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

V-SOC4AS: A Vehicle-SOC for Improving Automotive Security

Barletta, V. S., Caivano, D., Vincentiis, M. D., Ragone, A., Scalera, M., & Serrano, M. (2023) · Algorithms · doi:10.3390/a16020112 · Cited by 30

MVPSys : Toward practical multi-view based false alarm reduction system in network intrusion detection

Li, W., Meng, W., Luo, X., & Kwok, L. F. (2016) · Computers & Security · doi:10.1016/j.cose.2016.04.007 · Cited by 30

A Survey on Cyber Security Evolution and Threats: Biometric Authentication Solutions

Benarous, L., Kadri, B., & Bouridane, A. (2016) · Signal processing for security technologies · doi:10.1007/978-3-319-47301-7_15 · Cited by 30

Salting public traces with attack traffic to test flow classifiers

Celik, Z. B., Raghuram, J., Kesidis, G., & Miller, D. J. (2011) · Cited by 29

Another Fuzzy Anomaly Detection System Based on Ant Clustering Algorithm

Aminanto, M. E., Kim, H., Kim, K.-M., & Kim, K. (2016) · IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences · doi:10.1587/transfun.e100.a.176 · Cited by 29

Review of semi-supervised method for Intrusion Detection System

Fitriani, S., Mandala, S., & Murti, M. A. (2016) · Asia Pacific Conference on Multimedia and Broadcasting (APMediaCast) · doi:10.1109/apmediacast.2016.7878168 · Cited by 29

A comprehensive survey of anomaly detection in banking, wireless sensor networks, social networks, and healthcare

Zamini, M., & Hasheminejad, S. M. H. (2019) · Intelligent Decision Technologies · doi:10.3233/idt-170155 · Cited by 30

Performance Evaluation of Intrusion Detection System using Selected Features and Machine Learning Classifiers

Mahmood, R. A. R., Abdi, A. M., & Hussin, M. B. (2021) · Baghdad Science Journal · doi:10.21123/bsj.2021.18.2(suppl.).0884 · Cited by 40

Insider threats in information security categories and approaches

Elmrabit, N., Yang, S., & Yang, L. (2015) · International Conference on Automation and Computing (ICAC) · doi:10.1109/iconac.2015.7313979 · Cited by 29

Towards adaptive character frequency-based exclusive signature matching scheme and its applications in distributed intrusion detection

Meng, Y., Li, W., & Kwok, L. F. (2013) · Computer Networks · doi:10.1016/j.comnet.2013.08.009 · Cited by 29

Decentralized detection of network attacks through P2P data clustering of SNMP data

Cerroni, W., Moro, G., Pasolini, R., & Ramilli, M. (2015) · Computers & Security · doi:10.1016/j.cose.2015.03.006 · Cited by 29

Behavior-based intrusion detection in encrypted environments

Koch, R. A., Golling, M., & Rodosek, G. D. (2014) · IEEE Communications Magazine · doi:10.1109/mcom.2014.6852093 · Cited by 29

Cyber security challenges: An efficient intrusion detection system design

Kabir, M. F., & Hartmann, S. (2018) · International Young Engineers Forum (YEF-ECE) · doi:10.1109/yef-ece.2018.8368933 · Cited by 29

Learning Based Anomaly Detection in Critical Cyber-Physical Systems

Mozaffari, F. S., Karimipour, H., & Parizi, R. M. (2020) · Springer eBooks · doi:10.1007/978-3-030-45541-5_6 · Cited by 29

Seamless Industry 4.0 Integration: A Multilayered Cyber-Security Framework for Resilient SCADA Deployments in CPPS

Wai, E., & Lee, C. K. M. (2023) · Applied Sciences · doi:10.3390/app132112008 · Cited by 29

An Improved Binary Spider Wasp Optimization Algorithm for Intrusion Detection for Industrial Internet of Things

Shtayat, M. M., Hasan, M. K., Budati, A. K., Solaiman, R., Islam, S., Pandey, B., Abbas, H. S., & Saeed, M. M. A. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3421647 · Cited by 31

