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Toward Generating a Large Scale Intrusion Detection Dataset and Intruders Behavioral Profiling Using Network and Transportation Layers Traffic Flow Analyzer (NTLFlowLyzer)
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“Bridging the Gap in Feature Engineering: Addresses gaps between feature extraction [Shafi et al.59] and selection [Lakshminarayana et al.60] by combining both into a hybrid process for improved detection accuracy.”
From A Hybrid Deep Learning Model for Detection and Mitigation of DDoS Attacks in VANETs (2025) · cites this paper as Shafi et al.59
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An empirical comparison of botnet detection methods
García, S., Grill, M., Stiborek, J., & Zunino, A. (2014) · Computers & Security · doi:10.1016/j.cose.2014.05.011 · Cited by 962
Research works printed but not linked: 74
- Abdulganiyu, O.H., Ait Tchakoucht, T., Saheed, Y.K.: A systematic literature review for network intrusion detection system (ids). Int. J. Inf. Secur. 1–38 (2023) · DOI · Paper page
- de Neira, A.B., Kantarci, B., Nogueira, M.: Distributed denial of service attack prediction: challenges, open issues and opportunities. Comput. Netw. 222, 109553 (2023) · DOI
- Alashhab, Z.R., Anbar, M., Singh, M.M., Hasbullah, I.H., Jain, P., Al-Amiedy, T.A.: Distributed denial of service attacks against cloud computing environment: survey, issues, challenges and coherent taxonomy. Appl. Sci. 12(23), 12441 (2022) · DOI
- Markevych, M., Dawson, M.: A review of enhancing intrusion detection systems for cybersecurity using artificial intelligence (ai). Int. Confer. Knowl.-Based Org. 29, 30–37 (2023) · DOI
- Aloqaily, M., Kanhere, S., Bellavista, P., Nogueira, M.: Special issue on cybersecurity management in the era of ai. J. Netw. Syst. Manage. 30(3), 39 (2022) · DOI
- Shafi, M., Lashkari, A.H., Rodriguez, V., Nevo, R.: Toward generating a new cloud-based distributed denial of service (DDoS) dataset and cloud intrusion traffic characterization. Information 15(4), 195 (2024) · DOI
- Thakkar, A., Lohiya, R.: A review of the advancement in intrusion detection datasets. Proced. Comput. Sci. 167, 636–645 (2020) · DOI
- Lashkari, A.H., Draper-Gil, G., Mamun, M.S.I., Ghorbani, A.A., et al.: Characterization of tor traffic using time based features. In: ICISSp, pp. 253–262 (2017) · DOI · Paper page
- Tang, B., Wang, J., Yu, Z., Chen, B., Ge, W., Yu, J., Lu, T.: Advanced persistent threat intelligent profiling technique: a survey. Comput. Electr. Eng. 103, 108261 (2022) · DOI
- Shafi, M., Lashkari, A.H., Roudsari, A.H.: Ntlflowlyzer: towards generating an intrusion detection dataset and intruders behavior profiling through network and transport layers traffic analysis and pattern extraction. Comput. Secur. 148, 104160 (2025) · DOI
- Masdari, M., Khezri, H.: A survey and taxonomy of the fuzzy signature-based intrusion detection systems. Appl. Soft Comput. 92, 106301 (2020) · DOI
- Ayyagari, M.R., Kesswani, N., Kumar, M., Kumar, K.: Intrusion detection techniques in network environment: a systematic review. Wirel. Netw. 27(2), 1269–1285 (2021) · DOI
- Khraisat, A., Gondal, I., Vamplew, P., Kamruzzaman, J.: Survey of intrusion detection systems: techniques, datasets and challenges. Cybersecurity 2(1), 1–22 (2019) · DOI · Paper page
- Hajj, S., El Sibai, R., Bou Abdo, J., Demerjian, J., Makhoul, A., Guyeux, C.: Anomaly-based intrusion detection systems: the requirements, methods, measurements, and datasets. Trans. Emerging Telecommun. Technol. 32(4), e4240 (2021)
- Kocher, G., Kumar, G.: Machine learning and deep learning methods for intrusion detection systems: recent developments and challenges. Soft. Comput. 25(15), 9731–9763 (2021) · DOI
- Devi, M., Nandal, P., Sehrawat, H.: A novel rule-based intrusion detection framework for secure wireless sensor networks (2023) · DOI
