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Guide to Intrusion Detection and Prevention Systems (IDPS)
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The Information Technology Laboratory (ITL) at the National Institute of Standards and Technology (NIST) promotes the U.S. economy and public welfare by providing technical leadership for the nation's measurement and standards infrastructure.ITL develops tests, test methods, reference data, proof of concept implementations, and technical analysis to advance the development and productive use of information technology.ITL's responsibilities include the development of technical, physical, administrative, and management standards and guidelines for the cost-effective security and privacy of sensitive unclassified information in Federal computer systems.This Special Publication 800-series reports on ITL's research, guidance, and outreach efforts in computer security and its collaborative activities with industry, government, and academic organizations.Certain commercial entities, equipment, or materials may be identified in this document in order to describe an experimental procedure or concept adequately.Such identification is not intended to imply recommendation or endorsement by the National Institute of Standards and Technology, nor is it intended to imply that the entities, materials, or equipment are necessarily the best available for the purpose.
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“At the process layer, protection technologies like firewalls, intrusion detection systems, and role-based access controls are required to align with defined policies to provide concrete safeguards [7].”
From Seamless Industry 4.0 Integration: A Multilayered Cyber-Security Framework for Resilient SCADA Deployments in CPPS (2023) · cites this paper as [7]
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Use of the Common Vulnerabilities and Exposures (CVE) vulnerability naming scheme
Mell, P., & Grance, T. (2002) · doi:10.6028/nist.sp.800-51 · Cited by 50
Self-citationIngenhoven, C. (2010) · De Gruyter eBooks · doi:10.11129/detail.9783955531713.18 · Cited by 4
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Zarpelão, B. B., Miani, R. S., Kawakani, C. T., & de Alvarenga, S. C. (2017) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2017.02.009 · Cited by 1,022
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García, S., Grill, M., Stiborek, J., & Zunino, A. (2014) · Computers & Security · doi:10.1016/j.cose.2014.05.011 · Cited by 962
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Modi, C., Patel, D., Borisaniya, B., Patel, H. B., Patel, A., & Rajarajan, M. (2012) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2012.05.003 · Cited by 897
Xie, J., Yu, F. R., Huang, T., Xie, R., Liu, J., Wang, C., & Liu, Y. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2866942 · Cited by 700
Aldweesh, A., Derhab, A., & Emam, A. Z. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.105124 · Cited by 703
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Ashfaq, R. A. R., Wang, X., Huang, J. Z., Abbas, H., & He, Y. (2016) · Information Sciences · doi:10.1016/j.ins.2016.04.019 · Cited by 554
When Intrusion Detection Meets Blockchain Technology: A Review
Meng, W., Tischhauser, E., Wang, Q., Wang, Y., & Han, J. (2018) · IEEE Access · doi:10.1109/access.2018.2799854 · Cited by 528
Cyber-physical systems security: Limitations, issues and future trends
Yaacoub, J.-P. A., Salman, O., Noura, H., Kaaniche, N., Chehab, A., & Malli, M. A. (2020) · Microprocessors and Microsystems · doi:10.1016/j.micpro.2020.103201 · Cited by 512
Security and Privacy Controls for Federal Information Systems and Organizations
Joint Task Force Transformation Initiative (2013) · doi:10.6028/nist.sp.800-53r4 · Cited by 470
Passban IDS: An Intelligent Anomaly-Based Intrusion Detection System for IoT Edge Devices
Eskandari, M., Janjua, Z. H., Vecchio, M., & Antonelli, F. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2970501 · Cited by 437
An intrusion detection and prevention system in cloud computing: A systematic review
Patel, A., Taghavi, M., Bakhtiyari, K., & Celestino Júnior, J. (2013) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2012.08.007 · Cited by 425