An Autoencoder-Enhanced Stacking Neural Network Model for Increasing the Performance of Intrusion Detection

Brunner, C., Kő, A., & Fodor, S. (2021) · Journal of Artificial Intelligence and Soft Computing Research · doi:10.2478/jaiscr-2022-0010 · Cited by 28

Ensemble-based Intrusion Detection for Internet of Things Devices

Danso, P. K., Neto, E. C. P., Dadkhah, S., Zohourian, A., Molyneaux, H., & Ghorbani, A. A. (2022) · IEEE International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT (HONET) · doi:10.1109/honet56683.2022.10019140 · Cited by 28

An Efficient Network Log Anomaly Detection System Using Random Projection Dimensionality Reduction

Juvonen, A., & Hämäläinen, T. (2014) · IFIP International Conference on New Technologies, Mobility and Security (NTMS) · doi:10.1109/ntms.2014.6814006 · Cited by 28

Detection of Virtual Private Network Traffic Using Machine Learning

Miller, S., Curran, K., & Lunney, T. (2020) · International Journal of Wireless Networks and Broadband Technologies · doi:10.4018/ijwnbt.2020070104 · Cited by 27

A Wireless Intrusion Detection System for 802.11 WPA3 Networks

Dalal, N., Akhtar, N., Gupta, A., Karamchandani, N., Kasbekar, G. S., & Parekh, J. (2022) · International Conference on Communication Systems & Networks (COMSNETS) · doi:10.1109/comsnets53615.2022.9668542 · Cited by 27

Comparison of network security tools- Firewall, Intrusion Detection System and Honeypot

Kaur, T., Malhotra, V., & Singh, D. (2014) · Cited by 27

Identifying positioning-based attacks against 3D printed objects and the 3D printing process

Straub, J. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2264671 · Cited by 27

Sec2graph: Network Attack Detection Based on Novelty Detection on Graph Structured Data

Leichtnam, L., Totel, É., Prigent, N., & Mé, L. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-52683-2_12 · Cited by 29

An explainable ensemble-based intrusion detection system for software-defined vehicle ad-hoc networks

Ahsan, S. I., Legg, P., & Alam, S. M. I. (2025) · Cyber Security and Applications · doi:10.1016/j.csa.2025.100090 · Cited by 26

Fuzzy Inference Based Intrusion Detection System: FI-Snort

Naik, N. (2015) · doi:10.1109/cit/iucc/dasc/picom.2015.306 · Cited by 26

A framework for designing resilient distributed intrusion detection systems for critical infrastructures

Genge, B., Haller, P., & Kiss, I. (2016) · International Journal of Critical Infrastructure Protection · doi:10.1016/j.ijcip.2016.06.003 · Cited by 26

Method of Traffic Monitoring for DDoS Attacks Detection in e-Health Systems and Networks.

Zalisky, M., Одарченко, Р. С., Gnatyuk, S. O., Petrova, Y., & Chaplits, A. (2018) · IDDM · Cited by 26

Integrating Software Engineering Processes in the Development of Efficient Intrusion Detection Systems in Wireless Sensor Networks

Almomani, I., & AlRomi, A. N. (2020) · Sensors · doi:10.3390/s20051375 · Cited by 26

A Review on Android Malware: Attacks, Countermeasures and Challenges Ahead

Selvaganapathy, S., Sadasivam, G. S., & Ravi, V. (2021) · Journal of Cyber Security and Mobility · doi:10.13052/jcsm2245-1439.1017 · Cited by 26

A Simple Method for Improving Intrusion Detections in Corporate Networks

Nehinbe, J. O. (2010) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-642-11530-1_13 · Cited by 26

Intrusion detection system based on Software Defined Network firewall

Sayeed, M. A., Sayeed, M. A., & Saxena, S. (2015) · International Conference on Next Generation Computing Technologies · doi:10.1109/ngct.2015.7375145 · Cited by 26