- Einy, S., Oz, C., Navaei, Y.D.: The anomaly-and signature-based ids for network security using hybrid inference systems. Math. Probl. Eng. 2021, 1–10 (2021) · DOI
- Varzaneh, Z.A., Kuchaki Rafsanjani, M.: Intrusion detection system using a new fuzzy rule-based classification system based on genetic algorithm. Intell. Dec. Technol. 15(2), 231–237 (2021)
- Asad, H., Gashi, I.: Dynamical analysis of diversity in rule-based open source network intrusion detection systems. Empir. Softw. Eng. 27, 1–30 (2022) · DOI
- Díaz-Verdejo, J., Muñoz-Calle, J., Estepa Alonso, A., Estepa Alonso, R., Madinabeitia, G.: On the detection capabilities of signature-based intrusion detection systems in the context of web attacks. Appl. Sci. 12(2), 852 (2022) · DOI
- Liao, H.-J., Lin, C.-H.R., Lin, Y.-C., Tung, K.-Y.: Intrusion detection system: a comprehensive review. J. Netw. Comput. Appl. 36(1), 16–24 (2013) · DOI
- Mushtaq, E., Zameer, A., Umer, M., Abbasi, A.A.: A two-stage intrusion detection system with auto-encoder and lstms. Appl. Soft Comput. 121, 108768 (2022) · DOI
- Tama, B.A., Comuzzi, M., Rhee, K.-H.: Tse-ids: a two-stage classifier ensemble for intelligent anomaly-based intrusion detection system. IEEE Access 7, 94497–94507 (2019) · DOI
- Dina, A.S., Manivannan, D.: Intrusion detection based on machine learning techniques in computer networks. Internet Things 16, 100462 (2021)
- Zavrak, S., İskefiyeli, M.: Anomaly-based intrusion detection from network flow features using variational autoencoder. IEEE Access 8, 108346–108358 (2020) · DOI
- Salo, F., Injadat, M., Nassif, A.B., Shami, A., Essex, A.: Data mining techniques in intrusion detection systems: a systematic literature review. IEEE Access 6, 56046–56058 (2018) · DOI
- Guibene, K., Messai, N., Ayaida, M., Khoukhi, L.: A data mining-based intrusion detection system for cyber physical power systems. In: Proceedings of the 18th ACM International Symposium on QoS and Security for Wireless and Mobile Networks, pp. 55–62 (2022) · DOI
- Mohan, L., Jain, S., Suyal, P., Kumar, A.: Data mining classification techniques for intrusion detection system. In: 2020 12th International Conference on Computational Intelligence and Communication Networks (CICN), pp. 351–355. IEEE (2020) · DOI
- Monzer, M.-H., Beydoun, K., Ghaith, A., Flaus, J.-M.: Model-based ids design for icss. Reliab. Eng. Syst. Saf. 225, 108571 (2022)
- Sonchack, J., Aviv, A.J., Smith, J.M.: Cross-domain collaboration for improved ids rule set selection. J. Inf. Secur. Appl. 24, 25–40 (2015)
- Sagala, A.: Automatic snort ids rule generation based on honeypot log. In: 2015 7th International Conference on Information Technology and Electrical Engineering (ICITEE), pp. 576–580. IEEE (2015) · DOI
- Tomandl, A., Fuchs, K.-P., Federrath, H.: Rest-net: a dynamic rule-based ids for vanets. In: 2014 7th IFIP Wireless and Mobile Networking Conference (WMNC), pp. 1–8. IEEE (2014) · DOI
- Afzal, Z., Lindskog, S.: Ids rule management made easy. In: 2016 8th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), pp. 1–8. IEEE (2016) · DOI
- AlYousef, M.Y., Abdelmajeed, N.T.: Dynamically detecting security threats and updating a signature-based intrusion detection system’s database. Proced. Comput. Sci. 159, 1507–1516 (2019) · DOI
- Li, W., Tug, S., Meng, W., Wang, Y.: Designing collaborative blockchained signature-based intrusion detection in iot environments. Fut. Gener. Comput. Syst. 96, 481–489 (2019) · DOI
- Wang, Y., Meng, W., Li, W., Li, J., Liu, W.-X., Xiang, Y.: A fog-based privacy-preserving approach for distributed signature-based intrusion detection. J. Parallel Distrib. Comput. 122, 26–35 (2018) · DOI
- Zhang, C., Jia, D., Wang, L., Wang, W., Liu, F., Yang, A.: Comparative research on network intrusion detection methods based on machine learning. Comput. Secur. 121, 102861 (2022) · DOI
- Ahmad, Z., Shahid Khan, A., Wai Shiang, C., Abdullah, J., Ahmad, F.: Network intrusion detection system: a systematic study of machine learning and deep learning approaches. Trans. Emerging Telecommun. Technol. 32(1), e4150 (2021)
