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Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization
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“Many studies classify feature selection methods into three categories: filter-based techniques, wrapper techniques, and embed techniques”
From Comparative Analysis of Intrusion Detection Systems and Machine Learning Based Model Analysis through Decision Tree (2023) · cites this paper as [210]
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Cites
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 · cited 3×
“KDD’99 (University of California, Irvine 1998-99): This dataset is an updated version of the DARPA98, by processing the tcpdump portion.”
BackgroundMethodSelf-citationTesting Intrusion detection systems
McHugh, J. (2000) · ACM Transactions on Privacy and Security · doi:10.1145/382912.382923 · Cited by 1,490 · cited 4×
“Moreover, it lacks actual attack data records (McHugh, 2000) (Brown et al.”
BackgroundMethodInfluentialToward developing a systematic approach to generate benchmark datasets for intrusion detection
Shiravi, A., Shiravi, H., Tavallaee, M., & Ghorbani, A. A. (2012) · Computers & Security · doi:10.1016/j.cose.2011.12.012 · Cited by 1,345
Self-citationCharacterization of Tor Traffic using Time based Features
Lashkari, A. H., Gil, G. D., Mamun, M., & Ghorbani, A. A. (2017) · International Conference on Information Systems Security and Privacy (ICISSP) · doi:10.5220/0006105602530262 · Cited by 926 · cited 3×
“We begin to extract the 80 traffic features from the dataset using CICFlowMeter (CICFlowMeter, 2017),(Habibi Lashkari et al., 2017).”
MethodSelf-citationStatistical analysis of honeypot data and building of Kyoto 2006+ dataset for NIDS evaluation
Song, J., Takakura, H., Okabe, Y., Eto, M., Inoue, D., & Nakao, K. (2011) · International Workshop on Building Analysis Datasets and Gathering Experience Returns for Security · doi:10.1145/1978672.1978676 · Cited by 335 · cited 2×
“The normal traffic here has been simulated repeatedly during the attacks and producing only DNS and mail traffic data, which is not reflected in real world normal network traffic, so there are no false positives, which are important for minimizing the number of alerts (Song et al., 2011) (M.”
BackgroundGeneration of a new IDS test dataset: Time to retire the KDD collection
Creech, G., & Hu, J.-K. (2013) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc.2013.6555301 · Cited by 326 · cited 1×
“ADFA (University of New South Wales 2013): This dataset includes normal training and validating data and 10 attacks per vector (Creech and Hu, 2013).”
MethodTowards a Reliable Intrusion Detection Benchmark Dataset
Sharafaldin, I., Gharib, A., Lashkari, A. H., & Ghorbani, A. A. (2017) · Software Networking · doi:10.13052/jsn2445-9739.2017.009 · Cited by 309 · cited 9×
“Finally, we evaluate the quality of the generated dataset by searching for common mistakes and criticisms of other synthetically created datasets, based on the 11 criteria from the last proposed dataset evaluation framework by Canadian Institute for Cybersecurity (CIC)(Sharafaldin et al., 2017).”
MethodInfluentialSelf-citationAn Evaluation Framework for Intrusion Detection Dataset
Gharib, A., Sharafaldin, I., Lashkari, A. H., et al. (2016) · International Conferences on Information Science and System · doi:10.1109/icissec.2016.7885840 · Cited by 224 · cited 6×
“Regarding to the last dataset evaluation framework published on 2016 (Gharib et al., 2016), covering eleven criteria is necessary for each dataset.”
BackgroundMethodInfluentialSelf-citationNetwork Intrusion Detection and Prevention - Concepts and Techniques
Ghorbani, A., Lu, W., & Tavallaee, M. (2009) · Advances in Information Security · doi:10.1007/978-0-387-88771-5 · Cited by 175 · cited 1×
“Since 1999, Scott et al. (Scott and Wilkins, 1999), Heideman and Papadopulus (Heidemann and Papdopoulos, 2009), Ghorbani et al. (Ghorbani Ali and Mahbod, 2010), Nehinbe (Nehinbe, 2011), Shiravi et al. (Ali Shiravi and Ghorbani, 2012), and Sharfaldin et al. (Gharib et al., 2016) tried to propose an…”
BackgroundSelf-citationA Labeled Data Set for Flow-Based Intrusion Detection
Sperotto, A., Sadre, R., van Vliet, F., & Pras, A. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04968-2_4 · Cited by 155 · cited 1×
“It is labelled and is more realistic, but the lack of volume and diversity of attacks is obvious (Sperotto et al., 2009).”
