Literatura científica selecionada sobre o tema "Attacks detection"
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Artigos de revistas sobre o assunto "Attacks detection"
BALIGA, SANDEEP, ETHAN BUENO DE MESQUITA e ALEXANDER WOLITZKY. "Deterrence with Imperfect Attribution". American Political Science Review 114, n.º 4 (3 de agosto de 2020): 1155–78. http://dx.doi.org/10.1017/s0003055420000362.
Texto completo da fonteKareem, Mohammed Ibrahim, Mohammad Jawad Kadhim Abood e Karrar Ibrahim. "Machine learning-based PortScan attacks detection using OneR classifier". Bulletin of Electrical Engineering and Informatics 12, n.º 6 (1 de dezembro de 2023): 3690–96. http://dx.doi.org/10.11591/eei.v12i6.4142.
Texto completo da fonteO, Belej, Spas N, Artyshchuk I e Fedastsou M. "Construction of a multi-agent attack detection system based on artificial intelligence models". Artificial Intelligence 26, jai2021.26(1) (30 de junho de 2021): 22–30. http://dx.doi.org/10.15407/jai2021.01.022.
Texto completo da fonteSambangi, Swathi, e Lakshmeeswari Gondi. "A Machine Learning Approach for DDoS (Distributed Denial of Service) Attack Detection Using Multiple Linear Regression". Proceedings 63, n.º 1 (25 de dezembro de 2020): 51. http://dx.doi.org/10.3390/proceedings2020063051.
Texto completo da fonteXuan, Cho Do, Duc Duong e Hoang Xuan Dau. "A multi-layer approach for advanced persistent threat detection using machine learning based on network traffic". Journal of Intelligent & Fuzzy Systems 40, n.º 6 (21 de junho de 2021): 11311–29. http://dx.doi.org/10.3233/jifs-202465.
Texto completo da fonteHaseeb-ur-rehman, Rana M. Abdul, Azana Hafizah Mohd Aman, Mohammad Kamrul Hasan, Khairul Akram Zainol Ariffin, Abdallah Namoun, Ali Tufail e Ki-Hyung Kim. "High-Speed Network DDoS Attack Detection: A Survey". Sensors 23, n.º 15 (1 de agosto de 2023): 6850. http://dx.doi.org/10.3390/s23156850.
Texto completo da fonteZhou, Qing Lei, Yan Ke Zhao e Wei Jun Zhu. "Intrusion Detection for Universal Attack Mode Based on Projection Temporal Logic". Applied Mechanics and Materials 556-562 (maio de 2014): 2821–24. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.2821.
Texto completo da fonteSravanthi, P. "Machine Learning Methods for Attack Detection in Smart Grid". International Journal for Research in Applied Science and Engineering Technology 12, n.º 3 (31 de março de 2024): 2257–61. http://dx.doi.org/10.22214/ijraset.2024.59222.
Texto completo da fonteGupta, Punit, e Pallavi Kaliyar. "History Aware Anomaly Based IDS for Cloud IaaS". INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 10, n.º 6 (30 de agosto de 2013): 1779–84. http://dx.doi.org/10.24297/ijct.v10i6.3205.
Texto completo da fonteQiao, Peng Zhe, Yi Ran Wang e Yan Ke Zhao. "Intrusion Detection for Universal Attack Mode Based on Linear Temporal Logic with Past Construct". Applied Mechanics and Materials 680 (outubro de 2014): 433–36. http://dx.doi.org/10.4028/www.scientific.net/amm.680.433.
Texto completo da fonteTeses / dissertações sobre o assunto "Attacks detection"
Akdemir, Kahraman D. "Error Detection Techniques Against Strong Adversaries". Digital WPI, 2010. https://digitalcommons.wpi.edu/etd-dissertations/406.
Texto completo da fonteRodofile, Nicholas R. "Generating attacks and labelling attack datasets for industrial control intrusion detection systems". Thesis, Queensland University of Technology, 2018. https://eprints.qut.edu.au/121760/1/Nicholas_Rodofile_Thesis.pdf.
Texto completo da fonteOmar, Luma Qassam Abedalqader. "Face liveness detection under processed image attacks". Thesis, Durham University, 2018. http://etheses.dur.ac.uk/12812/.
Texto completo da fonteCheng, Long. "Program Anomaly Detection Against Data-Oriented Attacks". Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/84937.
Texto completo da fontePh. D.
Rosa, José Luís da Silva. "Customer-side detection of BGP routing attacks". Master's thesis, Universidade de Aveiro, 2016. http://hdl.handle.net/10773/17808.