Detection as a service: An SDN application

Monshizadeh, M., Khatri, V., & Kantola, R. (2017) · International Conference on Advanced Communication Technology · doi:10.23919/icact.2017.7890099 · Cited by 26

Network Forensics for Cloud Computing

Gebhardt, T., & Reiser, H. P. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-38541-4_3 · Cited by 26

Innovative Issues and Approaches in Social Sciences

(2020) · Innovative Issues and Approaches in Social Sciences · doi:10.12959/issn.1855-0541.iiass · Cited by 26

Guide to Industrial Control Systems (ICS) Security : Supervisory Control and Data Acquisition (SCADA) Systems, Distributed Control Systems (DCS), and Other Control System Configurations such as Programmable Logic Controllers (PLC)

Stouffer, K. A., Falco, J., & Scarfone, K. A. (2013) · doi:10.6028/nist.sp.800-82r1 · Cited by 26

Are We Ready for the Metaverse? Implications, Legal Landscape, and Recommendations for Responsible Development

Tu, X., & Silva, B. D. C. E. (2025) · Digital Society · doi:10.1007/s44206-025-00163-0 · Cited by 32

Wireless Intrusion Detection System

Boob, S., & Jadhav, P. S. (2010) · International Journal of Computer Applications · doi:10.5120/934-1312 · Cited by 25

Learning combination of anomaly detectors for security domain

Grill, M., & Pevný, T. (2016) · Computer Networks · doi:10.1016/j.comnet.2016.05.021 · Cited by 25

Anomaly-Based Intrusion Detection System for Cyber-Physical System Security

Colelli, R., Magri, F., Panzieri, S., & Pascucci, F. (2021) · Mediterranean Conference on Control and Automation (MED) · doi:10.1109/med51440.2021.9480182 · Cited by 25

SOOA: Exploring Special On-Off Attacks on Challenge-Based Collaborative Intrusion Detection Networks

Li, W., Meng, W., & Kwok, L. F. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-57186-7_30 · Cited by 25

Wireless Smart Home Sensor Networks: Mobile Agent Based Anomaly Detection

Usman, M., Muthukkumarasamy, V., Wu, X.-W., & Khanum, S. (2012) · Autonomic and Trusted Computing · doi:10.1109/uic-atc.2012.145 · Cited by 24

Performance analysis of machine learning techniques in intrusion detection

Kaya, Ç., Yıldız, O., & Ay, S. (2016) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu.2016.7496029 · Cited by 24

An Online Intrusion Detection System to Cloud Computing Based on Neucube Algorithms

Almomani, A., Alauthman, M., Albalas, F. A., Dorgham, O. M., & Obeidat, A. A. (2018) · International Journal of Cloud Applications and Computing · doi:10.4018/ijcac.2018040105 · Cited by 24

IDLP: An Efficient Intrusion Detection and Location-Aware Prevention Mechanism for Network Coding-Enabled Mobile Small Cells

Parsamehr, R., Μαντάς, Γ., Rodrı́guez, J., & Martí­nez-Ortega, J.-F. (2020) · IEEE Access · doi:10.1109/access.2020.2977428 · Cited by 25

Network Intrusion Detection Systems: A Systematic Literature Review o f Hybrid Deep Learning Approaches

Wanjau, S. K., Wambugu, G. M., & Oirere, A. M. (2022) · International Journal of Emerging Science and Engineering · doi:10.35940/ijese.f2530.0610722 · Cited by 24

Anomaly Detection and Mitigation at Internet Scale: A Survey

Steinberger, J., Schehlmann, L., Abt, S., & Baier, H. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-38998-6_7 · Cited by 24

Intrusion Detection and Prevention System: Classification and Quick Review

Beigh, B. M., & Peer, M. A. (2012) · Cited by 24

Enhancing Intelligent Alarm Reduction for Distributed Intrusion Detection Systems via Edge Computing

Meng, W., Wang, Y., Li, W., Liu, Z., Li, J., & Probst, C. W. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-93638-3_44 · Cited by 24

Privacy in Sensor-Driven Human Data Collection: A Guide for PractitionersPreprint

