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A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection
Abstract
This survey paper describes a focused literature survey of machine learning (ML) and data mining (DM) methods for cyber analytics in support of intrusion detection. Short tutorial descriptions of each ML/DM method are provided. Based on the number of citations or the relevance of an emerging method, papers representing each method were identified, read, and summarized. Because data are so important in ML/DM approaches, some well-known cyber data sets used in ML/DM are described. The complexity of ML/DM algorithms is addressed, discussion of challenges for using ML/DM for cyber security is presented, and some recommendations on when to use a given method are provided.
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“The C4.5 algorithm [200], the ID3 algorithm [199], and the CART algorithm [55] are among the well-known decision tree algorithms used for intrusion detection.”
From Comparative Analysis of Intrusion Detection Systems and Machine Learning Based Model Analysis through Decision Tree (2023) · cites this paper as [55]
In the citing paper
“Though there are various surveys on ML in networking [18,61,82,142,246,339], this survey is purposefully different.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [82]
In the citing paper
“Whereas, Fadlullah et al. [142] and Buczak et al. [82], both state-of-theart surveys, have a specialized treatment of ML to specific problems in networking.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [82]
Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Zadeh, L. A. (1965) · Information and Control · doi:10.1016/s0019-9958(65)90241-x · Cited by 67,855
Kennedy, J., & Eberhart, R. (2002) · Proceedings of ICNN'95 - International Conference on Neural Networks · doi:10.1109/icnn.1995.488968 · Cited by 58,192
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
Zadeh, L. A. (1996) · Cited by 46,960
The Nature of Statistical Learning Theory
Vapnik, V. (2000) · Statistics for Engineering and Information Science · doi:10.1007/978-1-4757-3264-1 · Cited by 46,387
Collective dynamics of ‘small-world’ networks
Watts, D. J., & Strogatz, S. H. (1998) · Nature · doi:10.1038/30918 · Cited by 43,060
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
Data Mining: Practical Machine Learning Tools and Techniques
Witten, I. H., Frank, E., & Hall, M. A. (2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,543
(1992) · Cited by 23,431
Flagged first, then by citations
1 of the 119 references couldn’t be matched to a known paper.
Research works printed but not linked: 14
- Shearer, 2000, The CRISP-DM model: The new blueprint for data mining, J. Data Warehouse., 5, 13
- 2000, R Core Team
- Jacobson, 1989, The Tcpdump Manual Page
- Combs, 2014, Wireshark
- 2014, Sourcefire
- Stolfo, 2014, KDD Cup 1999 Data Set
- 2015, IBM
- 10.4108/wkdd.2008.2686 · DOI
- 2014, Politecnico di Torino, Analyzer 3.0
- Baralis, 2008, Generalized Itemset Discovery by Means of Opportunistic Aggregation
- Markov, 1971, Extension of the limit theorems of probability theory to a sum of variables connected in a chain, Dynamic Probabilistic Systems, 1
- 10.2307/2984875 · DOI
- Brauckhoff, 2008, Flame: A low-level anomaly modeling engine
- Paxson, 2004, Bro 0.9
Cited by
Sorted by citations
Items 901–1,000 of 1,004
Neural Networks Toward Cybersecurity: Domain Map Analysis of State-of-the-Art Challenges
Shevchuk, R., & Martsenyuk, V. (2024) · IEEE Access · doi:10.1109/access.2024.3411632 · Cited by 23
A Privacy-Preserving Graph Neural Network for Network Intrusion Detection
Pei, X., Deng, X., Tian, S., Jiang, P., Zhao, Y., & Xue, K. (2024) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2024.3417853 · Cited by 20
A machine learning approach for cost prediction analysis in environmental governance engineering
Ai, D. M., & Yang, J. (2018) · Neural Computing and Applications · doi:10.1007/s00521-018-3860-z · Cited by 20
A Cascade-structured Meta-Specialists Approach for Neural Network-based Intrusion Detection
Labonne, M., Olivereau, A., Polve, B., et al. (2019) · IEEE Consumer Communications & Networking Conference (CCNC) · doi:10.1109/ccnc.2019.8651856 · Cited by 20
Semi-supervised tri-Adaboost algorithm for network intrusion detection
Yuan, Y., Huo, L., Yuan, Y., et al. (2019) · International Journal of Distributed Sensor Networks · doi:10.1177/1550147719846052 · Cited by 20
“Network intrusion detection systems (IDSs) are often used for monitoring, detecting, and analyzing events which flagged as violating network security policies.(2) In general, IDSs are mainly categorized into two categories: host-based IDSs and networkbased IDSs.”
