The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
A clustering-based method for unsupervised intrusion detections
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
In the citing paper
“Jiang et al. [220] challenge this notion by showcasing that the size of the cluster is not sufficient to detect anomalies and has to be coupled with the distance of the cluster from other clusters, to increase accuracy of detection.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [220]
In the citing paper
“The KDD dataset is used for evaluation and shows that IMM outperforms three related works [131, 139, 363] in terms of detection rate and FP.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [220]
Citations
Cites
Jain, A. K., Murty, M. N., & Flynn, P. J. (1999) · ACM Computing Surveys · doi:10.1145/331499.331504 · Cited by 13,925
Denning, D. E. (1987) · IEEE Transactions on Software Engineering · doi:10.1109/tse.1987.232894 · Cited by 3,608
Hawkins, D. M. (1980) · doi:10.1007/978-94-015-3994-4 · Cited by 2,982
UCI Repository of Machine Learning Databases
Merz, C. J. (1996) · Medical Entomology and Zoology · Cited by 2,363
Discovering cluster-based local outliers
He, Z., Xu, X., & Deng, S. (2003) · Pattern Recognition Letters · doi:10.1016/s0167-8655(03)00003-5 · Cited by 1,060
A framework for constructing features and models for intrusion detection systems
Lee, W., & Stolfo, S. J. (2000) · ACM Transactions on Information and System Security · doi:10.1145/382912.382914 · Cited by 986
Intrusion detection with unlabeled data using clustering
Portnoy, L. (2000) · doi:10.7916/d8mp5904 · Cited by 759
Outlier Detection Using Replicator Neural Networks
Hawkins, S., He, H., Williams, G., & Baxter, R. A. (2002) · Lecture notes in computer science · doi:10.1007/3-540-46145-0_17 · Cited by 750
A Geometric Framework for Unsupervised Anomaly Detection: Detecting Intrusions in Unlabeled Data
Eskin, E., Arnold, A., Prerau, M., Portnoy, L., & Stolfo, S. (2002) · Advances in Information Security · doi:10.1007/978-1-4615-0953-0_4 · Cited by 683
A Geometric Framework for Unsupervised Anomaly Detection: Detecting Intrusions in Unlabeled Data
Eskin, E., Arnold, A. O., Prerau, M. J., Portnoy, L., & Stolfo, S. J. (2002) · doi:10.7916/d8d50tqt · Cited by 607
Research works printed but not linked: 1
- KDD data set, 1999. URL: http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html.
Cited by
Sorted by citations
Deep Learning for Anomaly Detection: A Review
Pang, G., Shen, C., Cao, L., & Hengel, A. V. D. (2022) · ACM Computing Surveys · doi:10.1145/3439950 · Cited by 2,837
Network Anomaly Detection: Methods, Systems and Tools
Bhuyan, M. H., Bhattacharyya, D. K., & Kalita, J. K. (2013) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2013.052213.00046 · Cited by 1,358
Boutaba, R., Salahuddin, M. A., Limam, N., Ayoubi, S., Shahriar, N., Estrada-Solano, F., & Caicedo, O. M. (2018) · Journal of Internet Services and Applications · doi:10.1186/s13174-018-0087-2 · Cited by 1,036
Intrusion detection by machine learning: A review
Tsai, C.-F., Hsu, Y.-F., Lin, C.-Y., & Lin, W.-Y. (2009) · Expert Systems with Applications · doi:10.1016/j.eswa.2009.05.029 · Cited by 969
AdaBoost-Based Algorithm for Network Intrusion Detection
Hu, W., Hu, W., & Maybank, S. J. (2008) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2007.914695 · Cited by 395
An improved K-nearest-neighbor algorithm for text categorization
Jiang, S., Pang, G., Wu, M., & Kuang, L. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.08.040 · Cited by 323
Self-citationOnline Adaboost-Based Parameterized Methods for Dynamic Distributed Network Intrusion Detection
Hu, W., Gao, J. T., Wang, Y., Wu, O., & Maybank, S. J. (2013) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2013.2247592 · Cited by 165
