The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
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.
Overview
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
Verification
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
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.
Citations
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
Self-citationIngenhoven, C. (2010) · De Gruyter eBooks · doi:10.11129/detail.9783955531713.18 · Cited by 4
Cited by
Sorted by citations
Items 501–600 of 1,000
Li, W., Meng, W., Wang, Y., Kwok, L. F., & Lu, R. (2018) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom/bigdatase.2018.00167 · Cited by 8
Unsupervised Machine Learning for Malicious Network Activities
Hassan, A., Tahir, S., & Baig, A. I. (2019) · International Conference on Applied and Engineering Mathematics (ICAEM) · doi:10.1109/icaem.2019.8853788 · Cited by 8
INTRUSION DETECTION AND PREVENTION FRAMEWORK USING DATA MINING TECHNIQUES FOR FINANCIAL SECTOR
Sharma, G., & Kapil, A. K. (2021) · Acta Informatica Malaysia · doi:10.26480/aim.02.2021.58.61 · Cited by 8
Alhenawi, E., Alazzam, H., Al-Sayyed, R. M. H., AbuAlghanam, O., & Adwan, O. (2022) · Cybernetics and Information Technologies · doi:10.2478/cait-2022-0040 · Cited by 8
Alsubhi, K., Zhani, M. F., & Boutaba, R. (2012) · Globecom · doi:10.1109/glocom.2012.6503227 · Cited by 8
State-of-the-art of cloud computing cyber-security
Bennasar, H., Essaaidi, M., Bendahmane, A., & Ben‐Othman, J. (2015) · World Conference on Complex Systems · doi:10.1109/icocs.2015.7483283 · Cited by 8
An Intrusion Detection System Based on NSGA-II Algorithm
Tamimi, A., Naidu, D. S., & Kavianpour, S. (2015) · International Conference on Cyber Security, Cyber Warfare and Digital Forensic (CyberSec) · doi:10.1109/cybersec.2015.20 · Cited by 8
Flow-based features for a robust intrusion detection system targeting mobile traffic
Elrawy, M. F., Awad, A. I., & Hamed, H. F. A. (2016) · doi:10.1109/ict.2016.7500483 · Cited by 8
An approach for detecting encrypted insider attacks on OpenFlow SDN Networks
Neu, C. V., Zorzo, A. F., Orozco, A. M. S., & Michelin, R. A. (2016) · International Conference for Internet Technology and Secured Transactions · doi:10.1109/icitst.2016.7856698 · Cited by 8
Evaluating intrusion prevention systems with evasions
Särelä, M., Kyöstilä, T., Kiravuo, T., & Manner, J. (2017) · International Journal of Communication Systems · doi:10.1002/dac.3339 · Cited by 8
Abnormality prediction in high dimensional dataset among semi supervised learning approaches
Manghat, A., & Ashok, A. (2017) · International Conference on Advances in Computing, Communications and Informatics (ICACCI) · doi:10.1109/icacci.2017.8126052 · Cited by 8
Adaptive reallocation of cybersecurity analysts to sensors for balancing risk between sensors
Shah, A., Ganesan, R., Jajodia, S., & Çam, H. (2018) · Service Oriented Computing and Applications · doi:10.1007/s11761-018-0235-3 · Cited by 8
Detecting Zero-day Polymorphic Worm: A Review
Sulieman, S. M. A., & Fadlalla, Y. A. (2018) · Saudi Computer Society National Computer Conference (NCC) · doi:10.1109/ncg.2018.8593085 · Cited by 8
Evolutionary Computation Algorithms for Detecting Known and Unknown Attacks
Alyasiri, H., Clark, J. A., & Kudenko⋆, D. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-12942-2_14 · Cited by 8
Bansal, A., & Kaur, S. (2019) · Communications in computer and information science · doi:10.1007/978-981-13-9939-8_39 · Cited by 8
Stream Data Mining and Anomaly Detection
Esmaeili, M., & Arwa, A. (2011) · International Journal of Computer Applications · Cited by 8
Network security modeling with intelligent and complexity analysis
