Guide to Intrusion Detection and Prevention Systems (IDPS)
Open accessAbstract
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.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
At the process layer, protection technologies such as firewalls, intrusion detection systems, and role-based access controls are required to align with defined policies to provide concrete safeguards.
“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]
Section 9.1.3 says security policies specify features an IDPS needs to provide, and §8.3.3 describes firewalls as complementary security controls. The copy does not establish a “process layer” requiring firewalls, IDPSs, and role-based access controls collectively to align with policies; in particular, it does not identify role-based access control as part of that framework. (LLM, conf=0.85)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Part of this paper’s reference list could not be split into single entries.
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 · see citing works
Self-citationIngenhoven, C. (2010) · De Gruyter eBooks · doi:10.11129/detail.9783955531713.18 · Cited by 4 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Guide to Industrial Control Systems (ICS) Security
Stouffer, K., Pillitteri, V., Lightman, S., Abrams, M., & Hahn, A. (2015) · doi:10.6028/nist.sp.800-82r2 · Cited by 1,377 · see citing works
A survey of intrusion detection in Internet of Things
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 · see citing works
An empirical comparison of botnet detection methods
García, S., Grill, M., Stiborek, J., & Zunino, A. (2014) · Computers & Security · doi:10.1016/j.cose.2014.05.011 · Cited by 962 · see citing works
A survey of intrusion detection techniques in Cloud
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 · see citing works
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 · see citing works
Aldweesh, A., Derhab, A., & Emam, A. Z. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.105124 · Cited by 676 · see citing works
Fuzziness based semi-supervised learning approach for intrusion detection system
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 · see citing works
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 508 · see citing works
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 494 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
A feature selection algorithm for intrusion detection system based on Pigeon Inspired Optimizer
Alazzam, H., Sharieh, A., & Sabri, K. E. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113249 · Cited by 399 · see citing works
Detecting and Preventing Cyber Insider Threats: A Survey
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 · see citing works
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 · see citing works
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 · see citing works
An Implementation of Intrusion Detection System Using Genetic Algorithm
Hoque, M. S. (2012) · International Journal of Network Security & Its Applications · doi:10.5121/ijnsa.2012.4208 · Cited by 298 · see citing works
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 · see citing works
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 · see citing works
A survey of security issues for cloud computing
Khan, M. A. (2016) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2016.05.010 · Cited by 252 · see citing works
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 · see citing works
Intrusion detection model using machine learning algorithm on Big Data environment
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 232 · see citing works
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 · see citing works
Louk, M. H. L., & Tama, B. A. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119030 · Cited by 223 · see citing works
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 · see citing works
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 210 · see citing works
Designing collaborative blockchained signature-based intrusion detection in IoT environments
Li, W., Tug, S., Meng, W., & Wang, Y. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.02.064 · Cited by 213 · see citing works
Intrusion Detection in Cyber-Physical Systems: Techniques and Challenges
Han, S., Xie, M., Chen, H., & Ling, Y. (2014) · IEEE Systems Journal · doi:10.1109/jsyst.2013.2257594 · Cited by 203 · see citing works
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 · see citing works
Sabahi, F., & Movaghar, A. (2008) · International Conference on Systems and Networks Communications · doi:10.1109/icsnc.2008.44 · Cited by 188 · see citing works
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 · see citing works
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 180 · see citing works
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 · see citing works
Real-Time DDoS Attack Detection System Using Big Data Approach
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 · see citing works
Combining Naive Bayes and Decision Tree for Adaptive Intrusion Detection
Singh, D. M., Harbi, N., & Rahman, M. Z. (2010) · International Journal of Network Security & Its Applications · doi:10.5121/ijnsa.2010.2202 · Cited by 171 · see citing works
A Comprehensive Systematic Literature Review on Intrusion Detection Systems
Ozkan-Okay, M., Samet, R., Aslan, Ö., & Gupta, D. (2021) · IEEE Access · doi:10.1109/access.2021.3129336 · Cited by 170 · see citing works
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 · see citing works
