic.

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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 (August 24, 2021): 7789. http://dx.doi.org/10.3390/app11177789.

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Анотація:

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 artifacts from the encrypted network traffic of the prominent social messenger app Signal (on Android version 9). The analysis of the installed app was conducted over fully encrypted network traffic. By adopting the proposed strategy, the forensic investigator can easily detect encrypted traffic activities such as chatting, media messages, audio, and video calls by looking at the payload patterns. Furthermore, a detailed analysis of the trace files can help to create a list of chat servers and IP addresses of involved parties in the events. As a result, the proposed strategy significantly facilitates extraction of the app’s behavior from encrypted network traffic which can then be used as supportive evidence for forensic investigation.

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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 (December 24, 2021): 155. http://dx.doi.org/10.3390/app12010155.

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Анотація:

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 detection and family classification. Especially, we have discussed pros and cons of three main deployment models for encrypted NTA: TLS interception, inspection using cryptographic functions, and passive inspection without decryption. In addition, we have discussed the state-of-the-art methods in TLS-encrypted NTA for each component of a machine learning pipeline, typically used in the state-of-the-art methods.

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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 (May 28, 2024): 22. http://dx.doi.org/10.3390/cryptography8020022.

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Анотація:

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 defences try to protect specific side channels by modifying the packet transmission for the flow, e.g., by adding redundant information (padding of packets or use of junk packets) and perturbing packet timings (e.g., artificially delaying packet transmission at the sender). Such defences incur significant overhead and impact application-level performance metrics, such as latency, throughput, end-to-end delay, and jitter. Furthermore, these mechanisms can be complex, often ineffective, and are not general solutions—a new profile must be created for every application, which is an infeasible expectation to place on software developers. We show that an approach exploiting multipath communication can be effective against HMM-based traffic analysis. After presenting the core analytical background, we demonstrate the efficacy of this approach with a number of diverse, simulated traffic flows. Based on the results, we define some simple design rules for software developers to adopt in order to exploit the mechanism we describe, including a critical examination of existing communication protocol behavior.

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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 (December 9, 2021): 8231. http://dx.doi.org/10.3390/s21248231.

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Анотація:

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. The overall structure of CBD can be generally described as a of one-dimension CNN and the encoder of Transformer. The model can be pre-trained with unlabeled data to understand the basic characteristics of encrypted traffic data, and be transferred to other datasets to complete the classification of encrypted traffic from the packet level and the flow level. The performance of the proposed model was evaluated on a public dataset. The results showed that the performance of the CBD model was better than the baseline methods, and the pre-training method can improve the classification ability of the model.

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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 (September 28, 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 deployment of deep learning-based solutions on homomorphically encrypted data, and (iii) a proof-of-concept study for atrial fibrillation (AF) detection from electrocardiograms recorded on a wearable device. In the context of AF detection, two approaches are considered: a multi-layer perceptron (MLP) which receives as input the ECG features computed and encrypted on the wearable device, and an end-to-end deep convolutional neural network (1D-CNN), which receives as input the encrypted raw ECG data. The CNN model achieves a lower mean F1-score than the hand-crafted feature-based model. This illustrates the benefit of hand-crafted features over deep convolutional neural networks, especially in a setting with a small training data. Compared to state-of-the-art results, the two privacy-preserving approaches lead, with reasonable computational overhead, to slightly lower, but still similar results: the small performance drop is caused by limitations related to the use of homomorphically encrypted data instead of plaintext data. The findings highlight the potential of the proposed framework to enhance the functionality of wearables through privacy-preserving AI, by providing, within a reasonable amount of time, results equivalent to those achieved without privacy enhancing mechanisms. While the chosen homomorphic encryption scheme prioritizes performance and utility, certain security shortcomings remain open for future development.

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Choudhary, Swapna, and Sanjay Dorle. "Secured SDN Based Blockchain: An Architecture to Improve the Security of VANET." International journal of electrical and computer engineering systems 13, no. 2 (February 28, 2022): 145–53. http://dx.doi.org/10.32985/ijeces.13.2.7.

