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MAWILab: combining diverse anomaly detectors for automated anomaly labeling and performance benchmarking
Abstract
Evaluating anomaly detectors is a crucial task in traffic monitoring made particularly difficult due to the lack of ground truth. The goal of the present article is to assist researchers in the evaluation of detectors by providing them with labeled anomaly traffic traces. We aim at automatically finding anomalies in the MAWI archive using a new methodology that combines different and independent detectors. A key challenge is to compare the alarms raised by these detectors, though they operate at different traffic granularities. The main contribution is to propose a reliable graph-based methodology that combines any anomaly detector outputs. We evaluated four unsupervised combination strategies; the best is the one that is based on dimensionality reduction. The synergy between anomaly detectors permits to detect twice as many anomalies as the most accurate detector, and to reject numerous false positive alarms reported by the detectors. Significant anomalous traffic features are extracted from reported alarms, hence the labels assigned to the MAWI archive are concise. The results on the MAWI traffic are publicly available and updated daily. Also, this approach permits to include the results of upcoming anomaly detectors so as to improve over time the quality and variety of labels.
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“Farid and his team proposed a hybrid intrusion detection system that uses a combination of Naive Bayes and decision tree algorithms, and it results in the detection rate of 99.63 % on the KDD'99 dataset [79].”
From Comparative Analysis of Intrusion Detection Systems and Machine Learning Based Model Analysis through Decision Tree (2023) · cites this paper as [79]
Citations
Cites
Fast unfolding of communities in large networks
Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008) · Journal of Statistical Mechanics: Theory and Experiment · doi:10.1088/1742-5468/2008/10/p10008 · Cited by 21,424
Fortunato, S. (2010) · Physics Reports · doi:10.1016/j.physrep.2009.11.002 · Cited by 11,293
Fast Algorithms for Mining Association Rules in Large Databases
Agrawal, R., & Srikant, R. (1994) · Very Large Data Bases · Cited by 9,454
A detailed analysis of the KDD CUP 99 data set
Tavallaee, M., Bagheri, E., Lu, W., & Ghorbani, A. A. (2009) · 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications · doi:10.1109/cisda.2009.5356528 · Cited by 5,396
Combining Pattern Classifiers: Methods and Algorithms
Bagui, S. (2005) · Technometrics · doi:10.1198/tech.2005.s320 · Cited by 4,364
Kuncheva, L. I. (2004) · Wiley eBooks · doi:10.1002/0471660264 · Cited by 3,007
Testing Intrusion detection systems
McHugh, J. (2000) · ACM Transactions on Privacy and Security · doi:10.1145/382912.382923 · Cited by 1,490
Diagnosing network-wide traffic anomalies
Lakhina, A., Crovella, M., & Diot, C. (2004) · Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication · doi:10.1145/1015467.1015492 · Cited by 1,399
Mining anomalies using traffic feature distributions
Lakhina, A., Crovella, M. E., & Diot, C. (2005) · ACM SIGCOMM Conference (SIGCOMM) · doi:10.1145/1080091.1080118 · Cited by 1,064
Correspondence Analysis Handbook
Benzecri (1992) · CRC Press eBooks · doi:10.1201/9780585363035 · Cited by 928
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HLB: Toward Load-Aware Load Balancing
Yao, Z., Desmouceaux, Y., Cordero-Fuertes, J.-A., Townsley, M., & Clausen, T. (2022) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2022.3177163 · Cited by 18
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Self-citationAhmed, A., Hameed, S., Rafi, M., & Mirza, Q. K. A. (2020) · IEEE Access · doi:10.1109/access.2020.3042905 · Cited by 16
