Guide to Intrusion Detection and Prevention Systems (IDPS)

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

Design and Implementation

Ingenhoven, C. (2010) · De Gruyter eBooks · doi:10.11129/detail.9783955531713.18 · Cited by 4

A Survey on the Application of FPGAs for Network Infrastructure Security

Chen, H., Chen, Y., & Summerville, D. H. (2010) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2011.072210.00075 · Cited by 64

Privacy-preserving deep packet inspection in outsourced middleboxes

Yuan, X., Wang, X., Lin, J., & Wang, C. (2016) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom.2016.7524526 · Cited by 64

Toward a blockchain-based framework for challenge-based collaborative intrusion detection

Li, W., Wang, Y., Li, J., & Au, M. H. (2020) · International Journal of Information Security · doi:10.1007/s10207-020-00488-6 · Cited by 64

Preventive Maintenance for Advanced Metering Infrastructure Against Malware Propagation

Guo, Y., Ten, C., Hu, S., & Weaver, W. W. (2015) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2015.2453342 · Cited by 61

Machine Learning for Traffic Analysis: A Review

Alqudah, N., & Yaseen, Q. M. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.03.111 · Cited by 60

The “cyber security via determinism” paradigm for a quantum safe zero trust deterministic internet of things (IoT)

Szymanski, T. H. (2022) · IEEE Access · doi:10.1109/access.2022.3169137 · Cited by 59

Machine Learning for Network Intrusion Detection—A Comparative Study

Lail, M. A., García, A. J., & Olivo, S. (2023) · Future Internet · doi:10.3390/fi15070243 · Cited by 59

A study of methodologies used in intrusion detection and prevention systems (IDPS)

Mudzingwa, D., & Agrawal, R. K. (2012) · IEEE SoutheastCon · doi:10.1109/secon.2012.6197080 · Cited by 59

HIDCC: A hybrid intrusion detection approach in cloud computing

Hatef, M. A., Shaker, V., Jabbarpour, M. R., Jung, J. J., & Zarrabi, H. (2017) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.4171 · Cited by 59

DCDIDP: A Distributed, Collaborative, and Data-driven Intrusion Detection and Prevention Framework for Cloud Computing Environments

Zargar, S. T., Takabi, H., & Joshi, J. (2011) · International Conference on Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom) · doi:10.4108/icst.collaboratecom.2011.247158 · Cited by 56

Feature Ranking in Intrusion Detection Dataset using Combination of Filtering Methods

Karimi, Z., Kashani, M. M. R., & Harounabadi, A. (2013) · International Journal of Computer Applications · doi:10.5120/13478-1164 · Cited by 56

Design of Intrusion Sensitivity-Based Trust Management Model for Collaborative Intrusion Detection Networks

Li, W., Meng, W., & Kwok, L. F. (2014) · IFIP advances in information and communication technology · doi:10.1007/978-3-662-43813-8_5 · Cited by 55

The practice on using machine learning for network anomaly intrusion detection

Meng, Y.-X. (2011) · International Conference on Machine Learning and Cybernetics · doi:10.1109/icmlc.2011.6016798 · Cited by 55

Guide for security-focused configuration management of information systems

Johnson, A., Dempsey, K. L., Ross, R., Gupta, S., & Bailey, D. (2011) · doi:10.6028/nist.sp.800-128-2011 · Cited by 55

Effective network intrusion detection using stacking-based ensemble approach

Ali, M. S., Haque, M.-U., Durad, M. H., Usman, A., Mohsin, S. M., Mujlid, H. M., & Maple, C. R. (2023) · International Journal of Information Security · doi:10.1007/s10207-023-00718-7 · Cited by 64

Daily Trends and Origin of Computer-Focused Crimes Against a Large University Computer Network: An Application of the Routine-Activities and Lifestyle Perspective

Maimon, D., Kamerdze, A., Cukier, M., & Sobesto, B. (2013) · The British Journal of Criminology · doi:10.1093/bjc/azs067 · Cited by 54

Enhancing Trust Evaluation Using Intrusion Sensitivity in Collaborative Intrusion Detection Networks: Feasibility and Challenges

Li, W., Meng, Y., & Kwok, L. F. (2013) · International Conference on Computational Intelligence and Security (CIS) · doi:10.1109/cis.2013.115 · Cited by 53