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Анотація:

Vehicular Ad-hoc networks (VANETs) during the communication process, nodes are always varying and the process is always under security threats like Sybil attacks, masquerading attacks, etc. In order to reduce the probability of these attacks and to regulate traffic flow in the network, a software-defined network (SDN) is used. The SDN is used for implementing protocols like OpenFlow and reducing the routing load in the network, but it doesn’t provide a high level of security to the network, hence protocols like encryption, hashing, etc. are applied to the VANET. In the paper, SDN based blockchain-inspired algorithm is implemented, which coordinates network traffic and improves the overall security of the network. Security analysis of the proposed algorithm shows that the combination of blockchain with encrypted SDN is removing more than 95% of the network attacks as compared to its non-blockchain counterparts.

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Більше джерел

Дисертації з теми "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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Анотація:

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 les approches statiques de détection d'attaque présentent des limites d'efficacité et précision, de temps d'exécution et de scalabilité. Cette thèse propose des approches dynamiques de détection de cyberattaques liées au trafic réseau, en utilisant une ingénierie d'attributs basée sur les différentes phases de communication d'un flux réseau, couplée aux réseaux de neurones à convolution (1D-CNN) et leur feature detector. Cette double extraction permet ainsi une meilleure classification des flux réseau, une réduction du nombre d'attributs et des temps d'exécution des modèles donc une détection efficace des attaques. Les entreprises sont également confrontées à des cybermenaces qui évoluent constamment, et les attaques "zero-day", exploitant des vulnérabilités encore inconnues, deviennent de plus en plus fréquentes. La détection de ces attaques zero-day implique une veille technologique constante et une analyse minutieuse, mais coûteuse en temps, de l'exploitation de ces failles. Les solutions proposées garantissent pour la plupart la détection de certaines techniques d'attaque. Ainsi, nous proposons un framework de détection de ces attaques qui traite toute la chaîne d'attaque, de la phase de collecte des données à l'identification de tout type de zero-day, même dans un environnement en constante évolution. Enfin, face à l'obsolescence des jeux de données et techniques de génération de données existants pour la détection d'intrusion et à la nature figée, non évolutive, et non exhaustive des scénarios d'attaques récents, l'étude d'un générateur de données de synthèse adapté tout en garantissant la confidentialité des données est abordée. Les solutions proposées dans cette thèse optimisent la détection des techniques d'attaque connues et zero-day sur les flux réseau, améliorent la précision des modèles, tout en garantissant la confidentialité et la haute disponibilité des données et modèles avec une attention particulière sur l'applicabilité des solutions dans un réseau d'entreprise
The increasing complexity of cyberattacks, characterized by a diversification of attack techniques, an expansion of attack surfaces, and growing interconnectivity of applications with the Internet, makes network traffic management in a professional environment imperative. Companies of all types collect and analyze network flows and logs to ensure the security of exchanged data and prevent the compromise of information systems. However, techniques for collecting and processing network traffic data vary from one dataset to another, and static attack detection approaches have limitations in terms of efficiency and precision, execution time, and scalability. This thesis proposes dynamic approaches for detecting cyberattacks related to network traffic, using feature engineering based on the different communication phases of a network flow, coupled with convolutional neural networks (1D-CNN) and their feature detector. This double extraction allows for better classification of network flows, a reduction in the number of attributes and model execution times, and thus effective attack detection. Companies also face constantly evolving cyber threats, and "zero-day" attacks that exploit previously unknown vulnerabilities are becoming increasingly frequent. Detecting these zero-day attacks requires constant technological monitoring and thorough but time-consuming analysis of the exploitation of these vulnerabilities. The proposed solutions guarantee the detection of certain attack techniques. Therefore, we propose a detection framework for these attacks that covers the entire attack chain, from the data collection phase to the identification of any type of zero-day, even in a constantly evolving environment. Finally, given the obsolescence of existing datasets and data generation techniques for intrusion detection, and the fixed, non-evolving, and non-exhaustive nature of recent attack scenarios, the study of an adapted synthetic data generator while ensuring data confidentiality is addressed. The solutions proposed in this thesis optimize the detection of known and zero-day attack techniques on network flows, improve the accuracy of models, while ensuring the confidentiality and high availability of data and models, with particular attention to the applicability of the solutions in a company network

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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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Анотація:

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 targeting two tunnelling protocols: IPSec and SSL/TLS. A new fingerprinting methodology is presented, several fingerprinting discriminants are identified, and test results are set forth, showing that endpoint identities can be uncovered, and that some of the contents of encrypted VPN tunnels can in fact be discerned.
Dissertation (MSc (Computer Science))--University of Pretoria, 2005.
Computer Science
unrestricted