Texto completo da fonteA utilização diária da Internet tornou-se uma rotina que foi assimilada pelas pessoas sem considerarem a complexidade interna desta gigante rede. Até um certo ponto, o Border Gateway Protocol é o que mantem toda esta conectividade possível apesar de ser um protocolo defeituoso por natureza. Em 2008, um ataque Man-In-The-Middle foi pela primeira vez apresentado ao grande público e desde de então mais técnicas para explorar este protocolo e obter tráfego alheio de forma ilícita foram dadas a conhecer. Mesmo que o desvio não aconteça com natureza maliciosa, mas sim devido a um erro de configuração, este é um problema que deverá ser enfrentado. Alguns provedores de serviço e institutos de investigação já apresentaram propostas para novos protocolos e/ou sistemas de monitorização, mas estes estão atrasados no seu desenvolvimento ou apenas afetam a camada superior da rede, deixando utilizadores e um grande número de empresas que estão ligadas a um provedor sem meios para agir e sem informação sobre o encaminhamento do seu tráfego. Nesta dissertação, é apresentado, concebido e implementado um sistema que atinge uma monitorização ativa do BGP através da medição do tempo médio de viagem de vários pacotes enviados de várias localizações, através de uma rede mundial de sondas, e do processamento dos resultados obtidos, permitindo que todos os interessados possam ser alertados.
The daily use of the Internet has become a routine that many people absorbed into their lives without even thinking about the insides of this gigantic network. To an extent, the Border Gateway Protocol is what is keeping all this connectivity together despite being a very flawed protocol due to its design. In 2008 a Man-In-The-Middle attack was first presented to the general audience and ever since more techniques were reported to use the protocol to obtain traffic illicitly. Even if the routing deviation does not occur via a malicious intention but due to some poorly configured router, this is a problem that must be tackled. Some network providers and research institutes already presented some drafts for new protocols or monitoring systems but they are late into deployment or only affect the top layer of the network, leaving users and most part of the companies connected to the provider impotent and without any proper information about the routing of their traffic. In this dissertation a system is presented, implemented and deployed, achieving an active monitorization of BGP through measurements of the average travel time of several packets sent to various locations by a worldwide set of Probes and the collected results processed allowing all concerned actors to be alerted.
Liu, Jessamyn. "Anomaly detection methods for detecting cyber attacks in industrial control systems". Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/129055.
Texto completo da fonteCataloged from PDF version of thesis.
Includes bibliographical references (pages 119-123).
Industrial control systems (ICS) are pervasive in modern society and increasingly under threat of cyber attack. Due to the critical nature of these systems, which govern everything from power and wastewater plants to refineries and manufacturing, a successful ICS cyber attack can result in serious physical consequences. This thesis evaluates multiple anomaly detection methods to quickly and accurately detect ICS cyber attacks. Two fundamental challenges in developing ICS cyber attack detection methods are the lack of historical attack data and the ability of attackers to make their malicious activity appear normal. The goal of this thesis is to develop methods which generalize well to anomalies that are not included in the training data and to increase the sensitivity of detection methods without increasing the false alarm rate. The thesis presents and analyzes a baseline detection method, the multivariate Shewhart control chart, and four extensions to the Shewhart chart which use machine learning or optimization methods to improve detection performance. Two of these methods, stationary subspace analysis and maximized ratio divergence analysis, are based on dimensionality reduction techniques, and an additional model-based method is implemented using residuals from LASSO regression models. The thesis also develops an ensemble method which uses an optimization formulation to combine the output of multiple models in a way that minimizes detection delay. When evaluated on 380 samples from the Kasperskey Tennessee Eastman process dataset, a simulated chemical process that includes disruptions from cyber attacks, the ensemble method reduced detection delay on attack data by 12% (55 minutes) on average when compared to the baseline method and was 9% (42 minutes) faster on average than the method which performed best on training data.
by Jessamyn Liu.
S.M.
S.M. Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center
Lu, Yuanchao. "On Traffic Analysis Attacks To Encrypted VoIP Calls". Cleveland State University / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=csu1260222271.
Texto completo da fonteKazi, Shehab. "Anomaly based Detection of Attacks on Security Protocols". Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4806.
Texto completo da fonteWhitelaw, Clayton. "Precise Detection of Injection Attacks on Concrete Systems". Scholar Commons, 2015. http://scholarcommons.usf.edu/etd/6051.
Texto completo da fonteDandurand, 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.
Texto completo da fonteLivros sobre o assunto "Attacks detection"
Dübendorfer, Thomas P. Impact analysis, early detection, and mitigation of large-scale Internet attacks. Aachen: Shaker, 2005.
Encontre o texto completo da fonteLi, Beibei, Rongxing Lu e Gaoxi Xiao. Detection of False Data Injection Attacks in Smart Grid Cyber-Physical Systems. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58672-0.