Erlacher, F., & Dressler, F. (2018) · doi:10.1145/3229598.3229601 · Cited by 16
Fast Memory-efficient Extreme Events Prediction in Complex Time series
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Discovering Patterns of Interest in IP Traffic Using Cliques in Bipartite Link Streams
Viard, T., Fournier-S’niehotta, R., Magnien, C., & Matthieu Latapy, B. (2018) · Springer proceedings in complexity · doi:10.1007/978-3-319-73198-8_20 · Cited by 15
Adaptive and Reinforcement Learning Approaches for Online Network Monitoring and Analysis
Wassermann, S., Cuvelier, T., Mulinka, P., & Casas, P. (2020) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2020.3037486 · Cited by 15
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Nguyen, G., Dlugolinský, Š., Tran, V., & García, Á. L. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-09863-z · Cited by 18
Casas, P., D’Alconzo, A., Settanni, G., Fiadino, P., & Skopik, F. (2016) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/2976749.2989069 · Cited by 15
Bridges, R. A., Jamieson, J. D., & Reed, J. W. (2017) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2017.8258031 · Cited by 15
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Emily, H., & Gadepally, V. N. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9053419 · Cited by 15
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Hnamte, V., & Hussain, J. (2021) · International Conference on Electrical, Control and Instrumentation Engineering (ICECIE) · doi:10.1109/icecie52348.2021.9664737 · Cited by 14
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A High-Throughput Hardware Accelerator for Network Entropy Estimation Using Sketches
Soto, J. E., Ubisse, P., Fernández, Y., Hernández, C., & Figueroa, M. E. (2021) · IEEE Access · doi:10.1109/access.2021.3088500 · Cited by 14
A Reminiscent Intrusion Detection Model Based on Deep Autoencoders and Transfer Learning
dos Santos, R. R., Viegas, E. K., & Santin, A. O. (2021) · Globecom · doi:10.1109/globecom46510.2021.9685724 · Cited by 13
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Yadav, C. S., & Gupta, S. (2022) · SN Computer Science · doi:10.1007/s42979-022-01543-w · Cited by 13
HyperTester: High-Performance Network Testing Driven by Programmable Switches
Zhang, D., Zhou, Y., Xi, Z., Wang, Y., Xu, M., & Wu, J. (2021) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2021.3077652 · Cited by 13
ALSR: An adaptive label screening and relearning approach for interval-oriented anomaly detection
Wang, J., Jing, Y., Qi, Q., Feng, T., & Liao, J. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.06.028 · Cited by 13
Network Intrusion Detection: Taxonomy and Machine Learning Applications
Nazir, A., & Khan, R. A. (2020) · Studies in computational intelligence · doi:10.1007/978-3-030-57024-8_1 · Cited by 11
Traffic classification for efficient load balancing in server cluster using deep learning technique
Punitha, V., & Mala, C. (2021) · The Journal of Supercomputing · doi:10.1007/s11227-020-03613-3 · Cited by 12
Profiling internet scanners: Spatiotemporal structures and measurement ethics
Mazel, J., Fontugne, R., & Fukuda, K. (2017) · Network Traffic Measurement and Analysis Conference (TMA) · doi:10.23919/tma.2017.8002909 · Cited by 12
Self-citationData-Driven Network Intrusion Detection: A Taxonomy of Challenges and MethodsPreprint
Chou, D., & Jiang, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.07352 · Cited by 12
On the Potential of BGP Flowspec for DDoS Mitigation at Two Sources
Hinze, N., Nawrocki, M., Jonker, M., Dainotti, A., Schmidt, T. C., & Wählisch, M. (2018) · ACM SIGCOMM Conference Posters and Demos · doi:10.1145/3234200.3234209 · Cited by 11
