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Relevant bibliographies by topics / Network traffic detection

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Academic literature on the topic 'Network traffic detection'

Author: Grafiati

Published: 28 June 2021

Last updated: 17 February 2022

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Journal articles on the topic "Network traffic detection"

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Praveena, Nutakki, Dr Ujwal A. Lanjewar, and Chilakalapudi Meher Babu. "VIABLE NETWORK INTRUSION DETECTION ON WIRELESS ADHOC NETWORKS." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 5, no. 1 (June 23, 2013): 29–34. http://dx.doi.org/10.24297/ijct.v5i1.4383.

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Control architecture for resource allocation in satellite networks is proposed, along with the specification of performance indexes and control strategies. The latter, besides being based on information on traffic statistics and network status, rely upon some knowledge of the fading conditions over the satellite network channels. The resource allocation problem consists of the assignment, by a master station, of a total available bandwidth among traffic earth stations in the presence of different traffic types. Traffic stations are assumed to measure continuously their signal fade level, but this information may either be used only locally or also communicated to the master station. According to the information made available on-line to the master station on the level of the fading attenuation of the traffic stations, the assignment can be made static, based on the a priori knowledge of long-term fading statistics, or dynamic, based on the updated measurements. In any case, the decisions can be adapted to slowly time-varying traffic characteristics. At each earth station, two basic traffic types are assumed to be present, namely guaranteed bandwidth, real-time, synchronous data (stream traffic), and best effort traffic (datagram traffic). Numerical results are provided for a specific architecture in the dynamic case, in a real environment, based on the Italian satellite national coverage payload characteristics.

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

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Detecting exploits is crucial since the effect of undetected ones can be devastating. Identifying their presence on the network allows us to respond and block their malicious payload before they cause damage to the system. Inspecting the payload of network traffic may offer better performance in detecting exploits as they tend to hide their presence and behave similarly to legitimate traffic. Previous works on deep packet inspection for detecting malicious traffic regularly read the full length of application layer messages. As the length varies, longer messages will take more time to analyse, during which time the attack creates a disruptive impact on the system. Hence, we propose a novel early exploit detection mechanism that scans network traffic, reading only 35.21% of application layer messages to predict malicious traffic while retaining a 97.57% detection rate and a 1.93% false positive rate. Our recurrent neural network- (RNN-) based model is the first work to our knowledge that provides early prediction of malicious application layer messages, thus detecting a potential attack earlier than other state-of-the-art approaches and enabling a form of early warning system.

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

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Anomalous traffic often has a significant impact on network activities and lead to the severe damage to our networks because they usually are involved with network faults and network attacks. How to detect effectively network traffic anomalies is a challenge for network operators and researchers. This paper proposes a novel method for detecting traffic anomalies in a network, based on continuous wavelet transform. Firstly, continuous wavelet transforms are performed for network traffic in several scales. We then use multi-scale analysis theory to extract traffic characteristics. And these characteristics in different scales are further analyzed and an appropriate detection threshold can be obtained. Consequently, we can make the exact anomaly detection. Simulation results show that our approach is effective and feasible.

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Anwer, M., S. M. Khan, M. U. Farooq, and W. Waseemullah. "Attack Detection in IoT using Machine Learning." Engineering, Technology & Applied Science Research 11, no. 3 (June 12, 2021): 7273–78. http://dx.doi.org/10.48084/etasr.4202.

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Many researchers have examined the risks imposed by the Internet of Things (IoT) devices on big companies and smart towns. Due to the high adoption of IoT, their character, inherent mobility, and standardization limitations, smart mechanisms, capable of automatically detecting suspicious movement on IoT devices connected to the local networks are needed. With the increase of IoT devices connected through internet, the capacity of web traffic increased. Due to this change, attack detection through common methods and old data processing techniques is now obsolete. Detection of attacks in IoT and detecting malicious traffic in the early stages is a very challenging problem due to the increase in the size of network traffic. In this paper, a framework is recommended for the detection of malicious network traffic. The framework uses three popular classification-based malicious network traffic detection methods, namely Support Vector Machine (SVM), Gradient Boosted Decision Trees (GBDT), and Random Forest (RF), with RF supervised machine learning algorithm achieving far better accuracy (85.34%). The dataset NSL KDD was used in the recommended framework and the performances in terms of training, predicting time, specificity, and accuracy were compared.