Reducing false positives of network anomaly detection by local adaptive multivariate smoothing

Grill, M., Pevný, T., & Řehák, M. (2016) · Journal of Computer and System Sciences · doi:10.1016/j.jcss.2016.03.007 · Cited by 53

Statistical Perspectives on “Big Data”

Megahed, F. M., & Jones‐Farmer, L. A. (2015) · Springer eBooks · doi:10.1007/978-3-319-12355-4_3 · Cited by 53

Applying a Neural Network Ensemble to Intrusion Detection

Ludwig, S. A. (2019) · Journal of Artificial Intelligence and Soft Computing Research · doi:10.2478/jaiscr-2019-0002 · Cited by 54

An Exhaustive Research on the Application of Intrusion Detection Technology in Computer Network Security in Sensor Networks

Wang, Y., Juan, M., Sharma, A., Singh, P. K., Gaba, G. S., Masud, M., & Baz, M. (2021) · Journal of Sensors · doi:10.1155/2021/5558860 · Cited by 52

Filtering-Based Defense Mechanisms Against DDoS Attacks: A Survey

Kalkan, K., Gür, G., & Alagöz, F. (2016) · IEEE Systems Journal · doi:10.1109/jsyst.2016.2602848 · Cited by 52

Anomaly Detection in Industrial Networks using Machine Learning: A Roadmap

Meshram, A., & Haas, C. S. (2016) · Springer eBooks · doi:10.1007/978-3-662-53806-7_8 · Cited by 52

Enhancing challenge-based collaborative intrusion detection networks against insider attacks using blockchain

Meng, W., Li, W., Yang, L. T., & Li, P. (2019) · International Journal of Information Security · doi:10.1007/s10207-019-00462-x · Cited by 52

Virtual Machine Introspection: Techniques and Applications

Hebbal, Y., Laniepce, S., & Menaud, J.-M. (2015) · International Conference on Availability, Reliability and Security (ARES) · doi:10.1109/ares.2015.43 · Cited by 51

A Game-Theoretic Framework for Robust Optimal Intrusion Detection in Wireless Sensor Networks

Moosavi, H., & Bui, F. M. (2014) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2014.2332816 · Cited by 51

Analysis of intrusion detection in cyber attacks using DEEP learning neural networks

Kumar, P., Kumar, A., Sahayakingsly, C., & Udayakumar, A. S. M. (2020) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-020-00999-y · Cited by 50

DyHAP: Dynamic Hybrid ANFIS-PSO Approach for Predicting Mobile Malware

Afifi, F., Anuar, N. B., Shamshirband, S., & Choo, K. R. (2016) · PLoS ONE · doi:10.1371/journal.pone.0162627 · Cited by 50

Towards Effective Trust-Based Packet Filtering in Collaborative Network Environments

Meng, W., Li, W., & Kwok, L. F. (2017) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2017.2664893 · Cited by 49

Incremental Clustering for Semi-Supervised Anomaly Detection applied on Log Data

Wurzenberger, M., Skopik, F., Landauer, M., Greitbauer, P., Fiedler, R., & Kästner, W. (2017) · International Conference on Availability, Reliability and Security (ARES) · doi:10.1145/3098954.3098973 · Cited by 53

A Hierarchical Intrusion Detection System using Support Vector Machine for SDN Network in Cloud Data Center

Schueller, Q., Basu, K., Younas, M., Patel, M., & Ball, F. (2018) · International Telecommunication Networks and Applications Conference (ITNAC) · doi:10.1109/atnac.2018.8615255 · Cited by 49

IoT Denial-of-Service Attack Detection and Prevention Using Hybrid IDS

Shurman, M. M., Khrais, R. M., & Yateem, A. (2019) · International Arab Conference on Information Technology (ACIT) · doi:10.1109/acit47987.2019.8991097 · Cited by 49

Stateful intrusion detection for IEC 60870-5-104 SCADA security

Yang, Y., McLaughlin, K., Sezer, S., Yuan, Y., & Huang, W. (2014) · IEEE Power Engineering Society General Meeting · doi:10.1109/pesgm.2014.6939218 · Cited by 48