BackgroundFinding Needles in a Moving Haystack: Prioritizing Alerts with Adversarial Reinforcement Learning
Tong, L., Lászka, Á., Yan, C., Zhang, N., & Vorobeychik, Y. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i01.5442 · Cited by 24
Song, Y., Bu, B., & Zhu, L. (2020) · Electronics · doi:10.3390/electronics9010181 · Cited by 21
“As the anomaly based IDS can be used to detect “zero day” attacks that have not been disclosed before, it has attracted the attention of more and more researchers [10,11].”
BackgroundMethodInfluentialA Machine Learning Approach for Traffic Flow Provisioning in Software Defined Networks
Kumar, S., Bansal, G., & Shekhawat, V. S. (2020) · International Conference on Information Networking · doi:10.1109/icoin48656.2020.9016529 · Cited by 20
Sakthivel, R. K., Nagasubramanian, G., Al-Turjman, F. M., et al. (2020) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.3947 · Cited by 20
Wang, W., Maio, F. D., & Zio, E. (2020) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2020.107007 · Cited by 20
“Methods and algorithms have been developed also for diagnosing cyber attacks [14, 39, 40].”
MethodDetecting a Stealthy Attack in Distributed Control for Microgrids using Machine Learning Algorithms
Ma, M., Lahmadi, A., & Chrisment, I. (2020) · doi:10.1109/icps48405.2020.9274721 · Cited by 20
Deep learning algorithms for cyber security applications: A survey
Li, G., Sharma, P., Pan, L., Rajasegarar, S., Karmakar, C. K., & Patterson, N. (2021) · Journal of Computer Security · doi:10.3233/jcs-200095 · Cited by 26
Yousef, W. A., Traoré, I., & Briguglio, W. (2021) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2021.3125608 · Cited by 20
Chowdhury, R., Sen, S., Roy, A., & Saha, B. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12330-3 · Cited by 20
Real-Time Link Verification in Software-Defined Networks
Soltani, S., Shojafar, M., Mostafaei, H., et al. (2023) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2023.3238691 · Cited by 20
Alsaleh, A. (2023) · Scientific Reports · doi:10.1038/s41598-023-36304-z · Cited by 21
Data-Driven Network Anomaly Detection with Cyber Attack and Defense Visualization
Muhati, E., & Rawat, D. B. (2024) · Journal of Cybersecurity and Privacy · doi:10.3390/jcp4020012 · Cited by 25
“The survey [21] flags issues like sluggish response times during connection look-ups, owing to the sheer volume of TCP data structures housed in vast spans of protected kernel memory.”
BackgroundA training-resistant anomaly detection system
Muller, S., Lancrenon, J., Harpes, C., Traon, Y. L., Gombault, S., & Bonnin, J. (2018) · Computers & Security · doi:10.1016/j.cose.2018.02.015 · Cited by 20
“In the machine-learning domain, some schemes already exist which autonomously tell ‘normal’ data apart from outliers, and which are thus suitable for intrusion detection [8].”
BackgroundML-based cyber incident detection for Electronic Medical Record (EMR) systems
McGlade, D., & Scott-Hayward, S. (2018) · Smart Health · doi:10.1016/j.smhl.2018.05.001 · Cited by 20
“the Confidentiality Prototype to determine the best fit, as recommended in [38].”
BackgroundIntelligent Security Measures for Smart Cyber Physical Systems
Shafique, M., Khalid, F., & Rehman, S. (2018) · Euromicro Conference on Digital System Design (DSD) · doi:10.1109/dsd.2018.00058 · Cited by 20
Performance Analysis of Anomaly Based Network Intrusion Detection Systems
Abedin, M. Z., Siddiquee, K. N. E. A., Bhuyan, M. S., et al. (2018) · IEEE Conference on Local Computer Networks Workshops (LCN Workshops) · doi:10.1109/lcnw.2018.8628599 · Cited by 20
“[19] and Li et al [20] did comprehensive surveys of data mining and machine learning algorithms which are used in IDSs.”