Generating Labeled Flow Data from MAWILab Traces for Network Intrusion Detection
Kim, J., Sim, C., & Choi, J. (2019) · doi:10.1145/3322798.3329251 · Cited by 11
Fontugne, R., Abry, P., Fukuda, K., Borgnat, P., Mazel, J., Wendt, H., & Veitch, D. (2015) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2015.7179029 · Cited by 11
Self-citationUsing complex network theory for temporal locality in network traffic flows
Wang, J., He, X., Si, S., Hai, Z., Zheng, C., & Yu, H. (2019) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2019.04.203 · Cited by 10
Wang, C., Guan, X., Qin, T., Wang, P., Tao, J., Meng, Y., & Liu, J. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.106192 · Cited by 11
A multi-channel anomaly detection method with feature selection and multi-scale analysis
Huang, L., Ran, J., Wang, W., Yang, T., & Xiang, Y. (2020) · Computer Networks · doi:10.1016/j.comnet.2020.107645 · Cited by 11
A Novel Framework for Generating Personalized Network Datasets for NIDS Based on Traffic Aggregation
Velarde-Alvarado, P., Gonzalez, H., Martínez-Peláez, R., Mena, L. J., Ochoa-Brust, A. M., Moreno-García, E., Félix, V. G., & Ostos, R. (2022) · Sensors · doi:10.3390/s22051847 · Cited by 11
A Hough-transform-based anomaly detector with an adaptive time interval
Fontugne, R., & Fukuda, K. (2011) · ACM Symposium on Applied Computing · doi:10.1145/1982185.1982290 · Cited by 11
Self-citationFekih, R. B., & Jemili, F. (2019) · Smart innovation, systems and technologies · doi:10.1007/978-3-030-21005-2_19 · Cited by 11
Sketch-based multidimensional IDS: A new approach for network anomaly detection
Callegari, C., Casella, A., Giordano, S., Pagano, M., & Pepe, T. (2013) · IEEE Conference on Communications and Network Security · doi:10.1109/cns.2013.6682725 · Cited by 10
Detection and Recognition of Atomic Evasions Against Network Intrusion Detection/Prevention Systems
Jia, J., Chen, K., Chen, J., Zhou, D., & Ma, W. (2019) · IEEE Access · doi:10.1109/access.2019.2925639 · Cited by 10
MATATABI: Multi-layer Threat Analysis Platform with Hadoop
Tazaki, H., Okada, K., Sekiya, Y., & Kadobayashi, Y. (2014) · International Workshop on Building Analysis Datasets and Gathering Experience Returns for Security (BADGERS) · doi:10.1109/badgers.2014.12 · Cited by 10
Miguel-Diez, A., Campazas, A., Álvarez-Aparicio, C., Esteban, G., & Guerrero‐Higueras, Á. M. (2025) · Logic Journal of IGPL · doi:10.1093/jigpal/jzaf020 · Cited by 9
An approach to online network monitoring using clustered patterns
Kim, J., Sim, A., Suh, S. C., & Kim, I. (2017) · 2012 International Conference on Computing, Networking and Communications (ICNC) · doi:10.1109/iccnc.2017.7876207 · Cited by 9
A New Approach to Multivariate Network Traffic Analysis
Kim, J., & Sim, A. (2019) · Journal of Computer Science and Technology · doi:10.1007/s11390-019-1915-y · Cited by 9
Visual comparison of network anomaly detectors with chord diagrams
Mazel, J., Fontugne, R., & Fukuda, K. (2014) · ACM Symposium on Applied Computing · doi:10.1145/2554850.2554886 · Cited by 9
Self-citationCommunity-Based Collaborative Intrusion Detection
Cordero, C. G., Vasilomanolakis, E., Mühlhäuser, M., & Fischer, M. (2015) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-319-28865-9_44 · Cited by 9
Statistical Network Anomaly Detection: An Experimental Study
Callegari, C., Giordano, S., & Pagano, M. (2016) · Communications in computer and information science · doi:10.1007/978-3-319-48021-3_2 · Cited by 9
Generation of Source Data for Experiments with Network Attack Detection Software
Kotenko, I. V., Chechulin, A. A., & Branitskiy, A. A. (2017) · Journal of Physics Conference Series · doi:10.1088/1742-6596/820/1/012033 · Cited by 9
Should I (re)Learn or Should I Go(on)?