Bian, K., & Priyadarshi, R. (2024) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-024-10110-w · Cited by 153
Data mining-based intrusion detectors
Wu, S.-Y., & Yen, E. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.06.138 · Cited by 127
A multi-step outlier-based anomaly detection approach to network-wide traffic
Bhuyan, M. H., Bhattacharyya, D. K., & Kalita, J. K. (2016) · Information Sciences · doi:10.1016/j.ins.2016.02.023 · Cited by 110
Lee, S., Xu, Z., Li, T., & Yang, Y. (2017) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.11.005 · Cited by 109
Analysis and Study of Incremental K-Means Clustering Algorithm
Chakraborty, S., & Nagwani, N. K. (2011) · Communications in computer and information science · doi:10.1007/978-3-642-22577-2_46 · Cited by 101
Anomaly Detection Methods for Categorical Data
Taha, A., & Hadi, A. S. (2019) · ACM Computing Surveys · doi:10.1145/3312739 · Cited by 107
Gao, J., Chai, S., Zhang, B., & Xia, Y. (2019) · Energies · doi:10.3390/en12071223 · Cited by 92
Nearest neighbors based density peaks approach to intrusion detection
Li, L., Zhang, H., Peng, H., & Yang, Y. (2018) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2018.03.010 · Cited by 74
Network Intrusion Detection Using Clustering and Gradient Boosting
Verma, P., Anwar, S., Khan, S., & Mane, S. B. (2018) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt.2018.8494186 · Cited by 72
Advanced probabilistic approach for network intrusion forecasting and detection
Shin, S., Lee, S. M., Kim, H., & Kim, S. H. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.07.057 · Cited by 71
Locally linear reconstruction for instance-based learning
Kang, P., & Cho, S. (2008) · Pattern Recognition · doi:10.1016/j.patcog.2008.04.009 · Cited by 68
Outlier Detection Techniques for Process Mining Applications
Ghionna, L., Greco, G., Guzzo, A., & Pontieri, L. (2008) · Springer eBooks · doi:10.1007/978-3-540-68123-6_17 · Cited by 67
Amarbayasgalan, T., Pham, V.-H., Theera‐Umpon, N., & Ryu, K. H. (2020) · Symmetry · doi:10.3390/sym12081251 · Cited by 67
Machine Learning Approach to IDS: A Comprehensive Review
Kunal, & Dua, M. (2019) · International Conference on Electronics, Communication and Aerospace Technology (ICECA) · doi:10.1109/iceca.2019.8822120 · Cited by 61
Bostani, H., & Sheikhan, M. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.08.027 · Cited by 57
Anomaly Detection using Unsupervised Methods: Credit Card Fraud Case Study
Rezapour, M. (2019) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2019.0101101 · Cited by 56
A differentiated one-class classification method with applications to intrusion detection
Kang, I.-H., Jeong, M. K., & Kong, D. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.06.033 · Cited by 55
A Synthetic Feature Processing Method for Remaining Useful Life Prediction of Rolling Bearings
Mi, J., Liu, L., Zhuang, Y., Bai, L., & Li, Y. (2022) · IEEE Transactions on Reliability · doi:10.1109/tr.2022.3192526 · Cited by 54
An effective unsupervised network anomaly detection method
Bhuyan, M. H., Bhattacharyya, D. K., & Kalita, J. K. (2012) · International Conference on Advances in Computing, Communications and Informatics (ICACCI) · doi:10.1145/2345396.2345484 · Cited by 48
Introduction to Data Mining and Knowledge Discovery
Chakraborty, S., Islam, S. H., & Samanta, D. (2022) · EAI/Springer Innovations in Communication and Computing · doi:10.1007/978-3-030-93088-2_1 · Cited by 44
Data Mining in Intrusion Detection: A Comparative Study of Methods, Types and Data Sets
Azad, C., & Jha, V. K. (2013) · International Journal of Information Technology and Computer Science · doi:10.5815/ijitcs.2013.08.08 · Cited by 43
An incremental intrusion detection system using a new semi‐supervised stream classification method