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

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

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Lu, Jiazhong, Fengmao Lv, Zhongliu Zhuo, Xiaosong Zhang, Xiaolei Liu, Teng Hu, and Wei Deng. "Integrating Traffics with Network Device Logs for Anomaly Detection." Security and Communication Networks 2019 (June 13, 2019): 1–10. http://dx.doi.org/10.1155/2019/5695021.

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Advanced cyberattacks are often featured by multiple types, layers, and stages, with the goal of cheating the monitors. Existing anomaly detection systems usually search logs or traffics alone for evidence of attacks but ignore further analysis about attack processes. For instance, the traffic detection methods can only detect the attack flows roughly but fail to reconstruct the attack event process and reveal the current network node status. As a result, they cannot fully model the complex multistage attack. To address these problems, we present Traffic-Log Combined Detection (TLCD), which is a multistage intrusion analysis system. Inspired by multiplatform intrusion detection techniques, we integrate traffics with network device logs through association rules. TLCD correlates log data with traffic characteristics to reflect the attack process and construct a federated detection platform. Specifically, TLCD can discover the process steps of a cyberattack attack, reflect the current network status, and reveal the behaviors of normal users. Our experimental results over different cyberattacks demonstrate that TLCD works well with high accuracy and low false positive rate.

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Ali, Wasim Ahmed, Manasa K. N, Mohammed Aljunid, Malika Bendechache, and P. Sandhya. "Review of Current Machine Learning Approaches for Anomaly Detection in Network Traffic." Journal of Telecommunications and the Digital Economy 8, no. 4 (December 2, 2020): 64–95. http://dx.doi.org/10.18080/jtde.v8n4.307.

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Due to the advance in network technologies, the number of network users is growing rapidly, which leads to the generation of large network traffic data. This large network traffic data is prone to attacks and intrusions. Therefore, the network needs to be secured and protected by detecting anomalies as well as to prevent intrusions into networks. Network security has gained attention from researchers and network laboratories. In this paper, a comprehensive survey was completed to give a broad perspective of what recently has been done in the area of anomaly detection. Newly published studies in the last five years have been investigated to explore modern techniques with future opportunities. In this regard, the related literature on anomaly detection systems in network traffic has been discussed, with a variety of typical applications such as WSNs, IoT, high-performance computing, industrial control systems (ICS), and software-defined network (SDN) environments. Finally, we underlined diverse open issues to improve the detection of anomaly systems.

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Barrionuevo, Mercedes, Mariela Lopresti, Natalia Miranda, and Fabiana Piccoli. "Secure Computer Network: Strategies and Challengers in Big Data Era." Journal of Computer Science and Technology 18, no. 03 (December 12, 2018): e28. http://dx.doi.org/10.24215/16666038.18.e28.

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As computer networks have transformed in essential tools, their security has become a crucial problem for computer systems. Detecting unusual values fromlarge volumes of information produced by network traffic has acquired huge interest in the network security area. Anomaly detection is a starting point toprevent attacks, therefore it is important for all computer systems in a network have a system of detecting anomalous events in a time near their occurrence. Detecting these events can lead network administrators to identify system failures, take preventive actions and avoid a massive damage.This work presents, first, how identify network traffic anomalies through applying parallel computing techniques and Graphical Processing Units in two algorithms, one of them a supervised classification algorithm and the other based in traffic image processing.Finally, it is proposed as a challenge to resolve the anomalies detection using an unsupervised algorithm as Deep Learning.

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Lalitha, K. V., and V. R. Josna. "Traffic Verification for Network Anomaly Detection in Sensor Networks." Procedia Technology 24 (2016): 1400–1405. http://dx.doi.org/10.1016/j.protcy.2016.05.161.