Casas, P., Mulinka, P., & Vanerio, J. (2019) · ACM Workshop on Moving Target Defense (MTD) · doi:10.1145/3338468.3356829 · Cited by 9
A Multi-View Intrusion Detection Model for Reliable and Autonomous Model Updates
Tomio, R. L., Viegas, E. K., Santin, A. O., & dos Santos, R. R. (2021) · International Conference on Communications · doi:10.1109/icc42927.2021.9500831 · Cited by 8
Open-World Network Intrusion Detection
Rimmer, V., Nadeem, A., Verwer, S., Preuveneers, D., & Joosen, W. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-98795-4_11 · Cited by 10
A novel sensitive DDoS attacks against statistical test in network traffic fusion
Kulandaivel, M., Kumar, G., Sathiyamoorthi, V., & Gupta, S. K. (2023) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.4867 · Cited by 8
Erlacher, F., & Dressler, F. (2018) · ACM SIGCOMM Conference Posters and Demos · doi:10.1145/3234200.3234204 · Cited by 8
Automatic Detection of Computer Network Traffic Anomalies based on Eccentricity Analysis
Martins, R. S., Angelov, P. P., & Costa, B. S. J. (2018) · IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) · doi:10.1109/fuzz-ieee.2018.8491507 · Cited by 8
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Casas, P. (2018) · Network Traffic Measurement and Analysis Conference (TMA) · doi:10.23919/tma.2018.8506486 · Cited by 8
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Misa, C., Durairajan, R., Rejaie, R., & Willinger, W. (2021) · IEEE Network · doi:10.1109/mnet.201.2100064 · Cited by 8
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Yao, Z., Desmouceaux, Y., Cordero-Fuertes, J.-A., Townsley, M., & Clausen, T. (2022) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2022.3197130 · Cited by 8
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Zhou, X., Cheng, Z., Wang, C., Wang, S., Tao, C., Zhou, Z., Chen, X., Luo, J., Wang, D., & Zhou, H. (2026) · Scientific Data · doi:10.1038/s41597-026-06738-x · Cited by 8
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Simioni, J. A., Viegas, E. K., Santin, A. O., & Horchulhack, P. (2025) · ACM Symposium on Applied Computing · doi:10.1145/3672608.3707974 · Cited by 7
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Wang, Z., Zhu, S., Man, K., Zhu, P., Yu, H., Qian, Z., Krishnamurthy, S. V., Porta, T. L., & Lucia, M. J. D. (2021) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3460120.3484762 · Cited by 7
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Skaf, W., & Horváth, T. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-15743-1_17 · Cited by 10
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Chaurasia, B., Verma, A., & Verma, P. (2024) · Computer Networks · doi:10.1016/j.comnet.2024.110432 · Cited by 13
A lightweight network anomaly detection technique
Kim, J., Yoo, W., Sim, A., Suh, S. C., & Kim, I. (2017) · 2012 International Conference on Computing, Networking and Communications (ICNC) · doi:10.1109/iccnc.2017.7876251 · Cited by 7
A novel anomaly detection system using feature-based MSPCA with sketch
Chen, Z., Yeo, C. K., Lee, B., & Lau, C. T. (2017) · Wireless and Optical Communications Conference · doi:10.1109/wocc.2017.7928975 · Cited by 7
An aggregated statistical approach for network flood detection using Gamma-Normal mixture modeling
Hosseinzadeh, S., Amirmazlaghani, M., & Shajari, M. (2020) · Computer Communications · doi:10.1016/j.comcom.2020.01.028 · Cited by 7
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Fukuda, K. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-20305-3_3 · Cited by 7
Self-citationLine rate botnet detection with SmartNIC-embedded feature extraction
Patetta, M., Secci, S., & Taktak, S. (2024) · Computer Networks · doi:10.1016/j.comnet.2024.110809 · Cited by 6
Intrusion Detection System in Self-Organizing Networks: A Survey