Noorbehbahani, F., Fanian, A., Mousavi, R., & Hassannejad, H. (2015) · International Journal of Communication Systems · doi:10.1002/dac.3002 · Cited by 41
Survey of learning methods in intrusion detection systems
Aburomman, A. A., & Reaz, M. B. I. (2016) · International Conference on Advances in Electrical, Electronic and Systems Engineering · doi:10.1109/icaees.2016.7888070 · Cited by 36
Analysis and Study of Incremental DBSCAN Clustering AlgorithmPreprint
Chakraborty, S., & Nagwani, N. K. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1406.4754 · Cited by 33
Anomaly Detection of the Brake Operating Unit on Metro Vehicles Using a One-Class LSTM Autoencoder
Kang, J., Kim, C.-S., Kang, J. W., & Gwak, J. (2021) · Applied Sciences · doi:10.3390/app11199290 · Cited by 35
Evidence gathering for network security and forensics
Divakaran, D. M., Fok, K. W., Nevat, I., & Thing, V. L. L. (2017) · Digital Investigation · doi:10.1016/j.diin.2017.02.001 · Cited by 30
Machine learning and soft computing for ICT security: an overview of current trends
Camastra, F., Ciaramella, A., & Staiano, A. (2011) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-011-0073-z · Cited by 30
Neural projection techniques for the visual inspection of network traffic
Herrero, Á., Corchado, E., Gastaldo, P., & Zunino, R. (2009) · Neurocomputing · doi:10.1016/j.neucom.2008.12.038 · Cited by 29
Intrusion signature creation via clustering anomalies
Hendry, G., & Yang, S. J. (2008) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.775886 · Cited by 28
VISUALIZATION AND CLUSTERING FOR SNMP INTRUSION DETECTION
Sánchez, R. M., Herrero, Á., & Corchado, E. (2013) · Cybernetics & Systems · doi:10.1080/01969722.2013.803903 · Cited by 27
Cascading of C4.5 Decision Tree and Support Vector Machine for Rule Based Intrusion Detection System
Jashan, J., & Bag, M. (2012) · International Journal of Computer Network and Information Security · doi:10.5815/ijcnis.2012.08.02 · Cited by 27
Approach Based Ensemble Methods for Better and Faster Intrusion Detection
Bahri, E., Harbi, N., & Hoa, N. H. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-21323-6_3 · Cited by 26
Towards Ontology-Based Intelligent Model for Intrusion Detection and Prevention
Isaza, G. A., Castillo, A., López, M. M., & Castillo, L. (2009) · Advances in intelligent and soft computing · doi:10.1007/978-3-642-04091-7_14 · Cited by 26
A generalized cluster centroid based classifier for text categorization
Pang, G., & Jiang, S. (2012) · Information Processing & Management · doi:10.1016/j.ipm.2012.10.003 · Cited by 24
Self-citationA self-supervised anomaly detection algorithm with interpretability
Wu, Z., Yang, X. S., Wei, X., Yuan, P., Zhang, Y., & Bai, J. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121539 · Cited by 23
An approach to model building for accelerated cooling process using instance-based learning
Zheng, Y., Li, S., & Wang, X. (2010) · Expert Systems with Applications · doi:10.1016/j.eswa.2010.01.020 · Cited by 24
Network Traffic Anomaly Detection Techniques and Systems
Bhuyan, M. H., Bhattacharyya, D. K., & Kalita, J. K. (2017) · Computer communications and networks · doi:10.1007/978-3-319-65188-0_4 · Cited by 23
An integration of k-means clustering and naïve bayes classifier for Intrusion Detection
Varuna, S., & Natesan, P. (2015) · International Conference on Signal Processing, Communication and Networking (ICSCN) · doi:10.1109/icscn.2015.7219835 · Cited by 23
LDBOD: A novel local distribution based outlier detector
Zhang, Y., Yang, S., & Wang, Y. (2008) · Pattern Recognition Letters · doi:10.1016/j.patrec.2008.01.019 · Cited by 23
A Frontier: Dependable, Reliable and Secure Machine Learning for Network/System Management