Stopczynski, A., Pietri, R., Pentland, A., Lazer, D. M. J., & Lehmann, S. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1403.5299 · Cited by 38

Development of Rough Set – Hypergraph Technique for Key Feature Identification in Intrusion Detection Systems

Raman, M. R. G., Kirthivasan, K., & Sriram, V. S. S. (2017) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2017.01.006 · Cited by 23

An approach to detecting deliberately introduced defects and micro-defects in 3D printed objects

Straub, J. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2264588 · Cited by 23

Physical security and cyber security issues and human error prevention for 3D printed objects: detecting the use of an incorrect printing material

Straub, J. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2264578 · Cited by 23

Snapshotter: Lightweight intrusion detection and prevention system for industrial control systems

Jin, C., Valizadeh, S., & van Dijk, M. (2018) · IEEE International Conference on Industrial Cyber-Physical Systems (ICPS) · doi:10.1109/icphys.2018.8390813 · Cited by 23

Resilient Control Design for Vehicular Platooning in an Adversarial Environment

Dadras, S., Dadras, S., & Winstead, C. (2019) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.23919/acc.2019.8814721 · Cited by 23

Runtime Monitoring and Dynamic Reconfiguration for Intrusion Detection Systems

Řehák, M., Staab, E., Fusenig, V., Pěchouček, M., Grill, M., Stiborek, J., Bartoš, K., & Engel, T. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04342-0_4 · Cited by 23

Feature Selection Approach for Intrusion Detection System

Kumar, K., Kumar, G., & Kumar, Y. (2013) · Cited by 23

Automated flow-based rule generation for network intrusion detection systems

Fallahi, N., Sami, A., & Tajbakhsh, M. (2016) · Iranian Conference on Electrical Engineering · doi:10.1109/iraniancee.2016.7585840 · Cited by 23

Understanding Tradeoffs Between Throughput, Quality, and Cost of Alert Analysis in a CSOC

Shah, A., Ganesan, R., Jajodia, S., & Çam, H. (2018) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2018.2871744 · Cited by 23

A Comparative Study of Related Technologies of Intrusion Detection & Prevention Systems

Mukhopadhyay, I., Chakraborty, M., & Chakrabarti, S. (2011) · Journal of Information Security · doi:10.4236/jis.2011.21003 · Cited by 23

Autonomic Agent-Based Self-Managed Intrusion Detection and Prevention System.

Patel, A. M., Qassim, Q. S., Shukor, Z., Nogueira, J. H. M., Júnior, J. C., & Wills, C. (2010) · Cited by 23

Developing advanced fingerprint attacks on challenge-based collaborative intrusion detection networks

Li, W., Meng, W., Kwok, L. F., & Ip, H. H. S. (2017) · Cluster Computing · doi:10.1007/s10586-017-0955-8 · Cited by 22

Challenge-based collaborative intrusion detection networks under passive message fingerprint attack: A further analysis

Li, W., & Kwok, L. F. (2019) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2019.03.019 · Cited by 22

Merkle2: A Low-Latency Transparency Log System

Hu, Y., Hooshmand, K., Kalidhindi, H., Yang, S. J., & Popa, R. A. (2021) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp40001.2021.00088 · Cited by 26

Evaluation of Supervised Machine Learning Algorithms for Multi-class Intrusion Detection Systems

Kaddoura, S., Arid, A. E., & Moukhtar, M. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-89912-7_1 · Cited by 22

Adaptive non-critical alarm reduction using hash-based contextual signatures in intrusion detection

Meng, Y., & Kwok, L. F. (2013) · Computer Communications · doi:10.1016/j.comcom.2013.11.001 · Cited by 22

A Survey on SVM Classifiers for Intrusion Detection

RavinderReddy, R., Kavya, B., & Ramadevi, Y. (2014) · International Journal of Computer Applications · doi:10.5120/17294-7779 · Cited by 22