Statistical methods for network surveillance

Jeske, D. R., Stevens, N. T., Tartakovsky, A. G., & Wilson, J. D. (2018) · Applied Stochastic Models in Business and Industry · doi:10.1002/asmb.2326 · Cited by 48

Towards unbalanced multiclass intrusion detection with hybrid sampling methods and ensemble classification

Le, T.-T.-H., Shin, Y., Kim, M., & Kim, H. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.111517 · Cited by 58

A Comprehensive Survey of Machine Learning-Based Network Intrusion Detection

Chapaneri, R., & Shah, S. (2018) · Smart innovation, systems and technologies · doi:10.1007/978-981-13-1921-1_35 · Cited by 47

Finite-horizon semi-Markov game for time-sensitive attack response and probabilistic risk assessment in nuclear power plants

Zhao, Y., Huang, L., Smidts, C. S., & Zhu, Q. (2020) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2020.106878 · Cited by 46

Small Business Information Security: The Fundamentals

Paulsen, C., & Toth, P. (2016) · doi:10.6028/nist.ir.7621r1 · Cited by 50

A survey of intrusion detection systems for cloud computing environment

Chiba, Z., Abghour, N., Moussaid, K., Omri, A. E., & Rida, M. (2016) · International Conference on Engineering & MIS (ICEMIS) · doi:10.1109/icemis.2016.7745295 · Cited by 46

Anomaly Detection for Industrial Control Systems Using K-Means and Convolutional Autoencoder

Chang, C.-P., Hsu, W.-C., & Liao, I. (2019) · International Conference on Software, Telecommunications and Computer Networks (SoftCOM) · doi:10.23919/softcom.2019.8903886 · Cited by 47

A Survey on Forensics and Compliance Auditing for Critical Infrastructure Protection

Henriques, J., Caldeira, F., Cruz, T., & Simões, P. (2024) · IEEE Access · doi:10.1109/access.2023.3348552 · Cited by 58

Adaptive False Alarm Filter Using Machine Learning in Intrusion Detection

Meng, Y., & Kwok, L. F. (2011) · Advances in intelligent and soft computing · doi:10.1007/978-3-642-25658-5_68 · Cited by 45

Guide for security-focused configuration management of information systems

Johnson, A., Dempsey, K. L., Ross, R., Gupta, S., & Bailey, D. (2015) · doi:10.6028/nist.sp.800-128 · Cited by 45

A Collaborative Study of Intrusion Detection and Prevention Techniques in Cloud Computing

Alam, S., Shuaib, M., & Samad, A. (2018) · Lecture notes in networks and systems · doi:10.1007/978-981-13-2324-9_23 · Cited by 44

CBSigIDS: Towards Collaborative Blockchained Signature-Based Intrusion Detection

Tug, S., Meng, W., & Wang, Y. (2018) · IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) · doi:10.1109/cybermatics_2018.2018.00217 · Cited by 46

Practicing a Science of Security

Spring, J., Moore, T., & Pym, D. J. (2017) · New Security Paradigms Workshop · doi:10.1145/3171533.3171540 · Cited by 50

Enhancing collaborative intrusion detection networks using intrusion sensitivity in detecting pollution attacks

Li, W., & Meng, W. (2016) · Information and Computer Security · doi:10.1108/ics-12-2014-0077 · Cited by 42

A Deep Learning‐Based Framework for Feature Extraction and Classification of Intrusion Detection in Networks

Naveed, M., Arif, F., Usman, S. M., Anwar, A., Hadjouni, M., Elmannai, H., Hussain, S., Ullah, S. S., & Umar, F. (2022) · Wireless Communications and Mobile Computing · doi:10.1155/2022/2215852 · Cited by 42

A survey on formal active and passive testing with applications to the cloud

Cavalli, A. R., Teruo Higashino, Y., & Nunez, M. A. (2015) · Annals of Telecommunications · doi:10.1007/s12243-015-0457-8 · Cited by 41

Adaptive blacklist-based packet filter with a statistic-based approach in network intrusion detection

Meng, Y., & Kwok, L. F. (2013) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2013.05.009 · Cited by 41

An Energy Aware Trust Based Intrusion Detection System with Adaptive Acknowledgement for Wireless Sensor Network

Rajeshkumar, G., & Valluvan, K. R. (2016) · Wireless Personal Communications · doi:10.1007/s11277-016-3349-y · Cited by 41