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DeepDetect: Detection of Distributed Denial of Service Attacks Using Deep Learning
Asad, M., Asim, M., Javed, T., et al. (2019) · The Computer Journal · doi:10.1093/comjnl/bxz064 · Cited by 120
Multi‐objective‐based feature selection for DDoS attack detection in IoT networks
Roopak, M., Tian, G. Y., & Chambers, J. A. (2020) · IET Networks · doi:10.1049/iet-net.2018.5206 · Cited by 120
Error Prevalence in NIDS datasets: A Case Study on CIC-IDS-2017 and CSE-CIC-IDS-2018
Liu, L., Engelen, G., Lynar, T., et al. (2022) · IEEE Conference on Communications and Network Security · doi:10.1109/cns56114.2022.9947235 · Cited by 120
“NSL-KDD [5], an improved iteration of KDD, resolved the redundant record issue, and became the successive mainstay benchmark dataset for NIDS research, alongside UNSW-NB15 [6], CICIDS-2017 [7], CSE-CIC-IDS-2018 [8], and BoT-IoT [9].”
MethodUnveiling the potential of Graph Neural Networks for robust Intrusion Detection
Pujol-Perich, D., Suárez‐Varela, J., Cabellos‐Aparicio, A., & Barlet‐Ros, P. (2022) · ACM SIGMETRICS Performance Evaluation Review · doi:10.1145/3543146.3543171 · Cited by 142
On the Generation of Anomaly Detection Datasets in Industrial Control Systems
Gómez, Á. L. P., Maimó, L. F., Celdrán, A. H., Clemente, F. J. G., Sarmiento, C. C., Masa, C. D. C., & Nistal, R. M. (2019) · IEEE Access · doi:10.1109/access.2019.2958284 · Cited by 120
“Conversely, CICIDS 2017 Dataset [14] is collected from a simulated scenario with two separate networks: the outside and inside networks, made up of 4 attacking computers and 14 victim computers respectively.”
MethodDas, S., Saha, S., Priyoti, A. T., Roy, E. K., Sheldon, F. T., Haque, A., & Shiva, S. G. (2021) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2021.3138457 · Cited by 118
“test_X: test features, test_y: test target Output: m: a best performing model 1 init 2 fs = [‘ANOVA’, ‘CHI2’, ‘LASSO’, ‘ LRL1’, 3 ’MUTINFO’, ‘PEARSON’, ‘RF’, ‘ RFE’, ‘SFPR’] 4 mli = [‘LR’, ‘DT’, ‘NB’, ‘NN’, ‘ SVM’] 5 mle = [‘LR’, ‘DT’, ‘NB’, ‘NN’, ‘ SVM’] 6 result_df = [‘LR_Target’, ‘DT_Target’, ‘ NB_Target’, 7 ‘NN_Ta…”
MethodEIDM: deep learning model for IoT intrusion detection systems
Elnakib, O., Shaaban, E., Mahmoud, M. M. E. A., & Emara, K. (2023) · The Journal of Supercomputing · doi:10.1007/s11227-023-05197-0 · Cited by 146
“The CICIDS2017 dataset [23], which is one of the most commonly used datasets in intrusion detection systems, is used in the evaluation and comparison of the different models.”
MethodJullian, O., Otero, B., Rodríguez, E., Gutiérrez, N., Antona, H., & Canal, R. (2023) · Journal of Network and Systems Management · doi:10.1007/s10922-023-09722-7 · Cited by 134
“The system uses the CICIDS [20, 21] dataset but balances the DDoS attack dataset by duplicating the data, which improves the training of the DL methods.”
MethodEnhanced Intrusion Detection with LSTM-Based Model, Feature Selection, and SMOTE for Imbalanced Data
Sayegh, H. R., Wang, D., & Al-madani, A. M. (2024) · Applied Sciences · doi:10.3390/app14020479 · Cited by 150
“…CIC-IDS2017 including incorporates the B-Profile system, which data SSH, , including a complete network configuration, labeled dataset, complete interaction coverage, complete capture, available protocols, attack diversity, heterogeneity, feature set extraction, and comprehensive metadata [24].”