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Liu, L., Vel, O. D., Han, Q., Zhang, J., & Xiang, Y. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2800740 · Cited by 386
Anomaly detection in wireless sensor networks: A survey
Xie, M., Han, S., Tian, B., & Parvin, S. (2011) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2011.03.004 · Cited by 356
NICE: Network Intrusion Detection and Countermeasure Selection in Virtual Network Systems
Chung, C.-J., Khatkar, P., Xing, T., Lee, J., & Huang, D. (2013) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2013.8 · Cited by 322
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Hoque, M. S. (2012) · International Journal of Network Security & Its Applications · doi:10.5121/ijnsa.2012.4208 · Cited by 298
A hybrid intrusion detection system design for computer network security
Aydın, M. A., Zaim, A. H., & Ceylan, K. G. (2009) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2008.12.005 · Cited by 288
Guide to Cyber Threat Information Sharing
Johnson, C. S., Badger, M. L., Waltermire, D., Snyder, J., & Skorupka, C. (2016) · doi:10.6028/nist.sp.800-150 · Cited by 293
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Raman, M. R. G., Somu, N., Kirthivasan, K., Liscano, R., & Sriram, V. S. S. (2017) · Knowledge-Based Systems · doi:10.1016/j.knosys.2017.07.005 · Cited by 239
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Othman, S. M., Ba-Alwi, F. M., Alsohybe, N. T., & Al-Hashida, A. Y. (2018) · Journal Of Big Data · doi:10.1186/s40537-018-0145-4 · Cited by 246
A Host-Based Intrusion Detection and Mitigation Framework for Smart Home IoT Using OpenFlow
Nobakht, M., Sivaraman, V., & Boreli, R. (2016) · 2016 11th International Conference on Availability, Reliability and Security (ARES) · doi:10.1109/ares.2016.64 · Cited by 237
Louk, M. H. L., & Tama, B. A. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119030 · Cited by 223
The dark side of the Internet: Attacks, costs and responses
Kim, W. H., Jeong, O., Kim, C., & So, J. (2010) · Information Systems · doi:10.1016/j.is.2010.11.003 · Cited by 215
Swarm intelligence in intrusion detection: A survey
Kolias, C., Kambourakis, G., & Μaragoudakis, M. (2011) · Computers & Security · doi:10.1016/j.cose.2011.08.009 · Cited by 215
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Li, W., Tug, S., Meng, W., & Wang, Y. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.02.064 · Cited by 213
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Han, S., Xie, M., Chen, H., & Ling, Y. (2014) · IEEE Systems Journal · doi:10.1109/jsyst.2013.2257594 · Cited by 203
Anwar, S., Zain, J. M., Zolkipli, M. F., Inayat, Z., Khan, S., Anthony, B., & Chang, V. I. C. (2017) · Algorithms · doi:10.3390/a10020039 · Cited by 190
Sabahi, F., & Movaghar, A. (2008) · International Conference on Systems and Networks Communications · doi:10.1109/icsnc.2008.44 · Cited by 188
Intrusion detection techniques for mobile cloud computing in heterogeneous 5G
Gai, K., Meikang Qiu, A., Tao, L., & Zhu, Y. (2015) · Security and Communication Networks · doi:10.1002/sec.1224 · Cited by 182
Two-tier network anomaly detection model: a machine learning approach
Pajouh, H. H., Dastghaibyfard, G., & Hashemi, S. (2015) · Journal of Intelligent Information Systems · doi:10.1007/s10844-015-0388-x · Cited by 182
Cybersecurity in Big Data Era: From Securing Big Data to Data-Driven Security
Rawat, D. B., Doku, R., & Garuba, M. (2019) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2019.2907247 · Cited by 180
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Awan, M. J., Farooq, U., Babar, H. M. A., Yasin, A., Nobanee, H., Hussain, M., Hakeem, O., & Zain, A. M. (2021) · Sustainability · doi:10.3390/su131910743 · Cited by 178
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Ozkan-Okay, M., Samet, R., Aslan, Ö., & Gupta, D. (2021) · IEEE Access · doi:10.1109/access.2021.3129336 · Cited by 170
Assessing and augmenting SCADA cyber security: A survey of techniques
Nazir, S., Patel, S., & Patel, D. (2017) · Computers & Security · doi:10.1016/j.cose.2017.06.010 · Cited by 167
“A guide [106] to intrusion detection and protection is available by NIST.”