BackgroundAn empirical study of intelligent approaches to DDoS detection in large scale networks
Liang, X., & Znati, T. F. (2019) · 2012 International Conference on Computing, Networking and Communications (ICNC) · doi:10.1109/iccnc.2019.8685519 · Cited by 20
Si, H., Sun, C., Chen, B., Shi, L., & Qiao, H. (2019) · IEEE Access · doi:10.1109/access.2019.2923052 · Cited by 21
KARL: Fast Kernel Aggregation Queries
Chan, T. N., Yiu, M. L., & Leong Hou, U. (2019) · Proceedings - International Conference on Data Engineering · doi:10.1109/icde.2019.00055 · Cited by 20
An interactive visual analytics approach for network anomaly detection through smart labeling
Fan, X., Li, C., Yuan, X., Dong, X., & Liang, J. (2019) · Journal of Visualization · doi:10.1007/s12650-019-00580-7 · Cited by 20
“Despite current research achievements on anomaly detection (Bhuyan et al. 2014; Chandola et al. 2009; Buczak and Guven 2016), there are still some challenges and study directions worth exploring.”
BackgroundLearning paradigms for communication and computing technologies in IoT systems
Ejaz, W., Basharat, M., Saadat, S., Khattak, A. M., Naeem, M., & Anpalagan, A. (2020) · Computer Communications · doi:10.1016/j.comcom.2020.01.043 · Cited by 20
A Study of Ensemble Methods for Cyber Security
Lower, N., & Zhan, F. (2020) · IEEE Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc47524.2020.9031256 · Cited by 20
Trust-Based Authentication for Smart Home Systems
Mbarek, B., Ge, M., & Pitner, T. (2020) · Wireless Personal Communications · doi:10.1007/s11277-020-07965-0 · Cited by 20
“This can be done by faking for example the tag ID or transactions ID [3, 4].”
BackgroundSyrius: Synthesis of Rules for Intrusion Detectors
Alcantara, L., Padilha, G. A. G., Abreu, R., & d’Amorim, M. (2021) · IEEE Transactions on Reliability · doi:10.1109/tr.2021.3061297 · Cited by 20
Adversarial RL-Based IDS for Evolving Data Environment in 6LoWPAN
Pasikhani, A. M., Clark, J. A., & Gope, P. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3214099 · Cited by 20
Comprehensive Study on Smart Solar Grid with Embedded System and IoT Technology
Mahendran, S., Gomathy, B., Sathesh, S., et al. (2023) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt56998.2023.10308033 · Cited by 19
Interpretable Anomaly Detection: A Hybrid Approach Using Rule-Based and Machine Learning Techniques
Mohite, R., & Ouarbya, L. (2024) · IEEE International Conference for Convergence in Technology (I2CT) · doi:10.1109/i2ct61223.2024.10543396 · Cited by 19
A Neuro-Symbolic Artificial Intelligence Network Intrusion Detection System
Bizzarri, A., Jalaian, B. A., Riguzzi, F., et al. (2024) · Proceedings/Proceedings - International Conference on Computer Communications and Networks · doi:10.1109/icccn61486.2024.10637618 · Cited by 19
An Anomaly Detection Model Training Method Based on LLM Knowledge Distillation
Yang, Y., Tian, B., Yu, F., & He, Y. (2024) · International Conference on Networking and Network Applications (NaNA) · doi:10.1109/nana63151.2024.00084 · Cited by 21
Ogab, M., Zaidi, S., Bourouis, A., & Calafate, C. T. (2025) · IEEE Access · doi:10.1109/access.2025.3575236 · Cited by 29
Comprehensive Review of Intrusion Detection Techniques: ML and DL in Different Networks
Rakine, I., Oukaira, A., Guemmat, K. E., Atouf, I., Ouahabi, S., Talea, M., & Bouragba, T. (2025) · IEEE Access · doi:10.1109/access.2025.3579990 · Cited by 20
“It predicts the most accurate result by employing majority voting or weighted voting, based on the predictions of these trees [28].”