BackgroundInfluentialIoTDefender: A Federated Transfer Learning Intrusion Detection Framework for 5G IoT
Fan, Y., Li, Y., Zhan, M., Cui, H., & Zhang, Y. (2020) · doi:10.1109/bigdatase50710.2020.00020 · Cited by 121
“We use CICIDS2017 [39] as public dataset, NSL-KDD [37] and three IoT datasets [13], [38] as private dataset.”
MethodNetwork Anomaly Detection Using Memory-Augmented Deep Autoencoder
Min, B., Yoo, J., Kim, S. S., Shin, D., & Shin, D. (2021) · IEEE Access · doi:10.1109/access.2021.3100087 · Cited by 116
“Experiments were conducted on the open network intrusion detection datasets NSL-KDD [8], UNSW-NB15 [9], and CICIDS 2017 [10], and the results were compared with an AE and a one-class SVM (OCSVM) [11].”
MethodHo, S., Al-Jufout, S., Dajani, K. F., & Mozumdar, M. (2021) · IEEE Open Journal of the Computer Society · doi:10.1109/ojcs.2021.3050917 · Cited by 116
Behiry, M. H., & Aly, M. (2024) · Journal Of Big Data · doi:10.1186/s40537-023-00870-w · Cited by 115
“Sonule et al. [37] focused on UNSWNB15 Dataset and ML. Sharafaldin et al. [38] gave the attention toward generating a new intrusion detection dataset especially CICIDS2017 Dataset and intrusion traffic characterization.”
BackgroundAn Enhanced Anomaly Detection in Web Traffic Using a Stack of Classifier Ensemble
Tama, B. A., Nkenyereye, L., Islam, S. M. R., & Kwak, K. S. (2020) · IEEE Access · doi:10.1109/access.2020.2969428 · Cited by 114
“The proposed model is considerably robust in handling data imbalanced problem such as CICIDS-2017.”
BackgroundInfluentialAndresini, G., Pendlebury, F., Pierazzi, F., Loglisci, C. A. A. M. D., Appice, A., & Cavallaro, L. (2021) · ACM Workshop on Artificial Intelligence and Security (AISec) · doi:10.1145/3474369.3486864 · Cited by 117
“The CICIDS2017 dataset [58] from the Canadian Institute for Cybersecurity provides data collection of a complete network testbed of different devices and operating systems, in which separate victim and attacker networks communicate over the Internet.”
BackgroundMethodWang, Z., Li, Z., He, D., & Chan, S. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117671 · Cited by 119
Adedeji, K. B., Abu-Mahfouz, A. M. I., & Kurien, A. M. (2023) · Journal of Sensor and Actuator Networks · doi:10.3390/jsan12040051 · Cited by 145
Shahin, M., Maghanaki, M., Hosseinzadeh, A., & Chen, F. F. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102685 · Cited by 119
PBCNN: Packet Bytes-based Convolutional Neural Network for Network Intrusion Detection
Yu, L., Dong, J., Chen, L., Li, M., Xu, B., Li, Z., Qiao, L., Liu, L., Zhao, B., & Zhang, C. (2021) · Computer Networks · doi:10.1016/j.comnet.2021.108117 · Cited by 113
Chen, Y., Lin, Q., Wei, W., Ji, J., Wong, K., & Coello, C. A. C. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108505 · Cited by 114
Federated Learning for IoT Intrusion Detection
Lazzarini, R., Tianfield, H., & Charissis, V. (2023) · AI · doi:10.3390/ai4030028 · Cited by 138
Bindra, N., & Sood, M. (2019) · Automatic Control and Computer Sciences · doi:10.3103/s0146411619050043 · Cited by 111
“We chose Intrusion Detection Evaluation Dataset [19] from the Canadian Institute of Cybersecurity.”
MethodA Reliable Network Intrusion Detection Approach Using Decision Tree with Enhanced Data Quality
Guezzaz, A., Benkirane, S., Azrour, M., & Khurram, S. (2021) · Security and Communication Networks · doi:10.1155/2021/1230593 · Cited by 114
The use of generative adversarial networks to alleviate class imbalance in tabular data: a survey
Sauber-Cole, R., & Khoshgoftaar, T. M. (2022) · Journal Of Big Data · doi:10.1186/s40537-022-00648-6 · Cited by 132
“The researchers used the CICIDS 2017 dataset [40] to construct a basic GAN and augment the various classes in proportion to their rarity in the base dataset.”