Texto completo da fonteRaghavan, S. V., e E. Dawson, eds. An Investigation into the Detection and Mitigation of Denial of Service (DoS) Attacks. India: Springer India, 2011. http://dx.doi.org/10.1007/978-81-322-0277-6.
Texto completo da fonteK, Kokula Krishna Hari, ed. Early Detection and Prevention of Vampire Attacks in Wireless Sensor Networks: ICIEMS 2014. India: Association of Scientists, Developers and Faculties, 2014.
Encontre o texto completo da fonteCasola, Linda, e Dionna Ali, eds. Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies. Washington, D.C.: National Academies Press, 2019. http://dx.doi.org/10.17226/25534.
Texto completo da fonteRaghavan, S. V. An Investigation into the Detection and Mitigation of Denial of Service (DoS) Attacks: Critical Information Infrastructure Protection. India: Springer India Pvt. Ltd., 2011.
Encontre o texto completo da fonteNelson A. Rockefeller Institute of Government., ed. The role of "home" in homeland security: The prevention and detection of terrorist attacks : the challenge for state and local government. Albany, N.Y: The Institute, 2003.
Encontre o texto completo da fonteNational Academy of Sciences (U.S.). Committee on Effectiveness of National Biosurveillance Systems, BioWatch and the Public Health System. BioWatch and public health surveillance: Evaluating systems for the early detection of biological threats. Washington, D.C: National Academies Press, 2011.
Encontre o texto completo da fonteSalem, Malek Ben. Towards Effective Masquerade Attack Detection. [New York, N.Y.?]: [publisher not identified], 2012.
Encontre o texto completo da fonteWan, Jun, Guodong Guo, Sergio Escalera, Hugo Jair Escalante e Stan Z. Li. Multi-Modal Face Presentation Attack Detection. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-031-01824-4.
Texto completo da fonteCapítulos de livros sobre o assunto "Attacks detection"
Brooks, Richard R., e İlker Özçelik. "Attack Detection". In Distributed Denial of Service Attacks, 115–38. Boca Raton : CRC Press, 2020.: Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9781315213125-8.
Texto completo da fonteKuribayashi, Minoru. "Adversarial Attacks". In Frontiers in Fake Media Generation and Detection, 63–79. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1524-6_3.
Texto completo da fonteKrzysztoń, Mateusz, Marcin Lew e Michał Marks. "NAD: Machine Learning Based Component for Unknown Attack Detection in Network Traffic". In Cybersecurity of Digital Service Chains, 83–102. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04036-8_4.
Texto completo da fonteBrooks, Richard R., e İlker Özçelik. "Deceiving DDoS Detection". In Distributed Denial of Service Attacks, 139–49. Boca Raton : CRC Press, 2020.: Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9781315213125-9.
Texto completo da fonteSzynkiewicz, Paweł. "Signature-Based Detection of Botnet DDoS Attacks". In Cybersecurity of Digital Service Chains, 120–35. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04036-8_6.
Texto completo da fonteAyala, Luis. "Detection of Cyber-Attacks". In Cybersecurity for Hospitals and Healthcare Facilities, 53–60. Berkeley, CA: Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-2155-6_6.
Texto completo da fonteNing, Peng, Sushil Jajodia e X. Sean Wang. "Decentralized Detection of Distributed Attacks". In Intrusion Detection in Distributed Systems, 71–90. Boston, MA: Springer US, 2004. http://dx.doi.org/10.1007/978-1-4615-0467-2_7.
Texto completo da fonteKuribayashi, Minoru. "Defense Against Adversarial Attacks". In Frontiers in Fake Media Generation and Detection, 131–48. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1524-6_6.
Texto completo da fonteVella, Mark, Sotirios Terzis e Marc Roper. "Distress Detection (Poster Abstract)". In Research in Attacks, Intrusions, and Defenses, 384–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33338-5_24.
Texto completo da fonteWang, Wubing, Guoxing Chen, Yueqiang Cheng, Yinqian Zhang e Zhiqiang Lin. "Specularizer : Detecting Speculative Execution Attacks via Performance Tracing". In Detection of Intrusions and Malware, and Vulnerability Assessment, 151–72. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80825-9_8.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Attacks detection"
Alzubi, Saif, Frederic T. Stahl e Mohamed M. Gaber. "Towards Intrusion Detection Of Previously Unknown Network Attacks". In 35th ECMS International Conference on Modelling and Simulation. ECMS, 2021. http://dx.doi.org/10.7148/2021-0035.
Texto completo da fonteKolodziej, Joanna, Mateusz Krzyszton e Pawel Szynkiewicz. "Anomaly Detection In TCP/IP Networks". In 37th ECMS International Conference on Modelling and Simulation. ECMS, 2023. http://dx.doi.org/10.7148/2023-0542.