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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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Анотація:

Approved for public release, distribution unlimited
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 patterns that might indicate the presence of a potential threat. NBA also monitors and records trends in bandwidth and protocol use. Computer users in the Department of Defense (DoD) operational networks may use Hypertext Transport Protocol (HTTP) to stream video from multimedia sites like youtube.com, myspace.com, mtv.com, and blackplanet.com. Such streaming may hog bandwidth, a grave concern, given that increasing amounts of operational data are exchanged over the Global Information Grid, and introduce malicious viruses inadvertently. This thesis develops an NBA solution to identify and estimate the bandwidth usage of HTTP streaming video traffic entirely from flow records such as Cisco's NetFlow data.

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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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Анотація:

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 interactive way which essentially increases the productivity of the meeting. Also, creating interactive learning materials using 360° videos for students will help deliver the learning outcomes effectively. However, streaming 360° videos is not an easy task due to several reasons. First, 360° video frames are 4–6 times larger than normal video frames to achieve the same quality as a normal video. Therefore, delivering these videos demands higher bandwidth in the network. Second, processing relatively larger frames requires more computational resources at the end devices, particularly for end user devices with limited resources. This will impact not only the delivery of 360° videos but also many other applications running on shared resources. Third, these videos need to be streamed with very low latency requirements due their interactive nature. Inability to satisfy these requirements can result in poor Quality of Experience (QoE) for the user. For example, insufficient bandwidth incurs frequent rebuffer- ing and poor video quality. Also, inadequate computational capacity can cause faster battery draining and unnecessary heating of the device, causing discomfort to the user. Motion or cyber–sickness to the user will be prevalent if there is an unnecessary delay in streaming. These circumstances will hinder providing im- mersive streaming experiences to the much-needed communities, especially those who do not have enough network resources. To address the above challenges, we believe that enhancements to the three main components in video streaming pipeline, server, network and client, are essential. Starting from network, it is beneficial for network providers to identify 360° video flows as early as possible and understand their behaviour in the network to effec- tively allocate sufficient resources for this video delivery without compromising the quality of other services. Content servers, at one end of this streaming pipeline, re- quire efficient 360° video frame processing mechanisms to support adaptive video streaming mechanisms such as ABR (Adaptive Bit Rate) based streaming, VP aware streaming, a streaming paradigm unique to 360° videos that select only part of the larger video frame that fall within the user-visible region, etc. On the other end, the client can be combined with edge-assisted streaming to deliver 360° video content with reduced latency and higher quality. Following the above optimization strategies, in this thesis, first, we propose a mech- anism named 360NorVic to extract 360° video flows from encrypted video traffic and analyze their traffic characteristics. We propose Machine Learning (ML) mod- els to classify 360° and normal videos under different scenarios such as offline, near real-time, VP-aware streaming and Mobile Network Operator (MNO) level stream- ing. Having extracted 360° video traffic traces both in packet and flow level data at higher accuracy, we analyze and understand the differences between 360° and normal video patterns in the encrypted traffic domain that is beneficial for effec- tive resource optimization for enhancing 360° video delivery. Second, we present a WGAN (Wesserstien Generative Adversarial Network) based data generation mechanism (namely VideoTrain++) to synthesize encrypted network video traffic, taking minimal data. Leveraging synthetic data, we show improved performance in 360° video traffic analysis, especially in ML-based classification in 360NorVic. Thirdly, we propose an effective 360° video frame partitioning mechanism (namely VASTile) at the server side to support VP-aware 360° video streaming with dy- namic tiles (or variable tiles) of different sizes and locations on the frame. VASTile takes a visual attention map on the video frames as the input and applies a com- putational geometric approach to generate a non-overlapping tile configuration to cover the video frames adaptive to the visual attention. We present VASTile as a scalable approach for video frame processing at the servers and a method to re- duce bandwidth consumption in network data transmission. Finally, by applying VASTile to the individual user VP at the client side and utilizing cache storage of Multi Access Edge Computing (MEC) servers, we propose OpCASH, a mech- anism to personalize the 360° video streaming with dynamic tiles with the edge assistance. While proposing an ILP based solution to effectively select cached variable tiles from MEC servers that might not be identical to the requested VP tiles by user, but still effectively cover the same VP region, OpCASH maximize the cache utilization and reduce the number of requests to the content servers in congested core network. With this approach, we demonstrate the gain in latency and bandwidth saving and video quality improvement in personalized 360° video streaming.