MethodVijayanand, R., & Devaraj, D. (2020) · IEEE Access · doi:10.1109/access.2020.2978035 · Cited by 114
“Similarly, the features of the CICIDS2017 dataset had the characteristics of WMN attacks and were represented using 77 features [28].”
MethodA Comprehensive Review of the State-of-the-Art on Security and Privacy Issues in Healthcare
Martínez, A. L., Pérez, M. G., & Ruiz‐Martínez, A. (2022) · ACM Computing Surveys · doi:10.1145/3571156 · Cited by 107
Panigrahi, R., Borah, S., Bhoi, A. K., Ijaz, M. F., Pramanik, M., Jhaveri, R. H., & Chowdhary, C. L. (2021) · Mathematics · doi:10.3390/math9060690 · Cited by 109
A Detailed Analysis of Benchmark Datasets for Network Intrusion Detection System
Ghurab, M., Gaphari, G., Alshami, F., et al. (2021) · Asian Journal of Research in Computer Science · doi:10.9734/ajrcos/2021/v7i430185 · Cited by 109
Yang, J., Chen, X., Chen, S., Jiang, X., & Tan, X. (2021) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2021.3083422 · Cited by 114
Datasets are not enough: Challenges in labeling network traffic
Guerra, J. L., Catania, C. A., & Veas, E. E. (2022) · Computers & Security · doi:10.1016/j.cose.2022.102810 · Cited by 115
“Although dataset quality is not precisely defined, several authors [6, 7] agree that representative and accurate labels are the main two aspects for measuring the quality of a network traffic labeled dataset.”
BackgroundMethodInfluentialIdentifying and Benchmarking Key Features for Cyber Intrusion Detection: An Ensemble Approach
Binbusayyis, A., & Vaiyapuri, T. (2019) · IEEE Access · doi:10.1109/access.2019.2929487 · Cited by 109
VARMAN: Multi-plane security framework for software defined networks
Krishnan, P., Duttagupta, S., & Achuthan, K. (2019) · Computer Communications · doi:10.1016/j.comcom.2019.09.014 · Cited by 109
Comparison of Machine Learning and Deep Learning Models for Network Intrusion Detection Systems
Thapa, N., Liu, Z., KC, D. B., Gokaraju, B., & Roy, K. (2020) · Future Internet · doi:10.3390/fi12100167 · Cited by 124
“CIC-IDS2017 is also a labeled flow-based dataset with similar traits as that of the CIDDS dataset.”
MethodInfluentialElaziz, M. A., Al‐qaness, M. A. A., Dahou, A., Ibrahim, R. A., & El‐Latif, A. A. A. (2023) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2022.103402 · Cited by 110
Vishwakarma, M., & Kesswani, N. (2023) · Decision Analytics Journal · doi:10.1016/j.dajour.2023.100233 · Cited by 117
Patil, R. S., Dudeja, H., & Modi, C. (2019) · Computers & Security · doi:10.1016/j.cose.2019.05.016 · Cited by 106
A New Method for Flow-Based Network Intrusion Detection Using the Inverse Potts Model
Pontes, C. F. T., de Souza, M. M. C., Gondim, J. J. C., Bishop, M., & Marotta, M. A. (2021) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2021.3075503 · Cited by 122
“To assess EFC capabilities to separate benign from malicious traffic flow samples correctly, we performed classification experiments considering datasets CIDDS-001, CICIDS17 and CICDDoS19.”
BackgroundInfluentialIntrusion detection approach based on optimised artificial neural network
Choraś, M., & Pawlicki, M. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.07.138 · Cited by 109
Valdovinos, I. A., Pérez-Díaz, J. A., Choo, K. R., & Botero, J. F. (2021) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2021.103093 · Cited by 108
Bertoli, G. D. C., Pereira, L. A., Saotome, O., & dos Santos, A. L. (2023) · Computers & Security · doi:10.1016/j.cose.2023.103106 · Cited by 113
“More details about this dataset are available in its publication [54].”
BackgroundAlabsi, B. A., Anbar, M., & Rihan, S. D. A. (2023) · Sensors · doi:10.3390/s23146507 · Cited by 107
“On the BoT-IoT [34] and CIC-IDS2018 [35] datasets, the methods for binary and multiclass classification were evaluated, and the results were rated.”
A Multi-Layer Classification Approach for Intrusion Detection in IoT Networks Based on Deep Learning
Qaddoura, R., Al-Zoubi, A. M., Faris, H., & Almomani, I. (2021) · Sensors · doi:10.3390/s21092987 · Cited by 110
“Many popular datasets are introduced in the literature for intrusion detection, including ISCX2012 [21], UNSWNB15 [22] and CICIDS2017 [23].”