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Meimei Ding and Hui Tian. "PCA-based network Traffic anomaly detection." Tsinghua Science and Technology 21, no. 5 (October 2016): 500–509. http://dx.doi.org/10.1109/tst.2016.7590319.

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Dissertations / Theses on the topic "Network traffic detection"

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

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

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Critical infrastructure provides us with the most important parts of modern society, electricity, water and transport. To increase efficiency and to meet new demands from the customer remote monitoring and control of the systems is necessary. This opens new ways for an attacker to reach the Supervisory Control And Data Acquisition (SCADA) systems that control and monitors the physical processes involved. This also increases the need for security features specially designed for these settings. Anomaly-based detection is a technique suitable for the more deterministic SCADA systems. This thesis uses a combination of two techniques to detect anomalies. The first technique is an automatic whitelist that learns the behavior of the network flows. The second technique utilizes the differences in arrival times of the network packets. A prototype anomaly detector has been developed in Bro. To analyze the IEC 60870-5-104 protocol a new parser for Bro was also developed. The resulting anomaly detector was able to achieve a high detection rate for three of the four different types of attacks evaluated. The studied methods of detection are promising when used in a highly deterministic setting, such as a SCADA system.

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

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

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Botnets represent one of the most serious threats against Internet security since they serve as platforms that are responsible for the vast majority of large-scale and coordinated cyber attacks, such as distributed denial of service, spamming, and information stolen. Detecting botnets is therefore of great importance and a number of network-based botnet detection systems have been proposed. However, as botnets perform attacks in an increasingly stealthy way and the volume of network traffic is rapidly growing, existing botnet detection systems are faced with significant challenges in terms of effectiveness and scalability. The objective of this dissertation is to build novel network-based solutions that can boost both the effectiveness of existing botnet detection systems by detecting botnets whose attacks are very hard to be observed in network traffic, and their scalability by adaptively sampling network packets that are likely to be generated by botnets. To be specific, this dissertation describes three unique contributions. First, we built a new system to detect drive-by download attacks, which represent one of the most significant and popular methods for botnet infection. The goal of our system is to boost the effectiveness of existing drive-by download detection systems by detecting a large number of drive-by download attacks that are missed by these existing detection efforts. Second, we built a new system to detect botnets with peer-to-peer (P2P) command&control (C&C) structures (i.e., P2P botnets), where P2P C&Cs represent currently the most robust C&C structures against disruption efforts. Our system aims to boost the effectiveness of existing P2P botnet detection by detecting P2P botnets in two challenging scenarios: i) botnets perform stealthy attacks that are extremely hard to be observed in the network traffic; ii) bot-infected hosts are also running legitimate P2P applications (e.g., Bittorrent and Skype). Finally, we built a novel traffic analysis framework to boost the scalability of existing botnet detection systems. Our framework can effectively and efficiently identify a small percentage of hosts that are likely to be bots, and then forward network traffic associated with these hosts to existing detection systems for fine-grained analysis, thereby boosting the scalability of existing detection systems. Our traffic analysis framework includes a novel botnet-aware and adaptive packet sampling algorithm, and a scalable flow-correlation technique.