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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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Анотація:

Approved for public release; distribution is unlimited
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 by developing a software tool that allows users to create and edit networks in a Graphical Information System (GIS) visual environment, and then also run and view the results of functional network models. Our primary contribution is to provide an easy-to-use, graphical interface in the form of a plugin that allows users, regardless of their network expertise, to create networks and exercise network flow models on them. We demonstrate the usefulness of our plugin through the analysis of a fictional case study with a realistic Internet infrastructure. We run several minimum cost flow models with simulated network attacks to assess the robustness of the network.

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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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Анотація:

Thesis (M.S.) -- University of Maryland, College Park, 2005.
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) respond to events on networks worldwide. Although this line of thought is simple in its reasoning, undertaking such a task is no small feat. Full packet analysis is a method of network surveillance that seeks out specific characteristics within network traffic that may tell of malicious activity or anomalies in regular network usage. It is carried out within firewalls and implemented through packet classification. In the context of the networks that make up the Internet, this form of packet analysis has become infeasible, as the volume of traffic introduced onto these networks every day is so large that there are simply not enough processing resources to perform such a task on every packet in real time. One could combat this problem by performing post-incident forensics; archiving packets and processing them later. However, as one cannot process all incoming packets, the archive will eventually run out of space. Full packet analysis is also hindered by the fact that some existing, commonly-used solutions are designed around a single host and single thread of execution, an outdated approach that is far slower than necessary on current computing technology. This research explores the conceptual design and implementation of a scalable network traffic analysis system named Bolvedere. Analysis performed by Bolvedere simply asks whether the existence of a connection, coupled with its associated metadata, is enough to conclude something meaningful about that connection. This idea draws away from the traditional processing of every single byte in every single packet monitored on a network link (Deep Packet Inspection) through the concept of working with connection flows. Bolvedere performs its work by leveraging the NetFlow version 9 and IPFIX protocols, but is not limited to these. It is implemented using a modular approach that allows for either complete execution of the system on a single host or the horizontal scaling out of subsystems on multiple hosts. The use of multiple hosts is achieved through the implementation of Zero Message Queue (ZMQ). This allows for Bolvedre to horizontally scale out, which results in an increase in processing resources and thus an increase in analysis throughput. This is due to ease of interprocess communications provided by ZMQ. Many underlying mechanisms in Bolvedere have been automated. This is intended to make the system more userfriendly, as the user need only tell Bolvedere what information they wish to analyse, and the system will then rebuild itself in order to achieve this required task. Bolvedere has also been hardware-accelerated through the use of Field-Programmable Gate Array (FPGA) technologies, which more than doubled the total throughput of the system.

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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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Книги з теми "Analysis of encrypted network flow"

Cherukuri, Aswani Kumar, Sumaiya Thaseen Ikram, Gang Li, and Xiao Liu. Encrypted Network Traffic Analysis. Cham: 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. Malabar, Fla: R.E. Krieger Pub. Co., 1987.

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

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Частини книг з теми "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, 19–45. Cham: 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, 61–72. Cham: 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, 47–59. Cham: 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, 73–99. Cham: 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, 1–17. Cham: 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, 49–60. Hoboken, NJ, USA: 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, 79–120. Singapore: 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, 357–75. Singapore: 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, 622–29. Singapore: 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!, 169–86. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44129-6_9.

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Тези доповідей конференцій з теми "Analysis of encrypted network flow"

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), 397–402. IEEE, 2024. http://dx.doi.org/10.1109/metrolivenv60384.2024.10615766.

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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), 1–5. IEEE, 2024. http://dx.doi.org/10.1109/pesgm51994.2024.10689085.

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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. Reston, VA: 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. New York, NY, USA: 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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Siby, Sandra, Marc Juarez, Claudia Diaz, Narseo Vallina-Rodriguez, and Carmela Troncoso. "Encrypted DNS --> Privacy? A Traffic Analysis Perspective." In Network and Distributed System Security Symposium. Reston, VA: Internet Society, 2020. http://dx.doi.org/10.14722/ndss.2020.24301.