Le, D. C., & Zincir-Heywood, N. (2020) · Journal of Network and Systems Management · doi:10.1007/s10922-020-09512-5 · Cited by 23
Intrusion detection using clustering
Bharti, K. K., Shukla, S., & Jain, S. (2010) · International Journal of Computer and Communication Technology · doi:10.47893/ijcct.2010.1052 · Cited by 20
Sun, D., Zhao, H., Yue, H., Zhao, M., Cheng, S., & Han, W. (2017) · IET Intelligent Transport Systems · doi:10.1049/iet-its.2016.0261 · Cited by 20
Smart Attacks Learning Machine Advisor System for Protecting Smart Cities from Smart Threats
Ali, H., Elzeki, O. M., & Elmougy, S. (2022) · Applied Sciences · doi:10.3390/app12136473 · Cited by 19
Robust Distributed Anomaly Detection Using Optimal Weighted One-Class Random Forests
Tsou, Y.-L., Chu, H. M., Li, C., & Yang, S. (2018) · Proceedings · doi:10.1109/icdm.2018.00171 · Cited by 19
Real-Time Computer Network Anomaly Detection Using Machine Learning Techniques
Limthong, K. (2013) · Journal of Advances in Computer Networks · doi:10.7763/jacn.2013.v1.1 · Cited by 18
Machine Learning for Anomaly Detection
Dua, S., & Du, X. (2011) · Auerbach Publications eBooks · doi:10.1201/b10867-9 · Cited by 18
Fast structural ensemble for One-Class Classification
Liu, J., Miao, Q., Sun, Y., Song, J., & Quan, Y. (2016) · Pattern Recognition Letters · doi:10.1016/j.patrec.2016.06.028 · Cited by 17
Network Intrusion Detection System using Apache Storm
Manzoor, M. A., & Morgan, Y. L. (2017) · Advances in Science Technology and Engineering Systems Journal · doi:10.25046/aj0203102 · Cited by 16
Chattopadhyay, M., Sen, R., & Gupta, S. (2018) · AJIS. Australasian journal of information systems/AJIS. Australian journal of information systems/Australian journal of information systems · doi:10.3127/ajis.v22i0.1667 · Cited by 17
Analysis of Machine Learning Techniques for Intrusion Detection System: A Review
AliShah, A., Khiyal, M. S. H., & Awan, M. D. (2015) · International Journal of Computer Applications · doi:10.5120/21047-3678 · Cited by 17
Analysis Of Machine Learning Techniques For Intrusion Detection System: A Review
Shah, A. A., Khiyal, M. S. H., & Awan, M. D. (2015) · doi:10.5281/zenodo.32240 · Cited by 16
TOWARDS AN UNSUPERVISED METHOD FOR NETWORK ANOMALY DETECTION IN LARGE DATASETS
Bhuyan, M. H., Bhattacharyya, D. K., & Kalita, J. K. (2014) · Cited by 15
Multi-Agent-Based Anomaly Intrusion Detection
Renjit, J. A., & Shunmuganathan, K. L. (2011) · Information Security Journal A Global Perspective · doi:10.1080/19393555.2011.589424 · Cited by 15
An Unsupervised Intrusion Detection Method Combined Clustering with Chaos Simulated Annealing
Ni, L., & Zheng, H. (2007) · International Conference on Machine Learning and Cybernetics · doi:10.1109/icmlc.2007.4370702 · Cited by 14
Vasudevan, A. R., & Selvakumar, S. (2016) · Frontiers of Computer Science · doi:10.1007/s11704-015-5116-8 · Cited by 14
Online feature selected semi-supervised decision trees for network intrusion detection
Çataltepe, Z., Ekmekçi, Ü., Cataltepe, T., & Kelebek, I. (2016) · IEEE/IFIP Network Operations and Management Symposium (NOMS) · doi:10.1109/noms.2016.7502965 · Cited by 13
An efficient hybrid deep learning approach for internet security
Ertam, F. (2019) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2019.122492 · Cited by 12
An evaluation of clustering technique over intrusion detection system
Nadiammai, G. V., & Hemalatha, M. (2012) · International Conference on Advances in Computing, Communications and Informatics (ICACCI) · doi:10.1145/2345396.2345565 · Cited by 12
Yu, H., Xin-cun, Y., & Wang, L. (2020) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.4164 · Cited by 10
Packet-Based Intrusion Detection Using Bayesian Topic Models in Mobile Edge Computing