BackgroundA Security Model Based on LightGBM and Transformer to Protect Healthcare Systems From Cyberattacks
Ghourabi, A. (2022) · IEEE Access · doi:10.1109/access.2022.3172432 · Cited by 112
Network Anomaly Intrusion Detection Based on Deep Learning Approach
Wang, Y., Houng, Y.-C., Chen, H.-X., et al. (2023) · Sensors · doi:10.3390/s23042171 · Cited by 105
“The total number of datasets in the CSE-CIC-IDS2018 is 16,233,002 [7].”
BackgroundMethodHnamte, V., & Hussain, J. (2023) · Telematics and Informatics Reports · doi:10.1016/j.teler.2023.100077 · Cited by 104
MANDA: On Adversarial Example Detection for Network Intrusion Detection System
Wang, N., Chen, Y., Xiao, Y., Hu, Y., Lou, W., & Hou, Y. T. (2022) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2022.3148990 · Cited by 110
“CICIDS2017 dataset [55] contains benign traffic and 12 up-to-date attacks, which resembles the real-world Packet Capture (PCAPs) data.”
BackgroundAhmim, A., Maazouzi, F., Ahmim, M., Namane, S., & Dhaou, I. B. (2023) · IEEE Access · doi:10.1109/access.2023.3327620 · Cited by 122
A Survey of Deep Learning Technologies for Intrusion Detection in Internet of Things
Liao, H., Murah, M. Z., Hasan, M. K., Aman, A. H. M., Jin, F., Hu, X., & Khan, A. U. R. (2024) · IEEE Access · doi:10.1109/access.2023.3349287 · Cited by 135
“The categories of attacks identified in the CICIDS2017 dataset are Botnet attacks, Brute Force attacks, DoS & DDoS attacks, Infiltration attacks, Web attacks, and Port Scan attacks [72].”
BackgroundA Secure Ensemble Learning-Based Fog-Cloud Approach for Cyberattack Detection in IoMT
Khan, F., Jan, M. A., Alturki, R., Alshehri, M. D., Shah, S. T., & Rehman, A. U. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3231424 · Cited by 109
“packets using IoT devices in the CICIDS2017 dataset [20].”
MethodOpenStackDP: a scalable network security framework for SDN-based OpenStack cloud infrastructure
Krishnan, P., Jain, K., Aldweesh, A. Y., et al. (2023) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-023-00406-w · Cited by 102
“A synthetic dataset and a few real-world datasets were used to study the influence of thresholds (e.g., NSL-KDD [57], CICIDS2017 [58], Hog-zilla [59], CICIDS2018 [60], CICIDS2019 [61]).”
MethodA Survey of Public IoT Datasets for Network Security Research
Keersmaeker, F. D., Cao, Y., Ndonda, G. K., & Sadre, R. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3288942 · Cited by 111
A Systematic Literature Review on Host-Based Intrusion Detection Systems
Satılmış, H., Akleylek, S., & Tok, Z. Y. (2024) · IEEE Access · doi:10.1109/access.2024.3367004 · Cited by 103
“The ISCXIDS2012 [68] and CICIDS2017 [69] datasets consist of attack and normal data samples related to computer network traffic.”
BackgroundXu, R., Wu, G., Wang, W., Gao, X., He, A., & Zhang, Z. (2024) · Computer Networks · doi:10.1016/j.comnet.2024.110495 · Cited by 108
Hyperparameter Optimization for 1D-CNN-Based Network Intrusion Detection Using GA and PSO
Kilichev, D., & Kim, W. (2023) · Mathematics · doi:10.3390/math11173724 · Cited by 115
A Two-Level Flow-Based Anomalous Activity Detection System for IoT Networks
Ullah, I., & Mahmoud, Q. H. (2020) · Electronics · doi:10.3390/electronics9030530 · Cited by 104
“The CICIDS2017 [51] dataset that was developed at the Canadian Institute for Cybersecurity (CIC), University of New Brunswick Canada uses diverse attack scenarios, e.g., Brute Force, Heartbleed, Botnet, DoS, DDoS, Web, and Infiltration attack.”