BackgroundMethodLiu, X., Zhu, P., Zhang, Y., & Chen, K. (2015) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2015.2418280 · Cited by 164
Electronic authentication guideline
Burr, W. E., Dodson, D. F., & Polk, W. T. (2004) · doi:10.6028/nist.sp.800-63v1.0 · Cited by 155
A survey of intrusion detection and prevention systems
Patel, A. M., Qassim, Q. S., & Wills, C. (2010) · Information Management & Computer Security · doi:10.1108/09685221011079199 · Cited by 148
A Survey of Intrusion Detection Systems Leveraging Host Data
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AESMOTE: Adversarial Reinforcement Learning With SMOTE for Anomaly Detection
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Li, W., Meng, W., & Au, M. H. (2020) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2020.102631 · Cited by 129
A hybrid Intrusion Detection System based on Sparse autoencoder and Deep Neural Network
Rao, K. N., Rao, K. V., & Prasad Reddy, P. V. G. D. (2021) · Computer Communications · doi:10.1016/j.comcom.2021.08.026 · Cited by 125
The State of the Art in Intrusion Prevention and Detection
Pathan, A. K. (2013) · Auerbach Publications eBooks · doi:10.1201/b16390 · Cited by 124
Intrusion response systems: Foundations, design, and challenges
Inayat, Z., Gani, A., Anuar, N. B., Khan, M. K., & Anwar, S. (2015) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2015.12.006 · Cited by 121
A Survey of Network Attacks on Cyber-Physical Systems
Cao, L., Jiang, X., Zhao, Y., Wang, S., You, D., & Xu, X. (2020) · IEEE Access · doi:10.1109/access.2020.2977423 · Cited by 121
Stouffer, K. A., Falco, J., & Scarfone, K. A. (2011) · doi:10.6028/nist.sp.800-82 · Cited by 118
Self-citationIntrusion detection systems in Internet of Things: A literature review
Santos, L., Rabadao, C. M. D. S., & Gonçalves, R. (2018) · Iberian Conference on Information Systems and Technologies (CISTI) · doi:10.23919/cisti.2018.8399291 · Cited by 117
A survey on intrusion detection and prevention in wireless ad-hoc networks
Khan, K., Mehmood, A., Khan, S., Khan, M. A., Iqbal, Z., & Mashwani, W. K. (2019) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2019.101701 · Cited by 116
Towards Bayesian-Based Trust Management for Insider Attacks in Healthcare Software-Defined Networks
Meng, W., Choo, K. R., Furnell, S., Vasilakos, A. V., & Probst, C. W. (2018) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2018.2815280 · Cited by 115
Deception Techniques in Computer Security
Han, X., Kheir, N., & Balzarotti, D. (2018) · ACM Computing Surveys · doi:10.1145/3214305 · Cited by 121
Alkadi, O., Moustafa, N., & Turnbull, B. (2020) · IEEE Access · doi:10.1109/access.2020.2999715 · Cited by 175
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Sforzin, A., Mármol, F. G., Conti, M., & Bohli, J.-M. (2016) · IEEE Smart World Congress and International Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People (SmartWorld/UIC/ATC/ScalCom/CBDCom/IoP) · doi:10.1109/uic-atc-scalcom-cbdcom-iop-smartworld.2016.0080 · Cited by 112
Meng, W., Li, W., & Kwok, L. F. (2014) · Computers & Security · doi:10.1016/j.cose.2014.02.006 · Cited by 111
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Rakas, S. V. B., Stojanović, M. D., & Marković-Petrović, J. D. (2020) · IEEE Access · doi:10.1109/access.2020.2994961 · Cited by 110
Attacks and countermeasures in the internet of vehicles
Sun, Y., Wu, L., Wu, S., Li, S., Zhang, T., Zhang, L., Xu, J., Xiong, Y., & Cui, X. (2017) · Annals of Telecommunications · doi:10.1007/s12243-016-0551-6 · Cited by 103
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Duque, S., & Omar, M. N. (2015) · Procedia Computer Science · doi:10.1016/j.procs.2015.09.145 · Cited by 99
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Keegan, N., Ji, S.-Y., Chaudhary, A., Concolato, C., Yu, B., & Jeong, D. H. (2016) · Human-centric Computing and Information Sciences · doi:10.1186/s13673-016-0076-z · Cited by 99
Li, W., Meng, W., & Kwok, L. F. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3139052 · Cited by 101
Li, W., Meng, W., Kwok, L. F., & Ip, H. H. S. (2016) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2016.09.014 · Cited by 99