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

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Botnets are generally recognized as one of the most challenging threats on the Internet today. Botnets have been involved in many attacks targeting multinational organizations and even nationwide internet services. As more effective detection and mitigation approaches are proposed by security researchers, botnet developers are employing new techniques for evasion. It is not surprising that the Domain Name System (DNS) is abused by botnets for the purposes of evasion, because of the important role of DNS in the operation of the Internet. DNS provides a flexible mapping between domain names and IP addresses, thus botnets can exploit this dynamic mapping to mask the location of botnet controllers. Domain-flux and fast-flux (also known as IP-flux) are two emerging techniques which aim at exhausting the tracking and blacklisting effort of botnet defenders by rapidly changing the domain names or their associated IP addresses that are used by the botnet. In this thesis, we employ passive DNS analysis to develop an anomaly-based technique for detecting the presence of a domain-flux or fast- flux botnet in a network. To do this, we construct a lookup graph and a failure graph from captured DNS traffic and decompose these graphs into clusters which have a strong correlation between their domains, hosts, and IP addresses. DNS related features are extracted for each cluster and used as input to a classication module to identify the presence of a domain-flux or fast-flux botnet in the network. The experimental evaluation on captured traffic traces veried that the proposed technique successfully detected domain-flux botnets in the traces. The proposed technique complements other techniques for detecting botnets through traffic analysis.
Botnets betraktas som ett av de svåraste Internet-hoten idag. Botnets har använts vid många attacker mot multinationella organisationer och även nationella myndigheters och andra nationella Internet-tjänster. Allt eftersom mer effektiva detekterings - och skyddstekniker tas fram av säkerhetsforskare, har utvecklarna av botnets tagit fram nya tekniker för att undvika upptäckt. Därför är det inte förvånande att domännamnssystemet (Domain Name System, DNS) missbrukas av botnets för att undvika upptäckt, på grund av den viktiga roll domännamnssystemet har för Internets funktion - DNS ger en flexibel bindning mellan domännamn och IP-adresser. Domain-flux och fast-flux (även kallat IP-flux) är två relativt nya tekniker som används för att undvika spårning och svartlistning av IP-adresser av botnet-skyddsmekanismer genom att snabbt förändra bindningen mellan namn och IP-adresser som används av botnets. I denna rapport används passiv DNS-analys för att utveckla en anomali-baserad teknik för detektering av botnets som använder sig av domain-flux eller fast-flux. Tekniken baseras på skapandet av en uppslagnings-graf och en fel-graf från insamlad DNS-traffik och bryter ned dessa grafer i kluster som har stark korrelation mellan de ingående domänerna, maskinerna, och IP-adresserna. DNSrelaterade egenskaper extraheras för varje kluster och används som indata till en klassifficeringsmodul för identiffiering av domain-flux och fast-flux botnets i nätet. Utvärdering av metoden genom experiment på insamlade traffikspår visar att den föreslagna tekniken lyckas upptäcka domain-flux botnets i traffiken. Genom att fokusera på DNS-information kompletterar den föreslagna tekniken andra tekniker för detektering av botnets genom traffikanalys.

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

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Virtually every day data breach incidents are reported in the news. Scammers, fraudsters, hackers and malicious insiders are raking in millions with sensitive business and personal information. Not all incidents involve cunning and astute hackers. The involvement of insiders is ever increasing. Data information leakage is a critical issue for many companies, especially nowadays where every employee has an access to high speed internet.In the past, email was the only gateway to send out information but with the advent of technologies like SaaS (e.g. Dropbox) and other similar services, possible routes have become numerous and complicated to guard for an organisation. Data is valuable, for legitimate purposes or criminal purposes alike. An intuitive approach to check data leakage is to scan the network traffic for presence of any confidential information transmitted. The existing systems use slew of techniques like keyword matching, regular expression pattern matching, cryptographic algorithms or rolling hashes to prevent data leakage. These techniques are either trivial to evade or suffer with high false alarm rate. In this thesis, 'known file content' detection in network traffic using approximate matching is presented. It performs content analysis on-the-fly. The approach is protocol agnostic and filetype independent. Compared to existing techniques, proposed approach is straight forward and does not need comprehensive configuration. It is easy to deploy and maintain, as only file fingerprint is required, instead of verbose rules.

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

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

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

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

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Kakavelakis, Georgios. "A real-time system for abusive network traffic detection." Thesis, Monterey, California. Naval Postgraduate School, 2011. http://hdl.handle.net/10945/5754.