“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 · see citing works
Electronic authentication guideline
Burr, W. E., Dodson, D. F., & Polk, W. T. (2004) · doi:10.6028/nist.sp.800-63v1.0 · Cited by 155 · see citing works
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 · see citing works
A Survey of Intrusion Detection Systems Leveraging Host Data
Bridges, R. A., Glass-Vanderlan, T. R., Iannacone, M. D., Vincent, M. S., & Chen, Q. (2019) · ACM Computing Surveys · doi:10.1145/3344382 · Cited by 143 · see citing works
AESMOTE: Adversarial Reinforcement Learning With SMOTE for Anomaly Detection
Ma, X., & Shi, W. (2020) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2020.3004312 · Cited by 139 · see citing works
A Modified Grey Wolf Optimization Algorithm for an Intrusion Detection System
Alzaqebah, A., Aljarah, I., Al-Kadi, O. S., & Damaševičius, R. (2022) · Mathematics · doi:10.3390/math10060999 · Cited by 135 · see citing works
Li, W., Meng, W., & Au, M. H. (2020) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2020.102631 · Cited by 129 · see citing works
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 · see citing works
The State of the Art in Intrusion Prevention and Detection
Pathan, A. K. (2013) · Auerbach Publications eBooks · doi:10.1201/b16390 · Cited by 124 · see citing works
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 · see citing works
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 · see citing works
Stouffer, K. A., Falco, J., & Scarfone, K. A. (2011) · doi:10.6028/nist.sp.800-82 · Cited by 118 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Deception Techniques in Computer Security
Han, X., Kheir, N., & Balzarotti, D. (2018) · ACM Computing Surveys · doi:10.1145/3214305 · Cited by 121 · see citing works
Alkadi, O., Moustafa, N., & Turnbull, B. (2020) · IEEE Access · doi:10.1109/access.2020.2999715 · Cited by 120 · see citing works
RPiDS: Raspberry Pi IDS — A Fruitful Intrusion Detection System for IoT
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 · see citing works
Meng, W., Li, W., & Kwok, L. F. (2014) · Computers & Security · doi:10.1016/j.cose.2014.02.006 · Cited by 111 · see citing works
A Review of Research Work on Network-Based SCADA Intrusion Detection Systems
Rakas, S. V. B., Stojanović, M. D., & Marković-Petrović, J. D. (2020) · IEEE Access · doi:10.1109/access.2020.2994961 · Cited by 110 · see citing works
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 · see citing works
Using Data Mining Algorithms for Developing a Model for Intrusion Detection System (IDS)
Duque, S., & Omar, M. N. (2015) · Procedia Computer Science · doi:10.1016/j.procs.2015.09.145 · Cited by 99 · see citing works
A survey of cloud-based network intrusion detection analysis
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 · see citing works
Li, W., Meng, W., & Kwok, L. F. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3139052 · Cited by 101 · see citing works
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 97 · see citing works
CIDS: A Framework for Intrusion Detection in Cloud Systems
Kholidy, H. A., & Baiardi, F. (2012) · International Conference on Information Technology: New Generations · doi:10.1109/itng.2012.94 · Cited by 97 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Intrusion detection using deep sparse auto-encoder and self-taught learning
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 · see citing works
Meng, W., Li, W., & Kwok, L. F. (2015) · Security and Communication Networks · doi:10.1002/sec.1307 · Cited by 90 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Extreme Gradient Boosting Based Tuning for Classification in Intrusion Detection Systems
Bansal, A., & Kaur, S. (2018) · Communications in computer and information science · doi:10.1007/978-981-13-1810-8_37 · Cited by 84 · see citing works
Intrusion Detection in the Era of IoT: Building Trust via Traffic Filtering and Sampling
Meng, W. (2018) · Computer · doi:10.1109/mc.2018.3011034 · Cited by 83 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Daah, C., Qureshi, A., Awan, I. U., & Konur, S. (2024) · Electronics · doi:10.3390/electronics13050865 · Cited by 96 · see citing works
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 · see citing works
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 75 · see citing works
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 · see citing works
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 73 · see citing works
Mookerjee, V. S., Mookerjee, R. V., Bensoussan, A., & Yue, W. T. (2011) · Information Systems Research · doi:10.1287/isre.1100.0341 · Cited by 73 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 68 · see citing works
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 · see citing works
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 108 · see citing works
Survey of Current Network Intrusion Detection Techniques
Srivastava, R., & Richhariya, V. (2013) · Journals & Books Hosting (International Knowledge Sharing Platform) · Cited by 68 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
- 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 › Engineering › Biomedical Engineering › Artificial Immune Systems Applications
- Keywords
- intrusion detection systems, intrusion prevention systems, network security, cybersecurity, security testing, security standards
About these checks
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 ›
Are you an author, or do you see a problem with this page? Request a review