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Kholidy, H. A., & Baiardi, F. (2012) · International Conference on Information Technology: New Generations · doi:10.1109/itng.2012.94 · Cited by 97
Bul’ajoul, W., James, A. E., & Pannu, M. (2014) · Journal of Computer and System Sciences · doi:10.1016/j.jcss.2014.12.012 · Cited by 94
Raman, M. R. G., Somu, N., Kirthivasan, K., & Sriram, V. S. S. (2017) · Neural Networks · doi:10.1016/j.neunet.2017.01.012 · Cited by 93
A fog-based privacy-preserving approach for distributed signature-based intrusion detection
Wang, Y., Meng, W., Li, W., Li, J., Liu, W., & Xiang, Y. (2018) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2018.07.013 · Cited by 93
Alamleh, A., Albahri, O. S., Zaidan, A. A., Albahri, A. S., Alamoodi, A. H., Zaidan, B. B., Qahtan, S., Alsatar, H. A., Al-Samarraay, M. S., & Jasim, A. N. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3167256 · Cited by 93
DoS and DDoS Attack Detection Using Deep Learning and IDS
Shurman, M. M., Khrais, R. M., & Yateem, A. (2020) · The International Arab Journal of Information Technology · doi:10.34028/iajit/17/4a/10 · Cited by 92
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Qureshi, A. S., Khan, A., Shamim, N., & Durad, M. H. (2019) · Neural Computing and Applications · doi:10.1007/s00521-019-04152-6 · Cited by 90
Meng, W., Li, W., & Kwok, L. F. (2015) · Security and Communication Networks · doi:10.1002/sec.1307 · Cited by 90
Online anomaly detection using dimensionality reduction techniques for HTTP log analysis
Juvonen, A., Sipola, T., & Hämäläinen, T. (2015) · Computer Networks · doi:10.1016/j.comnet.2015.07.019 · Cited by 89
Guide to Malware Incident Prevention and Handling for Desktops and Laptops
Souppaya, M., & Scarfone, K. A. (2013) · doi:10.6028/nist.sp.800-83r1 · Cited by 89
Self-citationIntrusion detection in mobile ad hoc networks: techniques, systems, and future challenges
Kumar, S., & Dutta, K. (2016) · Security and Communication Networks · doi:10.1002/sec.1484 · Cited by 85
Abdallah, A. M., Alkaabi, A. S. R. O., Alameri, G. B. N. D., Rafique, S. H., Musa, N. S., & Thangavel, M. (2024) · IEEE Access · doi:10.1109/access.2024.3390844 · Cited by 85
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Bansal, A., & Kaur, S. (2018) · Communications in computer and information science · doi:10.1007/978-981-13-1810-8_37 · Cited by 84
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Meng, W. (2018) · Computer · doi:10.1109/mc.2018.3011034 · Cited by 83
Survey on malware evasion techniques: State of the art and challenges
Marpaung, J. A. P., Sain, M., & Lee, H. J. (2012) · International Conference on Advanced Communication Technology · Cited by 82
Machine Learning for DDoS Attack Detection in Industry 4.0 CPPSs
Saghezchi, F. B., Mantas, G., Violas, M. A., de Oliveira Duarte, A. M., & Rodriguez, J. (2022) · Electronics · doi:10.3390/electronics11040602 · Cited by 81
Security and Privacy in the Internet of Vehicles
Sun, Y., Wu, L., Wu, S., Li, S., Zhang, T., Zhang, L., Xu, J., & Xiong, Y. (2015) · 2015 International Conference on Identification, Information, and Knowledge in the Internet of Things (IIKI) · doi:10.1109/iiki.2015.33 · Cited by 81
Wang, Y., Meng, W., Li, W., Liu, Z., Liu, Y., & Xue, H. (2019) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.5101 · Cited by 81
A systematic review on keystroke dynamics
Pisani, P. H., & Lorena, A. C. (2013) · Journal of the Brazilian Computer Society · doi:10.1007/s13173-013-0117-7 · Cited by 78
Daah, C., Qureshi, A., Awan, I. U., & Konur, S. (2024) · Electronics · doi:10.3390/electronics13050865 · Cited by 96
Host-based misuse intrusion detection using PCA feature extraction and kNN classification algorithms
Serpen, G., & Aghaei, E. (2018) · Intelligent Data Analysis · doi:10.3233/ida-173493 · Cited by 75
Bio-Inspired Feature Selection Algorithms With Their Applications: A Systematic Literature Review
Pham, T. H., & Raahemi, B. (2023) · IEEE Access · doi:10.1109/access.2023.3272556 · Cited by 81
Intrusion Detection System in Cloud Computing: Challenges and opportunities
Mehmood, Y., Shibli, M. A., Habiba, U., & Masood, R. (2013) · National Conference on Information Assurance (NCIA) · doi:10.1109/ncia.2013.6725325 · Cited by 75
Intrusion Detection for Maritime Transportation Systems With Batch Federated Aggregation