MethodInfluentialNuaimi, M., Fourati, L. C., & Hamed, B. B. (2023) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2023.103637 · Cited by 109
Hypervisor-based cloud intrusion detection through online multivariate statistical change tracking
Aldribi, A., Traoré, I., Moa, B., & Nwamuo, O. (2019) · Computers & Security · doi:10.1016/j.cose.2019.101646 · Cited by 101
A Novel Multimodal-Sequential Approach Based on Multi-View Features for Network Intrusion Detection
He, H., Sun, X., He, H., Zhao, G., He, L., & Ren, J. (2019) · IEEE Access · doi:10.1109/access.2019.2959131 · Cited by 105
“The number of hidden layer of LSTM is set to 1 and the numbers of neurons are tuned in [120, 90, 80, 70, 60, 50, 40, 30, 20] units.”
BackgroundMethodFederated Learning for Distributed IIoT Intrusion Detection Using Transfer Approaches
Zhang, J., Luo, C., Carpenter, M., & Min, G. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3216575 · Cited by 101
“We use the CIC-IDS datasets [27] of network traffic, collected from the real-world network when intrusions occurred.”
MethodA Blended Deep Learning Intrusion Detection Framework for Consumable Edge-Centric IoMT Industry
Alzubi, J. A. A., Alzubi, O. A., Qiqieh, I., & Singh, A. P. (2024) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2024.3350231 · Cited by 112
“A publicly available CSE-CIC-IDS 2018 dataset is down-loaded from [46], [47] produced by the Amazon Web Services (AWS) platform.”
MethodKASIM, Ö. (2020) · Computer Networks · doi:10.1016/j.comnet.2020.107390 · Cited by 98
Optimized extreme learning machine for detecting DDoS attacks in cloud computing
Kushwah, G. S., & Ranga, V. (2021) · Computers & Security · doi:10.1016/j.cose.2021.102260 · Cited by 98
Deep Generative Learning Models for Cloud Intrusion Detection Systems
Vu, L., Nguyen, Q. U., Nguyen, D. N., Hoang, D. T., & Dutkiewicz, E. (2022) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2022.3163811 · Cited by 97
“This dataset contains many type of DDoS attacks, including low-rate DDoS attack and application layer DDoS attacks [41].”
BackgroundMethodIoT data analytics in dynamic environments: From an automated machine learning perspective
Yang, L., & Shami, A. A. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105366 · Cited by 100
Ensemble Classifiers for Network Intrusion Detection Using a Novel Network Attack Dataset
Mahfouz, A., Abuhussein, A., Venugopal, D., & Shiva, S. G. (2020) · Future Internet · doi:10.3390/fi12110180 · Cited by 107
“They lack traffic diversity, do not cover different types of attacks, and do not reflect current network traffic trends [10].”
BackgroundMalicious Network Traffic Detection Based on Deep Neural Networks and Association Analysis
Gao, M., Ma, L., Liu, H., et al. (2020) · Sensors · doi:10.3390/s20051452 · Cited by 96
“We also carried out comparison experiments with other learning models using dataset CICIDS 2017 [23].”
MethodA Spectrogram Image-Based Network Anomaly Detection System Using Deep Convolutional Neural Network
Khan, A. S., Ahmad, Z., Abdullah, J., & Ahmad, F. (2021) · IEEE Access · doi:10.1109/access.2021.3088149 · Cited by 96
“This dataset contains records of the most common benign and different types of attack network flows [46].”
BackgroundDDosTC: A Transformer-Based Network Attack Detection Hybrid Mechanism in SDN
Wang, H., & Li, W. (2021) · Sensors · doi:10.3390/s21155047 · Cited by 103
“The use of a hybrid deep learning model for intrusion detection was explored in [22], which used a hybrid model of LSTM, CNN, and GRU applied to the CICIDS2017 dataset [23]; the highest accuracy obtained was 98.97% [24].”
MethodInfluentialJaw, E., & Wang, X. (2021) · Symmetry · doi:10.3390/sym13101764 · Cited by 110
A Survey of Security in UAVs and FANETs: Issues, Threats, Analysis of Attacks, and Solutions
Ceviz, Ö., Şen, S., & Sadioglu, P. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3515051 · Cited by 113
“Yet again, the utilization of the CSE-CIC IDS-2018 dataset [199] for attack detection in this context lacks alignment with FANETs and the intricate infrastructure of 5G networks.”