Kijsanayothin, P. (2010) · ThinkTech (Texas Tech University) · Cited by 8
The Role of Technological Approaches in Cyber Security of Autonomous Vehicles
Seyyarer, E., AYATA, F., & Özdem, S. (2025) · ADBA computer science. · doi:10.69882/adba.cs.2025011 · Cited by 7
Improved Algorithm for Intrusion Detection Using Genetic Algorithm and SNORT
Dave, M. H., & Sharma, S. D. (2014) · Cited by 7
TinyAFD: Attack and Fault Detection Framework for Wireless Sensor Networks
Rughinis, R. V., & Gheorghe, L. (2013) · Journal of Control Engineering and Applied Informatics · Cited by 7
A New Intrusion Detection System Based on Convolutional Neural Network
Kamali, A. E., Chougdali, K., & Kobbane, A. (2023) · International Conference on Communications · doi:10.1109/icc45041.2023.10279012 · Cited by 7
Analysing 3G radio network performance with fuzzy methods
Kumpulainen, P. S., Särkioja, M., Kylväjä, M., & Hätönen, K. (2012) · Neurocomputing · doi:10.1016/j.neucom.2012.07.033 · Cited by 7
Multi-agent Intrusion Detection and Prevention System for Cloud Environment
Venkataramana, K., & Padmavathamma, M. (2012) · International Journal of Computer Applications · doi:10.5120/7888-1225 · Cited by 7
Personal Health Information detection in unstructured web documents
Razavi, A. H., & Ghazinour, K. (2013) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms.2013.6627781 · Cited by 7
Mobile Agents with Cryptographic Traces For Intrusion Detection in the Cloud Computing
Idrissi, H., Ennahbaoui, M., Souidi, E. M., & Hajji, S. E. (2015) · Procedia Computer Science · doi:10.1016/j.procs.2015.12.010 · Cited by 7
BRDO: Blockchain Assisted Intrusion Detection Using Optimized Deep Stacked Network
Kumaran, N., & Mohan, J. S. S. (2023) · Cybernetics & Systems · doi:10.1080/01969722.2023.2175153 · Cited by 7
A Risk Assessment Analysis to Enhance the Security of OT WAN with SD-WAN
Abergos, V. J., & Medjek, F. (2024) · Journal of Cybersecurity and Privacy · doi:10.3390/jcp4040042 · Cited by 13
Meng, Y., & Kwok, L. F. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-34601-9_1 · Cited by 7
Intrusion detection and prevention system
Aulakh, T. (2009) · San Jose State University Scholar Works (San Jose State University) · doi:10.31979/etd.8zdj-dmjn
Security configuration management in intrusion detection and prevention systems
Alsubhi, K., Alhazmi, Y. A., Bouabdallah, N., & Boutaba, R. (2012) · International Journal of Security and Networks · doi:10.1504/ijsn.2012.048493 · Cited by 7
Jadidoleslamy, H. (2011) · International Journal of Communications Network and System Sciences · doi:10.4236/ijcns.2011.48064 · Cited by 7
Biological Intrusion Prevention and Self-Healing Model for Network Security
Elsadig, M., & Abduallah, A. (2010) · International Conference on Future Networks · doi:10.1109/icfn.2010.103 · Cited by 7
Alternative Engine to Detect and Block Port Scan Attacks using Virtual Network Environments
Fuertes, W., Zambrano, P., Sánchez, M., & Gamboa, P. (2011) · Cited by 7
Najafian, Z., Aghazarian, V., & Hedayati, A. (2015) · International Journal of Computer and Electrical Engineering · doi:10.17706/ijcee.2015.v7.879 · Cited by 7
Weaknesses, Vulnerabilities And Elusion Strategies Against Intrusion Detection Systems
Jadidoleslamy, H. (2012) · International Journal of Computer Science & Engineering Survey · doi:10.5121/ijcses.2012.3402 · Cited by 7
Pastrana, S. (2014) · Dialnet (Universidad de la Rioja) · Cited by 7
SQLIIDaaS: A SQL Injection Intrusion Detection Framework as a Service for SaaS Providers
Yassin, M., Ould‐Slimane, H., Talhi, C., & Boucheneb, H. (2017) · IEEE International Conference on Cyber Security and Cloud Computing (CSCloud) · doi:10.1109/cscloud.2017.27 · Cited by 7