PMFA: Toward Passive Message Fingerprint Attacks on Challenge-Based Collaborative Intrusion Detection Networks

Li, W., Meng, W., Kwok, L. F., & Ip, H. H. S. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46298-1_28 · Cited by 41

Monitoring system reaction in cyber-physical testbed under cyber-attacks

Bernieri, G., Miciolino, E. E., Pascucci, F., & Setola, R. (2017) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2017.02.010 · Cited by 41

Sequence Covering for Efficient Host-Based Intrusion Detection

Marteau, P.-F. (2017) · HAL · Cited by 41

Cost Benefits of Using Machine Learning Features in NIDS for Cyber Security in UK Small Medium Enterprises (SME)

Rawindaran, N., Jayal, A., Prakash, E., & Hewage, C. T. E. R. (2021) · Future Internet · doi:10.3390/fi13080186 · Cited by 41

Apache Spark and MLlib-Based Intrusion Detection System or How the Big Data Technologies Can Secure the Data

Azeroual, O., & Nikiforova, A. (2022) · Information · doi:10.3390/info13020058 · Cited by 41

State-of-the-Art Security Schemes for the Internet of Underwater Things: A Holistic Survey

Adam, N. H., Ali, M., Naeem, F., Ghazy, A. S., & Kaddoum, G. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3474290 · Cited by 40

Signature-based Multi-Layer Distributed Intrusion Detection System using Mobile Agents

Uddin, M., Rehman, A. A., Uddin, N., Memon, J., Alsaqour, R. A., & Kazi, S. (2012) · Cited by 40

Automotive connectivity, cyber attack scenarios and automotive cyber security

Haas, R. E., & Möller, D. P. F. (2017) · IEEE International Conference on Electro Information Technology · doi:10.1109/eit.2017.8053441 · Cited by 40

Survey on Intrusion Detection System Types

Othman, S. M., & T.Alsohybe, F. M. B.-A. N. (2018) · International Journal of Cyber-Security and Digital Forensics · Cited by 40

Feature selection using cloud-based parallel genetic algorithm for intrusion detection data classification

Mehanović, D., Кеčo, D., Kevrić, J., Jukić, S., Miljković, A., & Mašetić, Z. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-05871-5 · Cited by 40

Emerging AI threats in cybercrime: a review of zero-day attacks via machine, deep, and federated learning

Alansary, S. A., Ayyad, S. M., Talaat, F. M., & Saafan, M. M. (2025) · Knowledge and Information Systems · doi:10.1007/s10115-025-02556-6 · Cited by 54

Detection of SSH Brute Force Attacks Using Aggregated Netflow Data

Najafabadi, M. M., Khoshgoftaar, T. M., Calvert, C. L., & Kemp, C. (2015) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2015.20 · Cited by 39

Cloud-Based Intrusion Detection and Response System: Open Research Issues, and Solutions

Inayat, Z., Gani, A., Anuar, N. B., Anwar, S., & Khan, M. K. (2017) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-016-2400-3 · Cited by 39

Resilient and Trustworthy Dynamic Data-driven Application Systems (DDDAS) Services for Crisis Management Environments

Badr, Y., Hariri, S., Al-Nashif, Y. B., & Blasch, E. (2015) · Procedia Computer Science · doi:10.1016/j.procs.2015.05.370 · Cited by 39

Design and Evaluation of Advanced Collusion Attacks on Collaborative Intrusion Detection Networks in Practice

Meng, W., Luo, X., Li, W., & Li, Y. (2016) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom.2016.0176 · Cited by 38

Sequence Covering for Efficient Host-Based Intrusion Detection

Marteau, P.-F. (2018) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2018.2868614 · Cited by 38

A Survey of Intrusion Detection Techniques

Lakshminarayana, D. H., Philips, J., & Tabrizi, N. (2019) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2019.00187 · Cited by 40

Modeling distributed denial of service attack in advanced metering infrastructure

Guo, Y., Ten, C., Hu, S., & Weaver, W. W. (2015) · IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT) · doi:10.1109/isgt.2015.7131828 · Cited by 37