Texto completo da fonteKazari, Kiarash, Ezzeldin Shereen e Gyorgy Dan. "Decentralized Anomaly Detection in Cooperative Multi-Agent Reinforcement Learning". In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/19.
Texto completo da fonteSegura, Gustavo A. Nunez, Arsenia Chorti e Cíntia Borges Margi. "IDIT-SDN: Intrusion Detection Framework for Software-defined Wireless Sensor Networks". In Anais Estendidos do Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/sbrc_estendido.2023.817.
Texto completo da fonteGhafouri, Amin, Yevgeniy Vorobeychik e Xenofon Koutsoukos. "Adversarial Regression for Detecting Attacks in Cyber-Physical Systems". In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/524.
Texto completo da fonteKim, Hannah, Celia Cintas, Girmaw Abebe Tadesse e Skyler Speakman. "Spatially Constrained Adversarial Attack Detection and Localization in the Representation Space of Optical Flow Networks". In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/107.
Texto completo da fonteXie, Liang, e Sencun Zhu. "Message Dropping Attacks in Overlay Networks: Attack Detection and Attacker Identification". In 2006 Securecomm and Workshops. IEEE, 2006. http://dx.doi.org/10.1109/seccomw.2006.359534.
Texto completo da fonteWu, Mingtao, e Young B. Moon. "Intrusion Detection of Cyber-Physical Attacks in Manufacturing Systems: A Review". In ASME 2019 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/imece2019-10135.
Texto completo da fonteMihai, Ioan cosmin, e Laurentiu Giurea. "MANAGEMENT OF ELEARNING PLATFORMS SECURITY". In eLSE 2016. Carol I National Defence University Publishing House, 2016. http://dx.doi.org/10.12753/2066-026x-16-061.
Texto completo da fonteHuang, Bo, Yi Wang e Wei Wang. "Model-Agnostic Adversarial Detection by Random Perturbations". In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/651.
Texto completo da fonteRelatórios de organizações sobre o assunto "Attacks detection"
Tan, Pang-Ning, e Anil K. Jain. Information Assurance: Detection & Response to Web Spam Attacks. Fort Belvoir, VA: Defense Technical Information Center, agosto de 2010. http://dx.doi.org/10.21236/ada535002.
Texto completo da fonteBaras, J. S., A. A. Cardenas e V. Ramezani. On-Line Detection of Distributed Attacks from Space-Time Network Flow Patterns. Fort Belvoir, VA: Defense Technical Information Center, janeiro de 2003. http://dx.doi.org/10.21236/ada439768.
Texto completo da fonteKirichek, Galina, Vladyslav Harkusha, Artur Timenko e Nataliia Kulykovska. System for detecting network anomalies using a hybrid of an uncontrolled and controlled neural network. [б. в.], fevereiro de 2020. http://dx.doi.org/10.31812/123456789/3743.
Texto completo da fonteKolencik, Marian. A critical evaluation of the risk indicators of criminal conduct involving CBRN and explosive materials - Behavioural and observational analysis in crime detection and investigation. ISEM Institute, n.p.o., outubro de 2023. http://dx.doi.org/10.52824/vzrb5079.
Texto completo da fonteYe, Nong. The Monitoring, Detection, Isolation and Assessment of Information Warfare Attacks Through Multi-Level, Multi-Scale System Modeling and Model Based Technology. Fort Belvoir, VA: Defense Technical Information Center, janeiro de 2004. http://dx.doi.org/10.21236/ada421322.
Texto completo da fonteTayeb, Shahab. Taming the Data in the Internet of Vehicles. Mineta Transportation Institute, janeiro de 2022. http://dx.doi.org/10.31979/mti.2022.2014.
Texto completo da fonteFedchenko, Vitaly. Nuclear Security During Armed Conflict: Lessons From Ukraine. Stockholm International Peace Research Institute, março de 2023. http://dx.doi.org/10.55163/zzsp5617.
Texto completo da fonteIngram, Joey Burton, Timothy J. Draelos, Meghan Galiardi e Justin E. Doak. Temporal Cyber Attack Detection. Office of Scientific and Technical Information (OSTI), novembro de 2017. http://dx.doi.org/10.2172/1409921.
Texto completo da fontePeterson, Dale. Cyber Security Audit and Attack Detection Toolkit. Office of Scientific and Technical Information (OSTI), maio de 2012. http://dx.doi.org/10.2172/1097617.
Texto completo da fonteJahanian, Farnam. Detecting and Surviving Large-Scale Network Infrastructure Attacks. Fort Belvoir, VA: Defense Technical Information Center, abril de 2005. http://dx.doi.org/10.21236/ada433781.
Texto completo da fonte