Zhang, D., Huang, D., Chen, Y., Lin, S., & Li, C. (2025) · AIMS Electronics and Electrical Engineering · doi:10.3934/electreng.2025017 · Cited by 21
Kumar, A., Dewangan, A. K., Raja, R., Dewangan, A. K., Kumar, M., Soni, A., Agarwal, D., & Saudagar, A. K. J. (2025) · Knowledge and Information Systems · doi:10.1007/s10115-025-02580-6 · Cited by 24
Hu, Y., Zhang, D., Cao, G., & Pan, Q. (2019) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2019.8913895 · Cited by 19
“Tcpdump, Wireshark, Snort, and Nmap are popular programs when using pcap data[12].”
MethodDeep Learning-Based Detection of Fake Task Injection in Mobile Crowdsensing
Sood, A., Şimşek, M., Zhang, Y., et al. (2019) · IEEE Global Conference on Signal and Information Processing · doi:10.1109/globalsip45357.2019.8969416 · Cited by 19
Multi-Layer Mapping of Cyberspace for Intrusion Detection
Shao, S., Satam, P., Satam, S., et al. (2021) · IEEE/ACS International Conference on Computer Systems and Applications (AICCSA) · doi:10.1109/aiccsa53542.2021.9686823 · Cited by 19
Bi-discriminator GAN for tabular data synthesis
Esmaeilpour, M., Chaalia, N., Abusitta, A. H., Devailly, F.-X., Maazoun, W., & Cardinal, P. (2022) · Pattern Recognition Letters · doi:10.1016/j.patrec.2022.05.023 · Cited by 23
Chen, Y., Zhang, J., & Yeo, C. K. (2022) · Information Sciences · doi:10.1016/j.ins.2022.07.104 · Cited by 20
Cyber Attack Detection Dataset: A Review
Yusof, N. N. M., & Sulaiman, N. S. (2022) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2319/1/012029 · Cited by 19
Cybercrime: Identification and Prediction Using Machine Learning Techniques
Veena, K., Meena, K., Kuppusamy, R., et al. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/8237421 · Cited by 19
“There is effective execution of ML procedures in system administration regions such as interruption discovery proposed by Buczak et al. [4] and intellectual radios proposed by Bkassiny et al. [5].”
MethodOn-device context-aware misuse detection framework for heterogeneous IoT edge
Nitish, A., Hanumanthappa, J., Prakash, S. P. S., & Krinkin, K. V. (2022) · Applied Intelligence · doi:10.1007/s10489-022-04039-5 · Cited by 18
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Lu, M., Zhou, B., & Bu, Z. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3263487 · Cited by 19
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Akhther, A. P., Maryposonia, A., & Prasanth, V. S. (2023) · International Conference on Intelligent Computing and Control Systems (ICICCS) · doi:10.1109/iciccs56967.2023.10142805 · Cited by 19
“In order to help with intrusion detection, [20] examine the”
BackgroundNeurosymbolic AI in Cybersecurity: Bridging Pattern Recognition and Symbolic Reasoning
Jalaian, B. A., & Bastian, N. D. (2023) · IEEE Military Communications Conference (MILCOM) · doi:10.1109/milcom58377.2023.10356283 · Cited by 19
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Rana, P., Batra, I., Malik, A., et al. (2024) · IEEE Access · doi:10.1109/access.2024.3424528 · Cited by 19
Leveraging Artificial Intelligence to Enhance Data Security and Combat Cyber Attacks
Weng, Y., & Wu, J. (2024) · Journal of Artificial Intelligence General science (JAIGS) · doi:10.60087/jaigs.v5i1.211 · Cited by 19
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Al-Utaibi, K. A., & El-Alfy, E.-S. M. (2018) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-169432 · Cited by 19
Uncertainty-Aware Authentication Model for Fog Computing in IoT
Heydari, M., Mylonas, A., Katos, V., et al. (2019) · International Conference on Fog and Mobile Edge Computing (FMEC) · doi:10.1109/fmec.2019.8795332 · Cited by 19