Network Intrusion Detection Using Data Mining and Network Behaviour Analysis

Youssef, A. E., & Emam, A. Z. (2011) · International Journal of Computer Science and Information Technology · doi:10.5121/ijcsit.2011.3607 · Cited by 37

Comparison of Classification Algorithms on ICMPv6-Based DDoS Attacks Detection

Elejla, O. E., Belaton, B., Anbar, M., Alabsi, B. A., & Al-Ani, A. K. (2018) · Lecture notes in electrical engineering · doi:10.1007/978-981-13-2622-6_34 · Cited by 37

Survey on security and privacy issues in cyber physical systems

Nazarenko, A. A., & Safdar, G. A. (2019) · AIMS Electronics and Electrical Engineering · doi:10.3934/electreng.2019.2.111 · Cited by 37

Challenge-based collaborative intrusion detection in software-defined networking: an evaluation

Li, W., Wang, Y., Jin, Z., Yu, K., Li, J., & Xiang, Y. (2020) · Digital Communications and Networks · doi:10.1016/j.dcan.2020.09.003 · Cited by 37

A Scoping Review for Cybersecurity in the Construction Industry

Pargoo, N. S., & Ilbeigi, M. (2022) · Journal of Management in Engineering · doi:10.1061/jmenea.meeng-5034 · Cited by 43

Flexible Network-Based Intrusion Detection and Prevention System on Software-Defined Networks

Le, A. N. T., Dinh, P., Le, H., & Tran, N. C. (2015) · International Conference on Advanced Computing and Applications (ACOMP) · doi:10.1109/acomp.2015.19 · Cited by 36

Intrusion Detection Systems of ICMPv6-based DDoS attacks

Elejla, O. E., Belaton, B., Anbar, M., & Alnajjar, A. Y. (2016) · Neural Computing and Applications · doi:10.1007/s00521-016-2812-8 · Cited by 35

Smart Grid Security : Innovative Solutions for a Modernized Grid

Skopik, F. (2015) · Cited by 35

Increased cyber-biosecurity for DNA synthesis

Puzis, R., Farbiash, D., Brodt, O., Elovici, Y., & Greenbaum, D. S. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-00761-y · Cited by 35

BlockCSDN: Towards Blockchain-Based Collaborative Intrusion Detection in Software Defined Networking

Li, W., Wang, Y., Meng, W., Jin, L. I., & Su, C. (2022) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2021bcp0013 · Cited by 38

N-GAN: a novel anomaly-based network intrusion detection with generative adversarial networks

Iliyasu, A. S., & Deng, H. (2022) · International Journal of Information Technology · doi:10.1007/s41870-022-00910-3 · Cited by 36

A Lightweight Intrusion Detection System for IoT and UAV Using Deep Neural Networks with Knowledge Distillation

Wisanwanichthan, T., & Thammawichai, M. (2025) · Computers · doi:10.3390/computers14070291 · Cited by 51

HTTP attack detection using n-gram analysis

Oza, A., Ross, K., Low, R. M., & Stamp, M. (2014) · Computers & Security · doi:10.1016/j.cose.2014.06.002 · Cited by 34

Using Sensitivity Analysis and Cross-Association for the Design of Intrusion Detection Systems in Industrial Cyber-Physical Systems

Haller, P., & Genge, B. (2017) · IEEE Access · doi:10.1109/access.2017.2703906 · Cited by 34

An authentication protocol based on chaos and zero knowledge proof

Major, W., Buchanan, W. J., & Ahmad, J. E. (2020) · Nonlinear Dynamics · doi:10.1007/s11071-020-05463-3 · Cited by 37

Signature Based Semantic Intrusion Detection System on Cloud

Sangeetha, S. K. B., devi, B. G., Ramya, R., Dharani, M. K., & Sathya, P. (2015) · Advances in intelligent systems and computing · doi:10.1007/978-81-322-2250-7_66 · Cited by 34

Effective Hybrid Intrusion Detection System: A Layered Approach

Tesfahun, A., & Bhaskari, D. L. (2015) · International Journal of Computer Network and Information Security · doi:10.5815/ijcnis.2015.03.05 · Cited by 33

An architecture based on proactive model for security in cloud computing

Srivastava, P., Singh, S. K., Pinto, A. A., Verma, S., Chaurasiya, V. K., & Gupta, R. (2011) · International Conference on Recent Trends in Information Technology · doi:10.1109/icrtit.2011.5972392 · Cited by 33