Abdulhammed, R., Faezipour, M., & Elleithy, K. M. (2017) · CRC Press eBooks · doi:10.1201/b21885-13 · Cited by 6
Resilient real-time network anomaly detection using novel non-parametric statistical tests
Bollmann, C. A., Tummala, M., & McEachen, J. (2020) · Computers & Security · doi:10.1016/j.cose.2020.102146 · Cited by 6
A streaming algorithm and hardware accelerator to estimate the empirical entropy of network flows
Fernández, Y., Soto, J. E., Vera, S., Prieto, Y., Hernández, C., & Figueroa, M. E. (2023) · Computer Networks · doi:10.1016/j.comnet.2023.110035 · Cited by 6
An analysis of longitudinal TCP passive measurements
Fukuda, K. (2011) · Traffic Monitoring and Analysis · Cited by 6
Self-citationOn the combined use of sketches and CUSUM for Anomaly Detection
Callegari, C., Giordano, S., & Pagano, M. (2015) · International Conference on Computing and Network Communications · doi:10.1109/coconet.2015.7411181 · Cited by 6
MLSEC - Benchmarking Shallow and Deep Machine Learning Models for Network Security
Casas, P., Marín, G., Capdehourat, G., & Korczyński, M. (2019) · IEEE Security and Privacy Workshops (SPW) · doi:10.1109/spw.2019.00050 · Cited by 6
Study and Evaluation of Unsupervised Algorithms Used in Network Anomaly Detection
Dromard, J., & Owezarski, P. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-32523-7_28 · Cited by 6
ADAM & RAL: Adaptive Memory Learning and Reinforcement Active Learning for Network Monitoring
Wassermann, S., Cuvelier, T., Mulinka, P., & Casas, P. (2019) · International Conference on Network and Service Management (CNSM) · doi:10.23919/cnsm46954.2019.9012675 · Cited by 6
Spark-based port and net scan detection
Affinito, A., Alessio Botta, B., Gallo, L., Garofalo, M., & Ventre, G. (2020) · ACM Symposium on Applied Computing · doi:10.1145/3341105.3373970 · Cited by 6
Toward a Reliable Evaluation of Machine Learning Schemes for Network-Based Intrusion Detection
Viegas, E. K., Santin, A. O., & Tedeschi, P. (2023) · IEEE Internet of Things Magazine · doi:10.1109/iotm.001.2300106 · Cited by 6
Iorliam, A., Tirunagari, S., Ho, A. T. S., Li, S., Waller, A. O., & Poh, N. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1609.04214 · Cited by 6
Comparative Analysis of Deep Learning Models for Real-World ISP Network Traffic Forecasting
Koumar, J., Smoleň, T., Jeřábek, K., & Čejka, T. (2025) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2025.3636557 · Cited by 5
Holasová, E., Fujdiak, R., & Mišurec, J. (2024) · Algorithms · doi:10.3390/a17050208 · Cited by 5
Xie, M., Chen, Q., Wang, T., Wang, F., Tao, Y., & Cheng, L. (2022) · IEEE Open Journal of the Computer Society · doi:10.1109/ojcs.2022.3219631 · Cited by 5
A Sketch-Based Algorithm for Network-Flow Entropy Estimation on Programmable Switches Using P4
Soto, J. E., Vera, S., Fernández, Y., Yunge, D., Hernández, C., & Figueroa, M. E. (2023) · Euromicro Conference on Digital System Design (DSD) · doi:10.1109/dsd60849.2023.00021 · Cited by 5
A TCP delay-based mechanism for detecting congestion in the Internet
Dassouki, K., Debar, H., Safa, H., & Hijazi, A. (2013) · International Conference on Communications and Information Technology (ICCIT) · doi:10.1109/iccitechnology.2013.6579538 · Cited by 5
Frécon, J., Fontugne, R., Didier, G., Pustelnik, N., Fukuda, K., & Abry, P. (2016) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2016.7472465 · Cited by 5
Self-citationADSIM: Network Anomaly Detection via Similarity-aware Heterogeneous Ensemble Learning
Chen, W., Wang, Z., Zhong, Y. C., Han, D., Duan, C., Yin, X., Yang, J., & Shi, X. (2021) · Cited by 5
Relaxing state-access constraints in stateful programmable data planes
Cascone, C., Bifulco, R., Pontarelli, S., & Capone, A. (2018) · ACM SIGCOMM Computer Communication Review · doi:10.1145/3211852.3211854 · Cited by 5
Identifying Coordination of Network Scans Using Probed Address Structure.