Khare, M., & Oak, R. (2019) · Asset analytics · doi:10.1007/978-981-13-8253-6_1 · Cited by 19
On the performance of intelligent techniques for intensive and stealthy DDos detection
Liang, X., & Znati, T. F. (2019) · Computer Networks · doi:10.1016/j.comnet.2019.106906 · Cited by 20
Zhang, F., Hines, J. W., & Coble, J. B. (2019) · Nuclear Technology · doi:10.1080/00295450.2019.1666599 · Cited by 19
“These four classification methods were selected based on a survey of data mining methods for cybersecurity intrusion detection.(18) Intrusion detection results suggest that KNN, bagging, and random forest have true alarm rates higher than 97% with low missed alarm and false alarm rates for man-in-the-middle (MITM) and…”
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Kazato, Y., Nakagawa, Y., & Nakatani, Y. (2020) · IEEE Consumer Communications & Networking Conference (CCNC) · doi:10.1109/ccnc46108.2020.9045113 · Cited by 19
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Bensoussan, A., Mookerjee, V. S., & Yue, W. T. (2020) · Production and Operations Management · doi:10.1111/poms.13198 · Cited by 19
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BackgroundA Comprehensive Survey on Various Machine Learning Methods used for Intrusion Detection System
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IoT Botnet Malware Classification Using Weka Tool and Scikit-learn Machine Learning
Susanto, S., Stiawan, D., Arifin, M. A. S., et al. (2020) · doi:10.23919/eecsi50503.2020.9251304 · Cited by 19
Liu, J., Xiao, K., Luo, L., Li, Y., & Chen, L. (2020) · IEEE International Conference on Software Quality, Reliability and Security (QRS) · doi:10.1109/qrs51102.2020.00028 · Cited by 19
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Ioannou, G., Tagaris, T., Alexandridis, G., & Stafylopatis, A. (2021) · EPJ Web of Conferences · doi:10.1051/epjconf/202124721011 · Cited by 21
Comparing and Analyzing Applications of Intelligent Techniques in Cyberattack Detection
Dixit, P., Kohli, R., Acevedo-Duque, Á., González-Díaz, R. R., & Jhaveri, R. H. (2021) · Security and Communication Networks · doi:10.1155/2021/5561816 · Cited by 19
Amin, H. E., Samhat, A. E., Chamoun, M., et al. (2024) · Journal of Cybersecurity and Privacy · doi:10.3390/jcp4020018 · Cited by 18
“Existing frameworks often struggle to adapt to the dynamic threat landscape, highlighting the importance of considering adversary techniques for proactive risk mitigation [14,52].”
BackgroundEnhancing UAV Network Security: A Human-in-the-Loop and GAN-Based Approach to Intrusion Detection
Zeng, Q., & Naït‐Abdesselam, F. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3545389 · Cited by 21
Addressing Security Orchestration Challenges in Next-Generation Networks: A Comprehensive Overview
Batewela, S., Ranaweera, P. S., Liyanage, M., Zeydan, E., & Ylianttila, M. (2025) · IEEE Open Journal of the Computer Society · doi:10.1109/ojcs.2025.3564788 · Cited by 20
A Network Intrusion Detection Model Based on Convolutional Neural Network
Tao, W., Zhang, W., Hu, C., et al. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-16946-6_63 · Cited by 18
A Deep Random Forest Model on Spark for Network Intrusion Detection
Liu, Z., Su, N., Qin, Y., et al. (2020) · Mobile Information Systems · doi:10.1155/2020/6633252 · Cited by 18
“A recent survey by Buczak and Guven [12] made a comprehensive review of the current data mining and machine learning methodologies of intrusion detection; the survey described the strengths and weaknesses of the algorithm and provided a clear outlook for future work.”