A Comparative study of machine learning models for Network Intrusion Detection System using UNSW-NB 15 dataset

Disha, R. A., & Waheed, S. (2021) · 2021 International Conference on Electronics, Communications and Information Technology (ICECIT) · doi:10.1109/icecit54077.2021.9641471 · Cited by 33

Designing Network Intrusion and Detection System using signature-based method for protecting OpenStack private cloud

Santoso, B. I., Idrus, M. R. S., & Gunawan, I. P. (2016) · International Annual Engineering Seminar (InAES) · doi:10.1109/inaes.2016.7821908 · Cited by 33

A Novel Intrusion Detection and Prevention Scheme for Network Coding-Enabled Mobile Small Cells

Parsamehr, R., Esfahani, A., Μαντάς, Γ., Radwan, A., Mumtaz, S., Rodrı́guez, J., & Martí­nez-Ortega, J.-F. (2019) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2019.2949153 · Cited by 33

Attack Graph Implementation and Visualization for Cyber Physical Systems

Ibrahim, M., Al-Hindawi, Q., Elhafiz, R., Alsheikh, A., & Alquq, O. (2019) · Processes · doi:10.3390/pr8010012 · Cited by 32

Threat classification model for security information event management focusing on model efficiency

Kim, J.-Y., & Kwon, H.-Y. (2022) · Computers & Security · doi:10.1016/j.cose.2022.102789 · Cited by 40

Defending against malicious peripherals with CinchPreprint

Angel, S., Wahby, R. S., Howald, M., Leners, J. B., Spilo, M., Sun, Z., Blumberg, A. J., & Walfish, M. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.01449 · Cited by 32

A Survey on Intrusion Detection Systems for Cloud Computing Environment

Kumar, U., & Gohil, B. N. (2015) · International Journal of Computer Applications · doi:10.5120/19150-0573 · Cited by 31

Few-Shot Network Intrusion Detection Using Discriminative Representation Learning with Supervised Autoencoder

Iliyasu, A. S., Abdurrahman, U. A., & Zheng, L. (2022) · Applied Sciences · doi:10.3390/app12052351 · Cited by 31

Discriminant Analysis based Feature Selection in KDD Intrusion Dataset

Siva, S., & Ramani, R. G. (2011) · International Journal of Computer Applications · Cited by 31

Effective Anomaly Intrusion Detection System based on Neural Network with Indicator Variable and Rough set Reduction

Sadek, R. A., Soliman, M. S., & Elsayed, H. S. (2013) · Cited by 31

MCGAN: Modified Conditional Generative Adversarial Network (MCGAN) for Class Imbalance Problems in Network Intrusion Detection System

Babu, K. S., & Rao, Y. N. (2023) · Applied Sciences · doi:10.3390/app13042576 · Cited by 30

Big Data in Intrusion Detection Systems and Intrusion Prevention Systems

Wang, L. (2017) · Journal of computer networks · doi:10.12691/jcn-4-1-5 · Cited by 30

Investigating the Influence of Special On–Off Attacks on Challenge-Based Collaborative Intrusion Detection Networks

Li, W., Meng, W., & Kwok, L. F. (2018) · Future Internet · doi:10.3390/fi10010006 · Cited by 30

Building an Information Technology Security Awareness and Training Program

Wilson, M., & Hash, J. (2003) · doi:10.6028/nist.sp.800-50 · Cited by 318

Contingency planning guide for information technology systems :

Swanson, M., Wohl, A., Pope, L., Grance, T., Hash, J., & Thomas, R. (2002) · doi:10.6028/nist.sp.800-34 · Cited by 30

Guide to test, training, and exercise programs for IT plans and capabilities

Grance, T., Nolan, T., Burke, K., Dudley, R., White, G., & Good, T. (2006) · doi:10.6028/nist.sp.800-84 · Cited by 41

Risk management guide for information technology systems :

Stoneburner, G., Goguen, A. Y., & Feringa, A. (2002) · doi:10.6028/nist.sp.800-30 · Cited by 1,357

DRAFT

Dworkin, M., Dworkin, M., Blank, R. M., Secretary, A., Gallagher, P. D., & -f, D. N. S. P. (2011)