Cao, X., Fu, Y., & Chen, B. (2020) · Security and Communication Networks · doi:10.1155/2020/8860418 · Cited by 9
Fast clustering-based weighted twin support vector regression
Gu, B., Fang, J., Pan, F., & Bai, Z. (2020) · Soft Computing · doi:10.1007/s00500-020-04746-6 · Cited by 9
Unsupervised Learning Model for Real-Time Anomaly Detection in Computer Networks
Limthong, K., Fukuda, K., Ji, Y., & Yamada, S. (2014) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.e97.d.2084 · Cited by 8
An Unsupervised Feature Selection Framework Based on Clustering
Jiang, S., & Wang, L. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-28320-8_29 · Cited by 8
Self-citationFramework of Clustering-Based Outlier Detection
Jiang, S., & Yang, A. (2009) · International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) · doi:10.1109/fskd.2009.94 · Cited by 8
Self-citationClustering extension of MOVICAB-IDS to distinguish intrusions in flow-based data
Sánchez, R. M., Herrero, Á., & Corchado, E. (2016) · Logic Journal of IGPL · doi:10.1093/jigpal/jzw047 · Cited by 8
A Comparative Study of Smart Grid Security Based on Unsupervised Learning and Load Ranking
Paul, S., Haq, M. R., Das, A., & Ni, Z. (2019) · IEEE International Conference on Electro Information Technology · doi:10.1109/eit.2019.8834059 · Cited by 8
Packet Header Anomaly Detection Using Bayesian Topic Models.Preprint
Cao, X., Chen, B., Li, H., & Fu, Y. (2016) · IACR Cryptology ePrint Archive · Cited by 8
Recurrent Autoencoder Ensembles for Brake Operating Unit Anomaly Detection on Metro Vehicles
Kang, J., Kim, C.-S., Kang, J. W., & Gwak, J. (2022) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.023641 · Cited by 7
Efficient Classification Method for Large Dataset
Jiang, S. (2006) · International Conference on Machine Learning and Cybernetics · doi:10.1109/icmlc.2006.258603 · Cited by 7
Self-citationEvaluation of Network Intrusion Detection Using Markov Chain
Brindasri, S., & Saravanan, K. A. (2014) · International Journal on Cybernetics & Informatics · doi:10.5121/ijci.2014.3202 · Cited by 7
A general approach for automating outliers identification in categorical data
Taha, A., & Hadi, A. S. (2013) · IEEE/ACS International Conference on Computer Systems and Applications (AICCSA) · doi:10.1109/aiccsa.2013.6616425 · Cited by 7
Tracking clusters and anomalies in evolving data streams
Guggilam, S., Chandola, V., & Patra, A. (2021) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.11552 · Cited by 7
A Combination Classification Algorithm Based on Outlier Detection and C4.5
Jiang, S., & Yu, W. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-03348-3_50 · Cited by 6
Self-citationA proposed outliers identification algorithm for categorical data sets
Taha, A., & Hegazy, O. (2010) · International Conference on Informatics and Systems · Cited by 10
Exploiting the anomaly detection for high dimensional data using descriptive approach of data mining
Singh, B., Kushwaha, N., & Vyas, O. P. (2013) · International Conference on Computer and Communication Technology · doi:10.1109/iccct.2013.6749614 · Cited by 6
Heterogeneous Univariate Outlier Ensembles in Multidimensional Data
Pang, G., & Cao, L. (2020) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3403934 · Cited by 6
An integrated intelligent paradigm to detect DDoS attack in mobile ad hoc networks
Devi, P. A., & Kannammal, A. (2015) · International Journal of Embedded Systems · doi:10.1504/ijes.2016.073754 · Cited by 6
Barhoom, T., & Matar, R. A. (2015) · IJARCCE · doi:10.17148/ijarcce.2015.4184 · Cited by 5
Scalable real-time health data sensing and analysis enabling collaborative care delivery
Dimitriadis, I., Mavroudopoulos, I., Kyrama, S., Toliopoulos, T., Gounaris, A., Vakali, A. I., Billis, A. S., & Bamidis, P. D. (2022) · Social Network Analysis and Mining · doi:10.1007/s13278-022-00891-y · Cited by 5