Liu, W., Xu, X., Wu, L., Qi, L., Jolfaei, A., Ding, W., & Khosravi, M. R. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3181436 · Cited by 76
Mookerjee, V. S., Mookerjee, R. V., Bensoussan, A., & Yue, W. T. (2011) · Information Systems Research · doi:10.1287/isre.1100.0341 · Cited by 73
Cyber Stealth Attacks in Critical Information Infrastructures
Cazorla, L., Alcaraz, C., & López, J. (2016) · IEEE Systems Journal · doi:10.1109/jsyst.2015.2487684 · Cited by 72
Protecting controlled unclassified information in nonfederal systems and organizations
Ross, R., Pillitteri, V. Y., Dempsey, K. L., Riddle, M., & Guissanie, G. (2020) · doi:10.6028/nist.sp.800-171r2 · Cited by 73
StrideBV: Single chip 400G+ packet classification
Ganegedara, T., & Prasanna, V. K. (2012) · IEEE International Conference on High Performance Switching and Routing (HPSR) · doi:10.1109/hpsr.2012.6260820 · Cited by 69
Cloud Computing Security Threats and Attacks with Their Mitigation Techniques
Naseer, A., Zhiqui, H., & Ali, A. (2017) · International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) · doi:10.1109/cyberc.2017.37 · Cited by 69
A Review of Machine Learning based Anomaly Detection Techniques
Kaur, H., Singh, G., & Minhas, J. (2013) · International Journal of Computer Applications Technology and Research · doi:10.7753/ijcatr0202.1020 · Cited by 69
Deep learning in intrusion detection perspective: Overview and further challenges
Kim, K., & Aminanto, M. E. (2017) · International Workshop on Big Data and Information Security (IWBIS) · doi:10.1109/iwbis.2017.8275095 · Cited by 71
Intrusion Detection and Prevention Systems: An Updated Review
Azeez, N. A., Bada, T. M., Misra, S., Adewumi, A., der Vyver, C. V., & Ahuja, R. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-981-32-9949-8_48 · Cited by 68
Intrusion Detection Based on Federated Learning: A Systematic Review
Hernández-Ramos, J. L., Καρόπουλος, Γ., Chatzoglou, E., Kouliaridis, V., Campos, E. M., González-Vidal, A., & Kambourakis, G. (2025) · ACM Computing Surveys · doi:10.1145/3731596 · Cited by 115
Survey of Current Network Intrusion Detection Techniques
Srivastava, R., & Richhariya, V. (2013) · Journals & Books Hosting (International Knowledge Sharing Platform) · Cited by 68
An Overview of Cyber-Resilient Smart Inverters Based on Practical Attack Models
Ahn, B., Kim, T., Ahmad, S., Mazumder, S. K., Johnson, J. D., Mantooth, H. A., & Farnell, C. (2023) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2023.3342842 · Cited by 67
The challenges of using an intrusion detection system
Werlinger, R., Hawkey, K., Müldner, K., Jaferian, P., & Beznosov, K. (2008) · Symposium On Usable Privacy and Security · doi:10.1145/1408664.1408679 · Cited by 67
A bio-inspired hybrid deep learning model for network intrusion detection
Moizuddin, M., & Jose, M. V. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107894 · Cited by 66
Experimental analysis of denial-of-service attacks on teleoperated robotic systems
Bonaci, T., Yan, J., Herron, J. A., Kohno, T., & Chizeck, H. J. (2015) · ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS) · doi:10.1145/2735960.2735980 · Cited by 66
Metadata
- Authors
- Scarfone, K. A., & Mell, P. M.
- Affiliations
- National Institute of Standards and Technology (US)
- Published
- 2007
- OpenAlex
- W2678934292
- Cited by
- 1,279
- References
- 2
- Citations by year
- 2026: 81 · 2025: 92 · 2024: 64 · 2023: 56 · 2022: 61 · 2021: 63 · 2020: 70 · 2019: 86 · 2018: 77 · 2017: 78 · 2016: 84 · 2015: 76 · 2014: 82 · 2013: 91 · 2012: 71
- Type
- report
- Language
- en
- Open access
- gold
- doi.org
- Licence
- public-domain
- Funders
- U.S. Department of State
- Cisco Systems
- National Institute of Standards and Technology
- Army Research Laboratory
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Hardware and Architecture › Network Packet Processing and Optimization
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Immune Systems Applications
- Keywords
- intrusion detection systems, intrusion prevention systems, network security, cybersecurity, security testing, security standards
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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