Intrusion detection in connected cars

Haas, R. E., Möller, D. P. F., Bansal, P., Ghosh, R. K., & Bhat, S. S. (2017) · IEEE International Conference on Electro Information Technology · doi:10.1109/eit.2017.8053416 · Cited by 22

Towards Blockchained Challenge-Based Collaborative Intrusion Detection

Li, W., Wang, Y., Li, J., & Au, M. H. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-29729-9_7 · Cited by 22

Enhanced Security Requirements for Protecting Controlled Unclassified Information: A Supplement to NIST Special Publication 800-171

Ross, R., Pillitteri, V. Y., Guissanie, G., WAGNER, R., Graubart, R. D., & Bodeau, D. J. (2021) · doi:10.6028/nist.sp.800-172 · Cited by 33

Intrusion Detection and Tolerance in Next Generation Wireless Network

Ahirwar, D., Shukla, P. K., Bhatele, K. R., Shukla, P. K., & Goyal, S. (2015) · Advances in information security, privacy, and ethics book series · doi:10.4018/978-1-4666-8687-8.ch011 · Cited by 21

Stress and Workload Profiles of Network Analysis: Not All Tasks Are Created Equal

Greenlee, E. T., Funke, G. J., Warm, J. S., Sawyer, B. D., Finomore, V. S., Mancuso, V., Funke, M. E., & Matthews, G. (2016) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-41932-9_13 · Cited by 21

Time Series Analysis: Unsupervised Anomaly Detection Beyond Outlier Detection

Landauer, M., Wurzenberger, M., Skopik, F., Settanni, G., & Peter Filzmoser, A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-99807-7_2 · Cited by 21

Response option for attacks detected by intrusion detection system

Anwar, S., Zain, J. M., Zolkipli, M. F., Inayat, Z., Jaber, A. N., & Odili, J. B. (2015) · International Conference on Software Engineering and Computer Systems · doi:10.1109/icsecs.2015.7333109 · Cited by 21

Secure Communications in Smart Grid: Networking and Protocols

McLaughlin, K., Friedberg, I., Kang, B., Maynard, P., Sezer, S., & McWilliams, G. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-802122-4.00005-5 · Cited by 21

Toward an Efficient Ontology-Based Event Correlation in SIEM

Kenaza, T., & Aiash, M. (2016) · Procedia Computer Science · doi:10.1016/j.procs.2016.04.109 · Cited by 21

An improvement of the state-of-the-art covariance-based methods for statistical anomaly detection algorithms

Fortunati, S., Gini, F., Greco, M., Farina, A., Graziano, A., & Giompapa, S. (2015) · Signal Image and Video Processing · doi:10.1007/s11760-015-0796-y · Cited by 21

Hybrid Intrusion Detection Systems (HIDS) using Fuzzy Logic

Shanmugam, B., & Bashah, N. (2011) · InTech eBooks · doi:10.5772/14130 · Cited by 21

Estimates of success rates of remote arbitrary code execution attacks

Sommestad, T., Holm, H., & Ekstedt, M. (2012) · Information Management & Computer Security · doi:10.1108/09685221211235625 · Cited by 21

Guide to securing legacy IEEE 802.11 wireless networks

Scarfone, K. A., Dicoi, D., Sexton, M., & Tibbs, C. (2008) · doi:10.6028/nist.sp.800-48r1 · Cited by 20

Application of dynamic fuzzy rule interpolation for intrusion detection: D-FRI-Snort

Naik, N., Diao, R., & Shen, Q. (2016) · IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) · doi:10.1109/fuzz-ieee.2016.7737671 · Cited by 20

New Anomaly Network Intrusion Detection System in Cloud Environment Based on Optimized Back Propagation Neural Network Using Improved Genetic Algorithm

Chiba, Z. (2019) · International Journal of Communication Networks and Information Security (IJCNIS) · doi:10.54039/ijcnis.v11i1.3764 · Cited by 20

Malware Correlation Monitoring in Computer Networks of Promising Smart Grids

Kuznetsov, A. A., Кавун, С., Smirnov, O. A., Babenko, V., Nakisko, O., & Kuznetsov, A. A. (2019) · IEEE International Conference on Energy Smart Systems (ESS) · doi:10.1109/ess.2019.8764228 · Cited by 20