- Kim, T., Pak, W.: Real-time network intrusion detection using deferred decision and hybrid classifier. Futur. Gener. Comput. Syst. 132, 51–66 (2022) · DOI
- Qiu, W., Ma, Y., Chen, X., Yu, H., Chen, L.: Hybrid intrusion detection system based on Dempster–Shafer evidence theory. Comput. Secur. 117, 102709 (2022)
- Herrera-Semenets, V., Hernández-León, R., van den Berg, J.: A fast instance reduction algorithm for intrusion detection scenarios. Comput. Electr. Eng. 101, 107963 (2022) · DOI
- Baldini, G., Amerini, I.: Online distributed denial of service (DDOS) intrusion detection based on adaptive sliding window and morphological fractal dimension. Comput. Netw. 210, 108923 (2022) · DOI
- Asif, M., Abbas, S., Khan, M., Fatima, A., Khan, M.A., Lee, S.-W.: Mapreduce based intelligent model for intrusion detection using machine learning technique. J. King Saud Univer.-Comput. Inf. Sci. (2021) · DOI
- Hou, J., Liu, F., Lu, H., Tan, Z., Zhuang, X., Tian, Z.: A novel flow-vector generation approach for malicious traffic detection. J. Parallel Distrib. Comput. 169, 72–86 (2022) · DOI
- Rabbani, M., Wang, Y.L., Khoshkangini, R., Jelodar, H., Zhao, R., Hu, P.: A hybrid machine learning approach for malicious behaviour detection and recognition in cloud computing. J. Netw. Comput. Appl. 151, 102507 (2020) · DOI
- Herrmann, D., Banse, C., Federrath, H.: Behavior-based tracking: exploiting characteristic patterns in DNS traffic. Comput. Secur. 39, 17–33 (2013) · DOI
- Imran, M., Haider, N., Shoaib, M., Razzak, I., et al.: An intelligent and efficient network intrusion detection system using deep learning. Comput. Electr. Eng. 99, 107764 (2022)
- Liu, Q., Wang, D., Jia, Y., Luo, S., Wang, C.: A multi-task based deep learning approach for intrusion detection. Knowl.-Based Syst. 238, 107852 (2022)
- Ravi, V., Chaganti, R., Alazab, M.: Recurrent deep learning-based feature fusion ensemble meta-classifier approach for intelligent network intrusion detection system. Comput. Electr. Eng. 102, 108156 (2022) · DOI
- Li, B., Wang, Y., Xu, K., Cheng, L., Qin, Z.: Dfaid: density-aware and feature-deviated active intrusion detection over network traffic streams. Comput. Secur. 118, 102719 (2022)
- Shiravi, A., Shiravi, H., Tavallaee, M., Ghorbani, A.A.: Toward developing a systematic approach to generate benchmark datasets for intrusion detection. Comput. Secur. 31(3), 357–374 (2012) · DOI
- Moustafa, N., Slay, J.: Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set). In: 2015 Military Communications and Information Systems Conference (MilCIS), pp. 1–6. IEEE (2015) · DOI · Paper page
- Lawrence, H., Ezeobi, U., Tauil, O., Nosal, J., Redwood, O., Zhuang, Y., Bloom, G.: Cupid: a labeled dataset with pentesting for evaluation of network intrusion detection. J. Syst. Arch. 129, 102621 (2022) · DOI
- Cohen, I., Huang, Y., Chen, J., Benesty, J., Benesty, J., Chen, J., Huang, Y., Cohen, I.: Pearson correlation coefficient. Noise Reduct. Speech Process. 1–4 (2009) · DOI
- Awerbuch, B.: A new distributed depth-first-search algorithm. Inf. Process. Lett. 20(3), 147–150 (1985) · DOI
- Chen, Y.-C.: A tutorial on kernel density estimation and recent advances. Biostat. Epidemiol. 1(1), 161–187 (2017) · DOI
- Heer, J.: Fast & accurate gaussian kernel density estimation. In: 2021 IEEE Visualization Conference (VIS), pp. 11–15. IEEE (2021) · DOI
- Sheikhpour, R., Sarram, M.A., Sheikhpour, R.: Particle swarm optimization for bandwidth determination and feature selection of kernel density estimation based classifiers in diagnosis of breast cancer. Appl. Soft Comput. 40, 113–131 (2016) · DOI
- He, Y.-L., Ye, X., Huang, D.-F., Huang, J.Z., Zhai, J.-H.: Novel kernel density estimator based on ensemble unbiased cross-validation. Inf. Sci. 581, 327–344 (2021) · DOI
- Kotsiantis, S., Kanellopoulos, D.: Association rules mining: a recent overview. GESTS Int. Trans. Comput. Sci. Eng. 32(1), 71–82 (2006)