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Jun, Luo, and Xu Chang Yue. "Analysis for an intelligent behavior of encrypted network." In 2020 International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE). IEEE, 2020. http://dx.doi.org/10.1109/icbase51474.2020.00061.

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"SECURITY SENSOR PROVIDING ANALYSIS OF ENCRYPTED NETWORK DATA." In 2nd International Conference on Web Information Systems and Technologies. SciTePress - Science and and Technology Publications, 2006. http://dx.doi.org/10.5220/0001254401720177.

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Звіти організацій з теми "Analysis of encrypted network flow"

Bethel, E. Wes. Query-Driven Network Flow Data Analysis and Visualization. Office of Scientific and Technical Information (OSTI), June 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,. Fort Belvoir, VA: Defense Technical Information Center, January 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, July 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 the association, so more consistency and further research is required. Despite this, the existing research using SNA to link characteristics of social networks to health and wellness find that stronger, more connected networks tend to be associated with better health outcomes. These results also present opportunities and insights for effective program implementation in response to disasters, to increase resilience, and to improve outcomes for individuals and communities.

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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.), June 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 element analysis of the rostrum. The flow network was weighted based on the parameter of interest, which is stress in the current study. The changing kinematics of the structural system was provided as input to the algorithm that computes the minimum-cut of the flow network. The proposed approach was verified using two classical problems three- and four-point bending of a simply-supported concrete beam. The current study also addresses the methodology used to prepare data in an appropriate format for a seamless transition from finite element binary database files to the abstract mathematical domain needed for the network flow analysis. A robust, platform-independent procedure was developed that efficiently handles the large datasets produced by the finite element simulations. Results from computational mechanics using Abaqus and complex network analysis are presented.

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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, August 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 outcome of our NMA will be measured by overall response rate and urodynamic tests, which includes postvoiding residual urine volume, maximum urinary flow rate, and maximal detrusor pressure.

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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, June 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, and hanging gardens (aka “springs”) in NCPN parks. The overall goal of the NCPN springs monitoring program is to determine long-term trends in hydrologic and vegetation properties in the context of changes in other ecological drivers, stressors, and processes. Specific objectives include describing the status and trends of water quantity (flow or stage as applicable), water quality (pH, specific conductance, temperature), and vegetation (endemic plant populations in hanging gardens, and vegetation species and cover). This protocol narrative describes the justification, sampling design, and field methods for NCPN springs monitoring. Details may be found in the SOPs, which are listed in Chapter 1 and available at irma.nps.gov. Other aspects of the protocol summarized in the narrative include procedures for data management, analysis, and reporting; personnel and operating requirements; and instructions for how to revise the protocol.

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Candelaria, Christopher, Sergey Borisov, Galina Hale, and Julián Caballero. Bank Linkages and International Trade. Inter-American Development Bank, December 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 with country-pair and year fixed effects, the paper finds that new connections between banks in a given country-pair lead to an increase in trade flow in the following year, even after controlling for the stock and flow of bank lending between the two countries. It is conjectured that the mechanism for this effect is that bank linkages reduce export risk, and four sets of results that support this conjecture are presented.

