Academic literature on the topic 'Attacks detection'
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Journal articles on the topic "Attacks detection":
BALIGA, SANDEEP, ETHAN BUENO DE MESQUITA, and ALEXANDER WOLITZKY. "Deterrence with Imperfect Attribution." American Political Science Review 114, no. 4 (August 3, 2020): 1155–78. http://dx.doi.org/10.1017/s0003055420000362.
Kareem, Mohammed Ibrahim, Mohammad Jawad Kadhim Abood, and Karrar Ibrahim. "Machine learning-based PortScan attacks detection using OneR classifier." Bulletin of Electrical Engineering and Informatics 12, no. 6 (December 1, 2023): 3690–96. http://dx.doi.org/10.11591/eei.v12i6.4142.
O, Belej, Spas N, Artyshchuk I, and Fedastsou M. "Construction of a multi-agent attack detection system based on artificial intelligence models." Artificial Intelligence 26, jai2021.26(1) (June 30, 2021): 22–30. http://dx.doi.org/10.15407/jai2021.01.022.
Sambangi, Swathi, and Lakshmeeswari Gondi. "A Machine Learning Approach for DDoS (Distributed Denial of Service) Attack Detection Using Multiple Linear Regression." Proceedings 63, no. 1 (December 25, 2020): 51. http://dx.doi.org/10.3390/proceedings2020063051.
Xuan, Cho Do, Duc Duong, and 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, no. 6 (June 21, 2021): 11311–29. http://dx.doi.org/10.3233/jifs-202465.
Haseeb-ur-rehman, Rana M. Abdul, Azana Hafizah Mohd Aman, Mohammad Kamrul Hasan, Khairul Akram Zainol Ariffin, Abdallah Namoun, Ali Tufail, and Ki-Hyung Kim. "High-Speed Network DDoS Attack Detection: A Survey." Sensors 23, no. 15 (August 1, 2023): 6850. http://dx.doi.org/10.3390/s23156850.
Zhou, Qing Lei, Yan Ke Zhao, and Wei Jun Zhu. "Intrusion Detection for Universal Attack Mode Based on Projection Temporal Logic." Applied Mechanics and Materials 556-562 (May 2014): 2821–24. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.2821.
Sravanthi, P. "Machine Learning Methods for Attack Detection in Smart Grid." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (March 31, 2024): 2257–61. http://dx.doi.org/10.22214/ijraset.2024.59222.
Gupta, Punit, and Pallavi Kaliyar. "History Aware Anomaly Based IDS for Cloud IaaS." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 10, no. 6 (August 30, 2013): 1779–84. http://dx.doi.org/10.24297/ijct.v10i6.3205.
Qiao, Peng Zhe, Yi Ran Wang, and Yan Ke Zhao. "Intrusion Detection for Universal Attack Mode Based on Linear Temporal Logic with Past Construct." Applied Mechanics and Materials 680 (October 2014): 433–36. http://dx.doi.org/10.4028/www.scientific.net/amm.680.433.
Dissertations / Theses on the topic "Attacks detection":
Akdemir, Kahraman D. "Error Detection Techniques Against Strong Adversaries." Digital WPI, 2010. https://digitalcommons.wpi.edu/etd-dissertations/406.
Rodofile, 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.
Omar, Luma Qassam Abedalqader. "Face liveness detection under processed image attacks." Thesis, Durham University, 2018. http://etheses.dur.ac.uk/12812/.
Cheng, Long. "Program Anomaly Detection Against Data-Oriented Attacks." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/84937.
Ph. 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.
A 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.
Cataloged 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.
Kazi, 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.
Whitelaw, Clayton. "Precise Detection of Injection Attacks on Concrete Systems." Scholar Commons, 2015. http://scholarcommons.usf.edu/etd/6051.
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.
Books on the topic "Attacks detection":
Dübendorfer, Thomas P. Impact analysis, early detection, and mitigation of large-scale Internet attacks. Aachen: Shaker, 2005.
Li, Beibei, Rongxing Lu, and 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.
Raghavan, S. V., and 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.
K, 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.
Casola, Linda, and 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.
Raghavan, 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.
Nelson 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.
National 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.
Salem, Malek Ben. Towards Effective Masquerade Attack Detection. [New York, N.Y.?]: [publisher not identified], 2012.
Wan, Jun, Guodong Guo, Sergio Escalera, Hugo Jair Escalante, and 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.
Book chapters on the topic "Attacks detection":
Brooks, Richard R., and 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.
Kuribayashi, 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.
Krzysztoń, Mateusz, Marcin Lew, and 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.
Brooks, Richard R., and 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.
Szynkiewicz, 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.
Ayala, 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.
Ning, Peng, Sushil Jajodia, and 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.
Kuribayashi, 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.
Vella, Mark, Sotirios Terzis, and 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.
Wang, Wubing, Guoxing Chen, Yueqiang Cheng, Yinqian Zhang, and 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.
Conference papers on the topic "Attacks detection":
Alzubi, Saif, Frederic T. Stahl, and 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.
Kolodziej, Joanna, Mateusz Krzyszton, and 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.
Kazari, Kiarash, Ezzeldin Shereen, and 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.
Segura, Gustavo A. Nunez, Arsenia Chorti, and 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.
Ghafouri, Amin, Yevgeniy Vorobeychik, and 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.
Kim, Hannah, Celia Cintas, Girmaw Abebe Tadesse, and 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.
Xie, Liang, and 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.
Wu, Mingtao, and 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.
Mihai, Ioan cosmin, and 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.
Huang, Bo, Yi Wang, and 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.
Reports on the topic "Attacks detection":
Tan, Pang-Ning, and Anil K. Jain. Information Assurance: Detection & Response to Web Spam Attacks. Fort Belvoir, VA: Defense Technical Information Center, August 2010. http://dx.doi.org/10.21236/ada535002.
Baras, J. S., A. A. Cardenas, and V. Ramezani. On-Line Detection of Distributed Attacks from Space-Time Network Flow Patterns. Fort Belvoir, VA: Defense Technical Information Center, January 2003. http://dx.doi.org/10.21236/ada439768.
Kirichek, Galina, Vladyslav Harkusha, Artur Timenko, and Nataliia Kulykovska. System for detecting network anomalies using a hybrid of an uncontrolled and controlled neural network. [б. в.], February 2020. http://dx.doi.org/10.31812/123456789/3743.
Kolencik, 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., October 2023. http://dx.doi.org/10.52824/vzrb5079.
Ye, 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, January 2004. http://dx.doi.org/10.21236/ada421322.
Tayeb, Shahab. Taming the Data in the Internet of Vehicles. Mineta Transportation Institute, January 2022. http://dx.doi.org/10.31979/mti.2022.2014.
Fedchenko, Vitaly. Nuclear Security During Armed Conflict: Lessons From Ukraine. Stockholm International Peace Research Institute, March 2023. http://dx.doi.org/10.55163/zzsp5617.
Ingram, Joey Burton, Timothy J. Draelos, Meghan Galiardi, and Justin E. Doak. Temporal Cyber Attack Detection. Office of Scientific and Technical Information (OSTI), November 2017. http://dx.doi.org/10.2172/1409921.
Peterson, Dale. Cyber Security Audit and Attack Detection Toolkit. Office of Scientific and Technical Information (OSTI), May 2012. http://dx.doi.org/10.2172/1097617.
Jahanian, Farnam. Detecting and Surviving Large-Scale Network Infrastructure Attacks. Fort Belvoir, VA: Defense Technical Information Center, April 2005. http://dx.doi.org/10.21236/ada433781.