Mazel, J., Fontugne, R., & Fukuda, K. (2016) · Traffic Monitoring and Analysis · Cited by 5
Self-citationAnomaly detection: An overview of selected methods
Callegari, C., Giordano, S., & Pagano, M. (2017) · International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON) · doi:10.1109/sibircon.2017.8109836 · Cited by 5
Wilmet, A., Viard, T., Matthieu Latapy, B., & Lamarche-Perrin, R. (2019) · Computer Networks · doi:10.1016/j.comnet.2019.07.002 · Cited by 5
HUMAN - Hierarchical Clustering for Unsupervised Anomaly Detection & Interpretation
Mulinka, P., Casas, P., Fukuda, K., & Kencl, L. (2020) · 2020 11th International Conference on Network of the Future (NoF) · doi:10.1109/nof50125.2020.9249194 · Cited by 5
Self-citationStudy of UDP-based Internet traffic: Long-range dependence characteristics
Jusak, J., & Harris, R. (2011) · International Telecommunication Networks and Applications Conference (ITNAC) · doi:10.1109/atnac.2011.6096648 · Cited by 5
Flow-Based Intrusion Detection Systems: A Survey
Al-Bakaa, A., & Al-Musawi, B. Q. (2022) · Communications in computer and information science · doi:10.1007/978-981-19-1166-8_10 · Cited by 4
Patronum: In-network Volumetric DDoS Detection and Mitigation with Programmable Switches
Wu, J., Pan, H., Cui, P., Huang, Y., Zhou, J., He, P., Li, Y., Li, Z., & Xie, G. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-70903-6_10 · Cited by 4
Leveraging Prefix Structure to Detect Volumetric DDoS Attack Signatures with Programmable Switches
Misa, C., Durairajan, R., Gupta, A. K., Rejaie, R., & Willinger, W. (2024) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp54263.2024.00267 · Cited by 6
Characterizing network traffic behaviour using granule‐based association rule mining
Bian, Y., Liu, B., Li, Y., & Gao, J. (2016) · International Journal of Network Management · doi:10.1002/nem.1935 · Cited by 4
Privacy-Preserving Internet Traffic Publication
Guo, L., & Shen, H. (2016) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom.2016.0152 · Cited by 4
ALSR: An Adaptive Label Screening and Relearning Approach for Anomaly Detection
Jing, Y., Qi, Q., Wang, J., Feng, T., & Liao, J. (2019) · IEEE Symposium on Computers and Communications (ISCC) · doi:10.1109/iscc47284.2019.8969661 · Cited by 4
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Compressing Distributed Network Sketches With Traffic-Aware Summaries
Harris, D., Rinberg, A., & Rottenstreich, O. (2022) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2022.3172299 · Cited by 4
Intrusion Detection Model Updates Through GAN Data Augmentation and Transfer Learning
Horchulhack, P., Viegas, E. K., Santin, A. O., & Geremias, J. (2022) · Globecom · doi:10.1109/globecom48099.2022.10000666 · Cited by 4
Metadata
- Authors
- Fontugne, R., Borgnat, P., Abry, P., & Fukuda, K.
- Affiliations
- The Graduate University for Advanced Studies, SOKENDAI (JP)
- École Normale Supérieure de Lyon (FR)
- National Institute of Informatics (JP)
- Venue
- Proceedings of the 6th International COnference
- Publisher
- ACM
- Published
- 2010-11-30
- Pages
- 1-12
- OpenAlex
- W2040333627
- Cited by
- 295
- References
- 38
- Citations by year
- 2026: 13 · 2025: 19 · 2024: 24 · 2023: 14 · 2022: 27 · 2021: 26 · 2020: 38 · 2019: 33 · 2018: 31 · 2017: 18 · 2016: 16 · 2015: 12 · 2014: 7 · 2013: 4 · 2012: 4
- FWCI
- 4.90
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Proceedings of the 6th International Conference (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
- Keywords
- network anomaly detection, traffic monitoring, dimensionality reduction, false positive reduction
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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.
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