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Approved for public release; distribution is unlimited
Abusive network traffic--to include unsolicited e-mail, malware propagation, and denial-of-service attacks--remains a constant problem in the Internet. Despite extensive research in, and subsequent deployment of, abusive-traffic detection infrastructure, none of the available techniques addresses the problem effectively or completely. The fundamental failing of existing methods is that spammers and attack perpetrators rapidly adapt to and circumvent new mitigation techniques. Analyzing network traffic by exploiting transport-layer characteristics can help remedy this and provide effective detection of abusive traffic. Within this framework, we develop a real-time, online system that integrates transport layer characteristics into the existing SpamAssasin tool for detecting unsolicited commercial e-mail (spam). Specifically, we implement the previously proposed, but undeveloped, SpamFlow technique. We determine appropriate algorithms based on classification performance, training required, adaptability, and computational load. We evaluate system performance in a virtual test bed and live environment and present analytical results. Finally, we evaluate our system in the context of Spam Assassin's auto-learning mode, providing an effective method to train the system without explicit user interaction or feedback.

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Books on the topic "Network traffic detection"

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

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

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

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

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

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

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

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Aghdam, Hamed Habibi, and Elnaz Jahani Heravi. Guide to Convolutional Neural Networks: A Practical Application to Traffic-Sign Detection and Classification. Springer, 2017.

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Aghdam, Hamed Habibi, and Elnaz Jahani Heravi. Guide to Convolutional Neural Networks: A Practical Application to Traffic-Sign Detection and Classification. Springer, 2018.

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Biersack, Ernst, Christian Callegari, and Maja Matijasevic. Data Traffic Monitoring and Analysis: From Measurement, Classification, and Anomaly Detection to Quality of Experience. Springer, 2013.

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Book chapters on the topic "Network traffic detection"

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Liu, ChenHuan, QianKun Liu, ShanShan Hao, CongXiao Bao, and Xing Li. "IPv6-Darknet Network Traffic Detection." In Lecture Notes in Computer Science, 231–41. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78612-0_19.

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Cui, Qian, Guy-Vincent Jourdan, Gregor V. Bochmann, and Iosif-Viorel Onut. "Proactive Detection of Phishing Kit Traffic." In Applied Cryptography and Network Security, 257–86. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78375-4_11.

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Coluccia, Angelo, Alessandro D’Alconzo, and Fabio Ricciato. "Distribution-Based Anomaly Detection in Network Traffic." In Data Traffic Monitoring and Analysis, 202–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36784-7_9.

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Bhuyan, Monowar H., Dhruba K. Bhattacharyya, and Jugal K. Kalita. "Network Traffic Anomaly Detection Techniques and Systems." In Computer Communications and Networks, 115–69. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65188-0_4.

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de la Puerta, José Gaviria, Iker Pastor-López, Borja Sanz, and Pablo G. Bringas. "Network Traffic Analysis for Android Malware Detection." In Lecture Notes in Computer Science, 468–79. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29859-3_40.

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Moussas, Vassilios C. "Adaptive Traffic Modelling for Network Anomaly Detection." In Springer Optimization and Its Applications, 333–51. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74325-7_17.

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Bialas, Andrzej, Marcin Michalak, and Barbara Flisiuk. "Anomaly Detection in Network Traffic Security Assurance." In Advances in Intelligent Systems and Computing, 46–56. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19501-4_5.

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Kang, Koohong. "Anomaly Detection of Hostile Traffic Based on Network Traffic Distributions." In Lecture Notes in Computer Science, 781–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89524-4_77.

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Mazel, Johan, Pedro Casas, and Philippe Owezarski. "Sub-Space Clustering and Evidence Accumulation for Unsupervised Network Anomaly Detection." In Traffic Monitoring and Analysis, 15–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20305-3_2.

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Cho, Yoohee, Koohong Kang, Ikkyun Kim, and Kitae Jeong. "Baseline Traffic Modeling for Anomalous Traffic Detection on Network Transit Points." In Management Enabling the Future Internet for Changing Business and New Computing Services, 385–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04492-2_39.

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Conference papers on the topic "Network traffic detection"

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Si, Wen, Jianghai Li, Ronghong Qu, and Xiaojin Huang. "Anomaly Detection for Network Traffic of I&C Systems Based on Neural Network." In 2020 International Conference on Nuclear Engineering collocated with the ASME 2020 Power Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/icone2020-16900.