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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, December 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 geologic features comprising the vadose zone; (ii) to develop a complementary set of data analysis tools for discerning the fractal properties of hydraulic and transport parameters of highly heterogeneous vadose zone; (iii) to develop and test the associated computational methods for probabilistic analysis of flow and transport in highly heterogeneous subsurface environments; and (iv) to apply the computational framework to design an “optimal” observation network for monitoring and forecasting the fate and migration of contaminant plumes originating from agricultural activities. During the course of the project, we modified the third objective to include additional computational method, based on the notion that the heterogeneous formation can be considered as a mixture of populations of differing spatial structures. Regarding uncertainly analysis, going beyond approaches based on mean and variance of system states, we succeeded to develop probability density function (PDF) solutions enabling one to evaluate probabilities of rare events, required for probabilistic risk assessment. In addition, we developed reduced complexity models for the probabilistic forecasting of infiltration rates in heterogeneous soils during surface runoff and/or flooding events Regarding flow and transport in variably saturated, spatially heterogeneous formations associated with fine- and coarse-textured embedded soils (FTES- and CTES-formations, respectively).We succeeded to develop first-order and numerical frameworks for flow and transport in three-dimensional (3-D), variably saturated, bimodal, heterogeneous formations, with single and dual porosity, respectively. Regarding the sampling problem defined as, how many sampling points are needed, and where to locate them spatially in the horizontal x₂x₃ plane of the field. Based on our computational framework, we succeeded to develop and demonstrate a methdology that might improve considerably our ability to describe quntitaively the response of complicated 3-D flow systems. The results of the project are of theoretical and practical importance; they provided a rigorous framework to modeling water flow and solute transport in a realistic, highly heterogeneous, composite flow system with uncertain properties under-specified by data. Specifically, they: (i) enhanced fundamental understanding of the basic mechanisms of field-scale flow and transport in near-surface geological formations under realistic flow scenarios, (ii) provided a means to assess the ability of existing flow and transport models to handle realistic flow conditions, and (iii) provided a means to assess quantitatively the threats posed to groundwater by contamination from agricultural sources.

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Siebenaler. L52272 Detection of Small Leaks in Liquid Pipelines - Gap Study of Available Methods. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), February 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 what liquid pipeline operators need in terms of leak detection and what various leak detection technologies can provide, specifically related to small leaks. For this gap analysis study, small leaks were considered to be pipeline releases that were less than 5% of nominal pipeline flow rate.

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Kyllönen, Katriina, Karri Saarnio, Ulla Makkonen, and Heidi Hellén. Verification of the validity of air quality measurements related to the Directive 2004/107/EC in 2019-2020 (DIRME2019). Finnish Meteorological Institute, 2020. http://dx.doi.org/10.35614/isbn.9789523361256.

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This project summarizes the results from 2000–2020and evaluates the trueness andthequality control (QC) procedures of the ongoing polycyclic aromatic hydrocarbon (PAH)and trace element measurements in Finlandrelating to Air Quality (AQ) Directive 2004/107/EC. The evaluation was focused on benzo(a)pyrene and other PAH compounds as well as arsenic, cadmium and nickel in PM10and deposition. Additionally, it included lead and other metals in PM10and deposition, gaseous mercury and mercury deposition, andbriefly other specificAQ measurements such as volatile organic compounds (VOC)and PM2.5chemical composition. This project was conducted by the National Reference Laboratory on air quality and thiswas the first time these measurements were assessed. A major part of the project was field and laboratory audits of the ongoing PAH and metal measurements. Other measurements were briefly evaluated through interviews and available literature. In addition, the national AQ database, the expertise of local measurement networks and related publications were utilised. In total, all theseven measurement networks performing PAH and metal measurements in 2019–2020took part in the audits. Eleven stations were audited while these measurements are performed at 22 AQ stations in Finland. For the large networks, one station was chosen to represent the performance of the network. The audits included also six laboratories performing the analysis of the collected samples. The audits revealed the compliance of the measurements with the AQ Decree 113/2017, Directive 2004/107/EC and Standards of the European Committee for Standardization(CEN). In addition, general information of the measurements, instruments and quality control procedures were gained. The results of the laboratory audits were confidential,but this report includes general findings, and the measurement networks were informed on the audit results with the permission of the participating laboratories. As a conclusion, the measurementmethodsusedwere mainly reference methods. Currently, all sampling methods were reference methods; however, before 2018 three networks used other methods that may have underestimated concentrations. Regarding these measurements, it should be noted the results are notcomparable with the reference method. Laboratory methods were reference methods excluding two cases, where the first was considered an acceptable equivalent method. For the other, a change to a reference method was strongly recommended and this realized in 2020. For some new measurements, the ongoing QC procedures were not yet fully established, and advice were given. Some networks used consultant for calibration and maintenance, and thus theywere not fully aware of the QC procedures. EN Standards were mostly followed. Main concerns were related to the checks of flow and calculation of measurement uncertainty, and suggestions for improvement were given. When the measurement networks implement the recommendations given inthe audits, it can be concluded that the EN Standards are adequately followed in the networks. In the ongoing sampling, clear factors risking the trueness of the result were not found. This applies also for the laboratory analyses in 2020. One network had concentrations above the target value, and theindicative measurementsshould be updated to fixed measurements.

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