Securing Infrastructure-as-a-Service Public Clouds Using Security Onion
Mikail, A., & Pranggono, B. (2019) · Applied System Innovation · doi:10.3390/asi2010006 · Cited by 7
A real-time remote IDS testbed for connected vehicles
Zieglmeier, V., Kacianka, S., Hutzelmann, T., & Pretschner, A. (2019) · ACM Symposium on Applied Computing · doi:10.1145/3297280.3297465 · Cited by 8
Shimeall, T. J., & Spring, J. (2013) · Elsevier eBooks · doi:10.1016/b978-1-59749-969-9.00012-2 · Cited by 7
Survey on machine learning algorithms as cloud service for CIDPS
Avdagic, I., & Hajdarevic, K. (2017) · Telecommunications Forum (TELFOR) · doi:10.1109/telfor.2017.8249467 · Cited by 7
Sree, T. R., & Bhanu, S. M. S. (2018) · IET Information Security · doi:10.1049/iet-ifs.2018.5382 · Cited by 7
Simulation Games Platform for Unintentional Perpetrator Attack Vector Identification
Macák, M., Kružíková, A., Daubner, L., & Bühnová, B. (2020) · doi:10.1145/3387940.3391475 · Cited by 7
Data Mining Approach for Intrusion Detection
Azalmad, M., & Fakir, Y. (2021) · Lecture notes in business information processing · doi:10.1007/978-3-030-76508-8_15 · Cited by 7
Fibonacci sequence and EWMA for intrusion forecasting system
Pontes, E., & Zucchi, W. L. (2010) · International Conference on Digital Information Management · doi:10.1109/icdim.2010.5664238 · Cited by 7
TRUSTED CLOUD COMPUTING FRAMEWORK FOR HEALTHCARE SECTOR
Bamiah, M. A. (2014) · Journal of Computer Science · doi:10.3844/jcssp.2014.240.250 · Cited by 7
A Framework for Evaluating Clustering Algorithm
Nehinbe, J. O. (2010) · IEEE International Conference on Cloud Computing Technology and Science · doi:10.1109/cloudcom.2010.90 · Cited by 7
Intrusion Detection Strategies in Smart Grid
Selvam, P. P., Venkatesh, C., Priyadharshini, M. D., & Balamurugan, S. (2020) · doi:10.1002/9781119555759.ch10 · Cited by 7
HTTP Attack Detection using N-gram Analysis
Oza, A. (2013) · doi:10.31979/etd.rbtj-p2jh · Cited by 7
Storm, J.-M., Houmb, S. H., Kaliyar, P., Erdődi, L., & Hagen, J. M. (2023) · Electronics · doi:10.3390/electronics13010060 · Cited by 6
Saidi, A. A., & Khouri, A. O. (2025) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.70186 · Cited by 6
Taxonomy and Survey on Cybersecurity Control Schemes for Smart Grids
Insfrán, A., López‐Pires, F., & Barán, B. (2025) · IET Smart Grid · doi:10.1049/stg2.70038 · Cited by 7
Task Interruptions Undermine Cyber Defense
Vieane, A. Z., Funke, G. J., Greenlee, E. T., Mancuso, V., Borghetti, B. J., Miller, B. T., Menke, L. E., Brown, R. D., Foroughi, C. K., & Boehm‐Davis, D. A. (2017) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/1541931213601576 · Cited by 6
NLP and Neural Networks for Insider Threat Detection
Baghalizadeh-Moghadam, N., Neal, C., Cuppens, F., & Cuppens-Boulahia, N. (2024) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom63139.2024.00279 · Cited by 6
GATransformer: A Network Threat Detection Method Based on Graph-Sequence Enhanced Transformer
Zhu, Q., Zhan, X., Chen, W., Li, Y., Ouyang, H., Jiang, T., & Shen, Y. (2025) · Electronics · doi:10.3390/electronics14193807 · Cited by 6
Knowledge discovery in ontologies
Furletti, B., & Turini, F. (2012) · Intelligent Data Analysis · doi:10.3233/ida-2012-0536 · Cited by 6
Cumbal, R., & Maria, N. (2015) · Asia-Pacific Conference on Computer Aided System Engineering (APCASE) · doi:10.1109/apcase.2015.74 · Cited by 6
A Manual for the Cyber Security Modeling Language
Holm, H., Ekstedt, M., Sommestad, T., & Korman, M. (2013) · Cited by 6
Virtualization Security: Virtual Machine Monitoring and Introspection
Tsifountidis, F. (2011) · Cited by 6