Research Intention Towards Incremental Clustering
Chakraborty, S., Islam, S. H., & Samanta, D. (2022) · EAI/Springer Innovations in Communication and Computing · doi:10.1007/978-3-030-93088-2_5 · Cited by 5
Machine Learning for Anomaly Detection
(2011) · Auerbach Publications eBooks · doi:10.1201/b10867-5 · Cited by 5
A semi-supervised learning model for intrusion detection
Jiang, E. P. (2019) · Intelligent Decision Technologies · doi:10.3233/idt-180127 · Cited by 5
Unsupervised feature selection based on clustering
Jiang, S., & Wang, L. (2010) · International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA) · doi:10.1109/bicta.2010.5645319 · Cited by 5
Self-citationAnomaly detection, classification and CEP with ML methods
SCHNEIDER, P. L., & Xhafa, F. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-823818-9.00020-1 · Cited by 6
Data Classification and Incremental Clustering Using Unsupervised Learning
Chakraborty, S., Islam, S. H., & Samanta, D. (2022) · EAI/Springer Innovations in Communication and Computing · doi:10.1007/978-3-030-93088-2_4 · Cited by 4
Xiong, G., & Zhang, M. (2010) · International Conference on Multimedia Information Networking and Security · doi:10.1109/mines.2010.127 · Cited by 4
Detecting Intrusive Behaviors using Swarm-based Fuzzy Clustering Approach
Mishra, D., & Naik, B. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-981-13-0514-6_79 · Cited by 4
A Fuzzy Clustering Approach for Intrusion Detection
Zeng, Q., & Wu, S. (2009) · International Conference on Web Information Systems and Mining (WISM) · doi:10.1109/wism.2009.150 · Cited by 4
Bisen, M., & Dubey, A. (2018) · The SIJ Transactions on Computer Science Engineering & its Applications (CSEA) · doi:10.9756/sijcsea/v6i1/03010040101 · Cited by 4
Yong-xiong, Z., Wang, L., & Yi, L. (2017) · International Journal of Computers and Applications · doi:10.1080/1206212x.2017.1397387 · Cited by 4
Big Data in Network Anomaly Detection
Le, D. C., & Zincir-Heywood, N. (2018) · Encyclopedia of Big Data Technologies · doi:10.1007/978-3-319-63962-8_161-1 · Cited by 4
A clustering-based feature selection via feature separability
Jiang, S., & Wang, L. (2016) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-169022 · Cited by 3
Self-citationFeature Subset Selection Techniques with Machine Learning
Chakraborty, S., Islam, S. H., & Samanta, D. (2022) · EAI/Springer Innovations in Communication and Computing · doi:10.1007/978-3-030-93088-2_7 · Cited by 3
Metadata
- Authors
- Jiang, S., Song, X., Wang, H., Han, J.-J., & Li, Q.-H.
- Affiliations
- Guangdong University of Foreign Studies (CN)
- Wuhan Technical College of Communications (CN)
- Huazhong University of Science and Technology (CN)
- Corresponding author
- Jiang, ShengYi
- Venue
- Pattern Recognition Letters
- Publisher
- Elsevier BV
- Published
- 2006-05
- Volume
- 27
- Issue
- 7
- Pages
- 802-810
- ISSN
- 0167-8655 (ISSN-L)
- OpenAlex
- W2045012776
- Cited by
- 164
- References
- 19
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 3 · 2023: 1 · 2022: 15 · 2021: 8 · 2020: 8 · 2019: 10 · 2018: 10 · 2017: 10 · 2016: 11 · 2015: 14 · 2014: 8 · 2013: 12 · 2012: 10
- FWCI
- 8.91
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Pattern Recognition Letters (publishedVersion)
- Funders
- National Natural Science Foundation of China
- Defense Advanced Research Projects Agency
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Immune Systems Applications
- Physical Sciences › Computer Science › Hardware and Architecture › Network Packet Processing and Optimization
- Keywords
- outlier factor, network security, unknown attack detection, linear time complexity
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