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Martín, P. B. I., & Unceta, I. (2021) · Entropy · doi:10.3390/e23030275 · Cited by 23
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Tayyab, M. A., Marjani, M., Jhanjhi, N. Z., Hashem, I. A. T., Almazroi, A. A., & Almazroi, A. A. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2021.017199 · Cited by 18
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Powell, B. A. (2022) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2022.200106 · Cited by 17
Seh, A. H., Al‐Amri, J. F., Subahi, A. F., Agrawal, A., Pathak, N., Kumar, R., & Khan, R. A. (2021) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2022.018163 · Cited by 18
Akter, M. S., Faruk, M. J. H., Anjum, N., Masum, M., Shahriar, H., Sakib, N., Rahman, A., Wu, F., & Cuzzocrea, A. (2022) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata55660.2022.10020813 · Cited by 20
GLEAM: GAN and LLM for Evasive Adversarial Malware
Devadiga, D., Jin, G., Potdar, B., et al. (2023) · International Conference on Information and Communication Technology Convergence (ICTC) · doi:10.1109/ictc58733.2023.10393706 · Cited by 18
Pant, R., Singh, R., Gehlot, A., Akram, S. V., Gupta, L. R., & Thakur, A. K. (2024) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-024-10116-4 · Cited by 22
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Zhang, W., & Lazaro, J. P. (2024) · International Journal of Emerging Technologies and Advanced Applications · doi:10.62677/ijetaa.2404117 · Cited by 18
Applying Event Stream Processing to Network Online Failure Prediction
Dueñas, J. C., Navarro, J. M., G., H. A. P., et al. (2018) · IEEE Communications Magazine · doi:10.1109/mcom.2018.1601135 · Cited by 18
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Ibor, A. E., Oladeji, F. A., & Okunoye, O. B. (2018) · International Journal of Security and Its Applications · doi:10.14257/ijsia.2018.12.4.02 · Cited by 18
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Masduki, B. W., Ramli, K., & Murfi, H. (2022) · International Journal of Communication Networks and Information Security (IJCNIS) · doi:10.17762/ijcnis.v10i2.3375 · Cited by 18
Learning and Applying Ontology for Machine Learning in Cyber Attack Detection
Zheng, H., Wang, Y., Han, C., et al. (2018) · doi:10.1109/trustcom/bigdatase.2018.00180 · Cited by 18
Intelligent Methods in Digital Forensics: State of the Art
Krivchenkov, A., Mišņevs, B., & Pavlyuk, D. (2019) · Lecture notes in networks and systems · doi:10.1007/978-3-030-12450-2_26 · Cited by 18
Forget the Myth of the Air Gap: Machine Learning for Reliable Intrusion Detection in SCADA Systems
Perez, R. L., Adamsky, F., Soua, R., & Engel, T. (2019) · ICST Transactions on Security and Safety · doi:10.4108/eai.25-1-2019.159348 · Cited by 20
Li, J., Wang, D., & Zhang, M. (2019) · IEEE photonics journal · doi:10.1109/jphot.2019.2936426 · Cited by 18
“Recently, machine learning (ML) has been successfully applied in the optical networks [14]–[19], which is specialized in modeling complicated functional relationships where the underlying principles of physic and mathematics are difficult to be described [20], [21].”
BackgroundModern Machine Learning for Cyber-Defense and Distributed Denial-of-Service Attacks
Paffenroth, R. C., & Zhou, C. (2019) · IEEE Engineering Management Review · doi:10.1109/emr.2019.2950183 · Cited by 20
INTELLIGENT AUTHENTICATION FOR IDENTITY AND ACCESS MANAGEMENT: A REVIEW PAPER
Hadi, I. H. (2019) · Iraqi Journal for Computers and Informatics · doi:10.25195/ijci.v45i1.39 · Cited by 18
Tagaris, T., Ioannou, G., Sdraka, M., Alexandridis, G., & Stafylopatis, A. (2019) · International Conference on Advances in Artificial Intelligence (ICAAI) · doi:10.1145/3369114.3369121 · Cited by 20
“Recently, an increase in anomaly detection methodologies based on machine learning techniques has also been witnessed [2].”
BackgroundA literature review of the current applications of machine learning and their practical implications
Chagas, B. N. R., Viana, J. A. N., Reinhold, O., Lobato, F. M. F., Jacob, A. F. L., & Alt, R. (2020) · Web Intelligence · doi:10.3233/web-200429 · Cited by 18
“Basically, ML consists of three main types of learning: supervised, unsupervised and semi-supervised [23,39,75,92,110].”