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Abstract Anomaly detection is significant for the cybersecurity of the I&C systems at nuclear power plants. There are a large number of network packets generated in the network traffic of the I&C systems. There are many attributes of the network traffic can used for anomaly detection. The structure of the network packets is analyzed in detail with examples. Then, Features are extracted from network packets. An unsupervised neural network called autoencoder is applied for anomaly detection. Training and testing database are captured from a physical PLC system which simulates a water level control system. The result of the test results shows that the neural network can detect anomaly successfully.

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Guillot, Andreas, Romain Fontugne, Philipp Winter, Pascal Merindol, Alistair King, Alberto Dainotti, and Cristel Pelsser. "Chocolatine: Outage Detection for Internet Background Radiation." In 2019 Network Traffic Measurement and Analysis Conference (TMA). IEEE, 2019. http://dx.doi.org/10.23919/tma.2019.8784607.

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Shah, Anant, Romain Fontugne, Emile Aben, Cristel Pelsser, and Randy Bush. "Disco: Fast, good, and cheap outage detection." In 2017 Network Traffic Measurement and Analysis Conference (TMA). IEEE, 2017. http://dx.doi.org/10.23919/tma.2017.8002902.

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Salvador, Paulo, and Antonio Nogueira. "Customer-side detection of Internet-scale traffic redirection." In 2014 16th International Telecommunications Network Strategy and Planning Symposium (Networks). IEEE, 2014. http://dx.doi.org/10.1109/netwks.2014.6958532.

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Goodall, John R. "Visualizing network traffic for intrusion detection." In the 6th ACM conference. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1142405.1142465.

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Nikishova, Arina, Irina Ananina, and Evgeny Ananin. "Network traffic clustering for intrusion detection." In IV International research conference "Information technologies in Science, Management, Social sphere and Medicine" (ITSMSSM 2017). Paris, France: Atlantis Press, 2017. http://dx.doi.org/10.2991/itsmssm-17.2017.53.

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Michalak, Marcin, Łukasz Wawrowski, Marek Sikora, Rafał Kurianowicz, Artur Kozłowski, and Andrzej Białas. "Outlier Detection in Network Traffic Monitoring." In 10th International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010238205230530.

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De Lucia, Michael, Paul E. Maxwell, Nathaniel D. Bastian, Ananthram Swami, Brian Jalaian, and Nandi Leslie. "Machine learning raw network traffic detection." In Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III, edited by Tien Pham, Latasha Solomon, and Myron E. Hohil. SPIE, 2021. http://dx.doi.org/10.1117/12.2586114.

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Prasse, Paul, Lukas Machlica, Tomas Pevny, Jiri Havelka, and Tobias Scheffer. "Malware Detection by Analysing Network Traffic with Neural Networks." In 2017 IEEE Security and Privacy Workshops (SPW). IEEE, 2017. http://dx.doi.org/10.1109/spw.2017.8.

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Chapaneri, Radhika, and Seema Shah. "Detection of Malicious Network Traffic using Convolutional Neural Networks." In 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT). IEEE, 2019. http://dx.doi.org/10.1109/icccnt45670.2019.8944814.

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Reports on the topic "Network traffic detection"

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

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

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Abstract:

Local and regional planners struggle to keep up with rapid changes in mobility patterns. This exploratory research is framed with the overarching goal of asking if and how geo-social network data (GSND), in this case, Twitter data, can be used to understand and explain commuting and non-commuting travel patterns. The research project set out to determine whether GSND may be used to augment US Census LODES data beyond commuting trips and whether it may serve as a short-term substitute for commuting trips. It turns out that the reverse is true and the common practice of employing LODES data to extrapolate to overall traffic demand is indeed justified. This means that expensive and rarely comprehensive surveys are now only needed to capture trip purposes. Regardless of trip purpose (e.g., shopping, regular recreational activities, dropping kids at school), the LODES data is an excellent predictor of overall road segment loads.

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