An Outsourcing Model for Alert Analysis in a Cybersecurity Operations Center
Shah, A., Ganesan, R., Jajodia, S., & Çam, H. (2020) · ACM Transactions on the Web · doi:10.1145/3372498 · Cited by 6
Li, W., Tian, F., Li, J., & Xiang, Y. (2020) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.5957 · Cited by 6
Chountasis, S., Pappas, D., & Sklavounos, D. (2020) · ETRI Journal · doi:10.4218/etrij.2019-0476 · Cited by 6
An Intrusion Detection Technique Using Frequency Analysis for Wireless Sensor Network
Choudhary, V., & Taruna, S. (2021) · International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) · doi:10.1109/icccis51004.2021.9397076 · Cited by 6
Kumar, B. V. (2021) · Turkish Journal of Computer and Mathematics Education (TURCOMAT) · Cited by 6
Gunawan, A. R., Sastra, N. P., & Wiharta, D. M. (2021) · Majalah Ilmiah Teknologi Elektro · doi:10.24843/mite.2021.v20i01.p09 · Cited by 6
Distributed Detection of Anomalies in the Network Flow Using Generative Adversarial Networks
Grekov, M., & Sychugov, A. (2022) · International Russian Automation Conference (RusAutoCon) · doi:10.1109/rusautocon54946.2022.9896307 · Cited by 6
Behaviour-Based Cheat Detection in Multiplayer Games with Event-B
Tian, H., Brooke, P. J., & Bosser, A.-G. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-30729-4_15 · Cited by 6
Falsifiability of network security research
Gamayunov, D. (2014) · doi:10.1145/2618137.2618141 · Cited by 6
Online Road Traffic Accident Monitoring System for Nigeria
Williams, K. O., Idowu, A. P., & Olonade, E. (2015) · Transactions on Networks and Communications · doi:10.14738/tnc.31.589 · Cited by 6
Intra-domain IP traceback using OSPF
Castelucio, A. O., Gomes, A. T. A., Ziviani, A., & Salles, R. M. (2009) · Latin American Network Operations and Management Symposium · doi:10.1109/lanoms.2009.5338801 · Cited by 6
Patsos, D., Mitropoulos, S., & Douligeris, C. (2010) · Information Management & Computer Security · doi:10.1108/09685221011079207 · Cited by 6
Oblivious DDoS mitigation with locator/ID separation protocol
Okada, K., Hazeyama, H., & Kadobayashi, Y. (2014) · International Conference on Future Internet Technologies · doi:10.1145/2619287.2619291 · Cited by 6
A novel framework for password securing system from key-logger spyware
Tyagi, G., Ahmad, K., & Doja, M. N. (2014) · International Conference on Issues and Challenges in Intelligent Computing Techniques · doi:10.1109/icicict.2014.6781255 · Cited by 6
Creating a Complete Model of an Intrusion Detection System effective on the LAN
Farhaoui, Y., & Asimi, A. (2012) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2012.030523 · Cited by 6
Gill, K. S., & Sharma, A. (2015) · International Conference on Computing, Communication and Security (ICCCS) · doi:10.1109/cccs.2015.7374153 · Cited by 6
Mohammadzadeh, H. (2012) · International Journal of Information and Electronics Engineering · doi:10.7763/ijiee.2012.v2.89 · Cited by 6
Discovering Attackers Past Behavior to Generate Online Hyper-Alerts
Kawakani, C. T., Barbon, S., Miani, R. S., Cukier, M., & Zarpel�ão, B. B. (2017) · iSys - Brazilian Journal of Information Systems · doi:10.5753/isys.2017.331 · Cited by 6
Madsen, D., Li, W., Meng, W., & Wang, Y. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-05063-4_36 · Cited by 6
Bamou, A., Khardioui, M., Ouadghiri, D. E., & Aghoutane, B. (2019) · Lecture notes in networks and systems · doi:10.1007/978-3-030-33103-0_17 · Cited by 6
Intrusion Detection System Using Deep Learning and Its Application to Wi-Fi Network
Kim, K. (2020) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2019ici0001 · Cited by 7