- Zeng, Y., Yin, S., Liu, J., Zhang, M.: Research of improved fp-growth algorithm in association rules mining. Sci. Program. 2015, 6–6 (2015)
- Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN’95-International Conference on Neural Networks, vol. 4, pp. 1942–1948. IEEE (1995) · DOI · Paper page
- Poli, R., Kennedy, J., Blackwell, T.: Particle swarm optimization: an overview. Swarm Intell. 1, 33–57 (2007) · DOI · Paper page
- Zhang, Z.: Improved adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2. IEEE (2018) · DOI
- Wei, Z., Wang, J., Zhao, Z., Shi, K.: Toward data efficient anomaly detection in heterogeneous edge-cloud environments using clustered federated learning. Futur. Gener. Comput. Syst. 164, 107559 (2025)
- Dilworth, R., Gudla, C.: Harnessing pu learning for enhanced cloud-based ddos detection: a comparative analysis. arXiv preprint arXiv:2410.18380 (2024) · arXiv
- Shafi, M., Lashkari, A.H., Mohanty, H.: Unveiling malicious dns behavior profiling and generating benchmark dataset through application layer traffic analysis. Comput. Electr. Eng. 118, 109436 (2024) · DOI
- Zou, F., Ren, Y., Zhu, J., Tang, J.: Detecting data leakage in dns traffic based on time series anomaly detection. In: 2021 IEEE 23rd Int Conf on High Performance Computing & Communications; 7th Int Conf on Data Science & Systems; 19th Int Conf on Smart City; 7th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys), pp. 503–510. IEEE (2021) · DOI
- Lison, P., Mavroeidis, V.: Neural reputation models learned from passive dns data. In: 2017 IEEE International Conference on Big Data (Big Data), pp. 3662–3671. IEEE (2017) · DOI
- Göcs, L., Johanyák, Z.C.: Identifying relevant features of cse-cic-ids2018 dataset for the development of an intrusion detection system. arXiv preprint arXiv:2307.11544 (2023) · DOI
- Sarhan, M., Layeghy, S., Portmann, M.: Feature analysis for machine learning-based iot intrusion detection. arXiv preprint arXiv:2108.12732 (2021) · DOI
- Cambiaso, E., Papaleo, G., Chiola, G., Aiello, M.: Slow dos attacks: definition and categorisation. Int. J. Trust Manag. Comput. Commun. 1(3–4), 300–319 (2013) · DOI
- Cambiaso, E., Papaleo, G., Aiello, M.: Taxonomy of slow dos attacks to web applications. In: Recent Trends in Computer Networks and Distributed Systems Security: International Conference, SNDS 2012, Trivandrum, India, October 11–12, 2012. Proceedings 1, pp. 195–204. Springer (2012) · DOI
- Lashkari, A.H., Gil, G.D., Mamun, M.S.I., Ghorbani, A.A.: Characterization of tor traffic using time based features. In: Proceeding of the 3rd International Conference on Information System Security and Privacy, SCITEPRESS (2017)
Cited by
Sorted by citations
A Hybrid Deep Learning Model for Detection and Mitigation of DDoS Attacks in VANETs
Jayakrishna, N., & Prasanth, N. N. (2025) · Scientific Reports · doi:10.1038/s41598-025-15215-1 · Cited by 20
Metadata
- Authors
- Shafi, M., Lashkari, A. H., & Roudsari, A. H.
- Affiliations
- York University (CA)
- Gachon University (KR)
- Corresponding author
- Shafi, MohammadMoein
- Venue
- Journal of Network and Systems Management
- Published
- 2025-03-10
- Volume
- 33
- Issue
- 2
- ISSN
- 1064-7570 (ISSN-L)
- 1573-7705
- OpenAlex
- W4408283015
- Cited by
- 10
- References
- 1
- Citations by year
- 2026: 5 · 2025: 5
- FWCI
- 4.23
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Journal of Network and Systems Management (publishedVersion)
- Funders
- Canada Research Chairs (CRC-2021-00340)
- Natural Sciences and Engineering Research Council of Canada (RGPIN-2020-04701)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
- Physical Sciences › Computer Science › Hardware and Architecture › Network Packet Processing and Optimization
- Keywords
- intrusion detection, network traffic analysis, traffic characteristics, feature selection, relation extraction, named entity recognition, false positive reduction, network security
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