Towards Evaluating the Robustness of Deep Intrusion Detection Models in Adversarial Environment
Sriram, S., Simran, K., Vinayakumar, R., et al. (2020) · Communications in computer and information science · doi:10.1007/978-981-15-4825-3_9 · Cited by 18
Deep Learning-Based Network Security Data Sampling and Anomaly Prediction in Future Network
Liu, L., Lin, J., Wang, P., et al. (2020) · Discrete Dynamics in Nature and Society · doi:10.1155/2020/4163825 · Cited by 18
“(e long short-term memory (LSTM) improves the original RNN algorithm [22–24], solves the problem of gradient disappearance or gradient explosion after training of time series modeling, and conducts deep learning through long-term state preservation and forward calculation and uses the back-propagation algorithm to tra…”
BackgroundMalFinder: An Ensemble Learning-based Framework For Malicious Traffic Detection
Rong, C., Gou, G., Cui, M., Xiong, G., Li, Z., & Guo, L. (2020) · IEEE Symposium on Computers and Communications (ISCC) · doi:10.1109/iscc50000.2020.9219609 · Cited by 18
Massaro, A. S., Gargaro, M., Dipierro, G., Galiano, A. M., & Buonopane, S. (2020) · IEEE Access · doi:10.1109/access.2020.3034399 · Cited by 18
ME-Box: A reliable method to detect malicious encrypted traffic
Xu, B., He, G., & Zhu, H. (2021) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2021.102823 · Cited by 18
Proactive trust classification for detection of replication attacks in 6LoWPAN-based IoT
Mbarek, B., Ge, M., & Pitner, T. (2021) · Internet of Things · doi:10.1016/j.iot.2021.100442 · Cited by 18
A Network Intrusion Detection System for Concept Drifting Network Traffic Data
Andresini, G., Appice, A., Loglisci, C. A. A. M. D., Belvedere, V., Redavid, D., & Malerba, D. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-88942-5_9 · Cited by 18
Knowledge-Driven Machine Learning and Applications in Wireless Communications
Li, D., Xu, Y., Zhao, M., Zhu, J., & Zhang, S. (2021) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2021.3128597 · Cited by 22
A Survey of Intrusion Detection Systems Leveraging Host DataPreprint
Glass-Vanderlan, T. R., Iannacone, M. D., Vincent, M. S., Qian, Q., Chen, & Bridges, R. A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.06070 · Cited by 19
Xai-driven black-box adversarial attacks on network intrusion detectors
Okada, S., Jmila, H., Akashi, K., et al. (2025) · International Journal of Information Security · doi:10.1007/s10207-025-01016-0 · Cited by 17
Exploring Adversarial Properties of Insider Threat Detection
Le, D. C., & Zincir-Heywood, N. (2020) · 2020 IEEE Conference on Communications and Network Security (CNS) · doi:10.1109/cns48642.2020.9162254 · Cited by 17
Providing Cyber Security using Artificial Intelligence – A survey
B.S., S., S., N., Kashyap, N., & D.N., S. (2019) · 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC) · doi:10.1109/iccmc.2019.8819719 · Cited by 34
Data Security and Privacy in the Digital Era: Challenges for Modern Government
Sarjito, A. (2024) · JIAN (Jurnal Ilmiah Administrasi Negara) · doi:10.56071/jian.v8i3.933 · Cited by 9
Metadata
- Authors
- Buczak, A. L., & Guven, E.
- Affiliations
- Johns Hopkins University (US)
- ORCID
- Buczak, Anna L.
- Venue
- IEEE Communications Surveys & Tutorials
- Published
- 2016
- Volume
- 18
- Issue
- 2
- Pages
- 1153-1176
- ISSN
- 1553-877X (ISSN-L)
- 2373-745X
- OpenAlex
- W2342408547
- Semantic Scholar
- 971766088dfaf63fb55e6f0190b14f28f2c98ad0
- Cited by
- 3,332
- References
- 119
- Citations by year
- 2026: 460 · 2025: 395 · 2024: 297 · 2023: 319 · 2022: 403 · 2021: 447 · 2020: 394 · 2019: 314 · 2018: 217 · 2017: 73 · 2016: 11
- FWCI
- 115.39
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Communications Surveys & Tutorials (publishedVersion)
- Funders
- Department of Homeland Security Network Security Deployment Division (HSSA01-13-C-2709)
- 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, cybersecurity, machine learning, data mining, algorithmic complexity
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