Maintaining the level of operational effectiveness of a CSOC under adverse conditions
Shah, A., Ganesan, R., Jajodia, S., & Çam, H. (2021) · International Journal of Information Security · doi:10.1007/s10207-021-00573-4 · Cited by 6
Investigation of home router security
Karamanos, E. (2010) · Cited by 6
Automated Technique for Debugging Network Intrusion Detection Systems
Nehinbe, J. O. (2010) · International Conference on Intelligent Systems, Modelling and Simulation · doi:10.1109/isms.2010.72 · Cited by 6
Reducing false rate packet recognition using Dual Counting Bloom Filter
Dodig, I., Sruk, V., & Cafuta, D. (2017) · Telecommunication Systems · doi:10.1007/s11235-017-0375-3 · Cited by 6
Security Issues in the Internet of Vehicles
Gafencu, L., & Scripcariu, L. (2018) · International Conference on Communications (COMM) · doi:10.1109/iccomm.2018.8430112 · Cited by 6
Connected Parking and Automated Valet Parking
Möller, D. P. F., & Haas, R. E. (2019) · Computer communications and networks · doi:10.1007/978-3-319-73512-2_10 · Cited by 6
Towards adaptive false alarm reduction using Cloud as a Service
Meng, Y., Li, W., & Kwok, L. F. (2013) · International Conference on Communications and Networking in China (ChinaCom) · doi:10.1109/chinacom.2013.6694633 · Cited by 6
Morris, B. (2019) · 2019 IEEE AUTOTESTCON · doi:10.1109/autotestcon43700.2019.8961052 · Cited by 5
Zada, I., Omran, E., Jan, S., Alfraihi, H., Alsalamah, S. M., Alshahrani, A., Hayat, S., & Phi, N. (2025) · PLoS ONE · doi:10.1371/journal.pone.0328050 · Cited by 5
RetractedBijlwan, V. P., & Kumar, N. (2026) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2026.3656733 · Cited by 7
Continuous Monitoring System Based on Systems' Environment
Weintraub, E., & Cohen, Y. (2015) · Cited by 5
Architecture of an intelligent Intrusion Detection System for Smart Home
Graf, J., Neubauer, K., Fischer, S., & Hackenberg, R. (2020) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomworkshops48775.2020.9156168 · Cited by 6
HEDL-IDS2: An Innovative Hybrid Ensemble Deep Learning Prototype for Cyber Intrusion Detection
Psathas, A. P., Iliadis, L., Papaleonidas, A., & Pimenidis, E. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-62495-7_15 · Cited by 5
Multicore Flow Processor with Wire-Speed Flow Admission Control
Doo, K.-H. (2012) · ETRI Journal · doi:10.4218/etrij.12.1812.0046 · Cited by 5
A Pay-and-Stay model for tackling intruders in hybrid wireless mesh networks
Pathan, A. K., Abduallah, W. M., Khanam, S., & Saleem, H. Y. (2012) · SIMULATION · doi:10.1177/0037549712467827 · Cited by 5
Network Intrusion Detection System for Denial of Service Attack based on Misuse Detection
Sharma, M. (2011) · Cited by 5
Fu, B., Xiao, Y., & Chen, H. (2017) · ACM Turing Celebration Conference - China (ACM TURC) · doi:10.1145/3063955.3063986 · Cited by 5
A Cooperative Abnormal Behavior Detection Framework Based on Big Data Analytics
Marir, N., & Wang, H. (2017) · Communications in computer and information science · doi:10.1007/978-981-10-6385-5_17 · Cited by 5
Platform Design and Implementation for Flexible Data Processing and Building ML Models of IDS Alerts
Shin, I., Choi, Y., Kwon, T., Lee, H.-R., & Song, J. (2019) · Asia Joint Conference on Information Security (AsiaJCIS) · doi:10.1109/asiajcis.2019.000-4 · Cited by 5
Which Packet Did They Catch? Associating NIDS Alerts with Their Communication Sessions
Ishibashi, R., Goto, H., Han, C., Ban, T., Takahashi, T., & Takeuchi, J. (2021) · Asia Joint Conference on Information Security (AsiaJCIS) · doi:10.1109/asiajcis53848.2021.00012 · Cited by 5
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
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