Optimizing anomaly-based attack detection using classification machine learning

Gouda, H. A., Ahmed, M. A., & Roushdy, M. I. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-09309-y · Cited by 20

Blockchain-based IoT security solutions for IDS research centers

Hızal, S., Akhter, A. F. M. S., Çavuşoğlu, Ü., & Akgün, D. (2024) · Internet of Things · doi:10.1016/j.iot.2024.101307 · Cited by 20

Classification model of network intrusion using Weighted Extreme Learning Machine

Srimuang, W., & Intarasothonchun, S. (2015) · International Joint Conference on Computer Science and Software Engineering · doi:10.1109/jcsse.2015.7219794 · Cited by 20

A Hierarchical Intrusion Detection Architecture for Wireless Sensor Networks

Jadidoleslamy, H. (2011) · International Journal of Network Security & Its Applications · doi:10.5121/ijnsa.2011.3511 · Cited by 20

An incremental and approximate local outlier probability algorithm for intrusion detection and its evaluation

Hamlet, C., Straub, J., Russell, M. B., & Kerlin, S. D. (2016) · Journal of Cyber Security Technology · doi:10.1080/23742917.2016.1226651 · Cited by 20

Review of Human Decision-making during Computer Security Incident Analysis

Spring, J., & Illari, P. K. (2021) · Digital Threats Research and Practice · doi:10.1145/3427787 · Cited by 24

Studying the Applicability of Intrusion Detection to Multi-Tenant Container Environments

Flora, J., & Antunes, N. (2019) · European Dependable Computing Conference · doi:10.1109/edcc.2019.00033 · Cited by 20

Security and Privacy Controls for Information Systems and OrganizationsPreprint

Joint Task Force Transformation Initiative (2020) · doi:10.6028/nist.sp.800-53r5-draft · Cited by 22

Intrusion detection taxonomy and data preprocessing mechanisms

Al-Utaibi, K. A., & El-Alfy, E.-S. M. (2018) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-169432 · Cited by 19

AI and Security of Critical Infrastructure

Sakhnini, J., Karimipour, H., Dehghantanha, A., & Parizi, R. M. (2020) · Springer eBooks · doi:10.1007/978-3-030-38557-6_2 · Cited by 24

Cloud-based Network Intrusion Detection System using Deep Learning

Archana, Chaitra, H. P., Khushi, K., Nandini, P., Sivaraman, & Honnavalli, P. B. (2021) · International Conference on Arab Women in Computing (ArabWIC) · doi:10.1145/3485557.3485562 · Cited by 19

A Literature Review on Attacks Prevention and Profiling in Cloud Computing

Eddermoug, N., Mansour, A., Azmi, M., Sadik, M., Sabir, E., & Bahassi, H. (2023) · Procedia Computer Science · doi:10.1016/j.procs.2023.03.134 · Cited by 19

Intrusion Detection using Open Source Tools

Timofte, J. (2008) · RePEc: Research Papers in Economics · Cited by 19

Efficient and effective NIDS for cloud virtualization environment

Lin, C.-H., Tien, C., & Pao, H.-K. (2012) · IEEE International Conference on Cloud Computing Technology and Science · doi:10.1109/cloudcom.2012.6427583 · Cited by 19

Anomaly detection based on profile signature in network using machine learning technique

Atefi, K., Yahya, S., Rezaei, A., & Hashim, S. H. B. M. (2016) · IEEE Region 10 Symposium (TENSYMP) · doi:10.1109/tenconspring.2016.7519380 · Cited by 19

3D printing cybersecurity: detecting and preventing attacks that seek to weaken a printed object by changing fill level

Straub, J. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2264575 · Cited by 19

A Host Intrusion Detection System architecture for embedded industrial devices

Martínez, C. V., & Vogel‐Heuser, B. (2019) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2019.03.037 · Cited by 19

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)