Gupta, N., Jindal, V., & Bedi, P. (2021) · Computer Networks · doi:10.1016/j.comnet.2021.108076 · Cited by 103
Transfer-Learning-Based Intrusion Detection Framework in IoT Networks
Rodríguez, E., Valls, P., Otero, B., Costa, J. J., Verdú, J., Pajuelo, M. A., & Canal, R. (2022) · Sensors · doi:10.3390/s22155621 · Cited by 102
“They implement transfer learning using CNN, and they evaluate the solution with the CICIDS2017 [22] as base dataset and different custom target datasets for the different IoT networks.”
MethodDoS Attack Detection Using Machine Learning and Neural Network
Wankhede, S., & Kshirsagar, D. D. (2018) · International Conference on Computing Communication Control and automation · doi:10.1109/iccubea.2018.8697702 · Cited by 94
“CIC IDS 2017 dataset [15] consists of labeled data of”
BackgroundA Multiple-Layer Representation Learning Model for Network-Based Attack Detection
Zhang, X., Chen, J., Zhou, Y., et al. (2019) · IEEE Access · doi:10.1109/access.2019.2927465 · Cited by 94
“The dataset used in this paper consists of two open source datasets, including UNSW-NB15 (Australian Cyber Security Center dataset) [47], [48] and CIC-IDS2017 (Canadian Cyber Security Institute dataset) [49].”
MethodThe Evolution of Federated Learning-Based Intrusion Detection and Mitigation: A Survey
Lavaur, L., Pahl, M., Busnel, Y., & Autrel, F. (2022) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2022.3177512 · Cited by 110
Alahmadi, A. A., Aljabri, M., Alhaidari, F. A., et al. (2023) · Electronics · doi:10.3390/electronics12143103 · Cited by 94
Toward Early and Accurate Network Intrusion Detection Using Graph Embedding
Hu, X., Gao, W., Cheng, G., Li, R., Zhou, Y., & Wu, H. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3318960 · Cited by 105
“We visualize the constructed flow graphs with Matplotlib. end if 9: end for 10: for i in range (0, len ( B ) - 1) do end if 17: end for 18: for i in range (0, len ( B )) do end if 24: end for 25: return G from three randomly selected flows of different attack types on the CICIDS2017 dataset [48].”
Kalakoti, R., Bahsi, H., & Nõmm, S. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3360626 · Cited by 93
“The authors developed an Oracle-based Explainer module that measures the distance between clusters formed from the CICIDS2017 data set [43] and the test instances.”
MethodEvolving techniques in cyber threat hunting: A systematic review
Mahboubi, A., Luong, K., Aboutorab, H., Bui, H. T., Jarrad, G., Bahutair, M., Camtepe, S., Pogrebna, G., Ahmed, E., Barry, B., et al. (2024) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2024.104004 · Cited by 94
Aksu, D., & Aydın, M. A. (2018) · International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT) · doi:10.1109/ibigdelft.2018.8625370 · Cited by 97
“Therefore we used an up-to-date and new CICIDS2017 dataset [12] in our study.”
BackgroundMethodExperimental Review of Neural-Based Approaches for Network Intrusion Management
Mauro, M. D., Galatro, G., & Liotta, A. (2020) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2020.3024225 · Cited by 98
“These datasets have been created starting from an experimental testbed under controlled conditions [11], whereby an attack network (including a router, a switch, and four PCs equipped with Kali Linux OS, which is a popular Linux distribution to perform penetration testing) is counterposed to a victim network (includin…”
BackgroundMethodA Novel Network Intrusion Detection System Based on CNN
Chen, L., Kuang, X., Xu, A., et al. (2020) · International Conference on Advanced Cloud and Big Data · doi:10.1109/cbd51900.2020.00051 · Cited by 93
A survey of outlier detection in high dimensional data streams
Souiden, I., Omri, M. N., & Brahmi, Z. (2022) · Computer Science Review · doi:10.1016/j.cosrev.2022.100463 · Cited by 107
Mohammadian, H., Ghorbani, A. A., & Lashkari, A. H. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110173 · Cited by 100
Self-citationHybrid Deep Learning: An Efficient Reconnaissance and Surveillance Detection Mechanism in SDN
Malik, J., Akhunzada, A., Bibi, I., Imran, M., Musaddiq, A., & Kim, S. W. (2020) · IEEE Access · doi:10.1109/access.2020.3009849 · Cited by 92
Data Analytics-enabled Intrusion Detection: Evaluations of Linux ToN_IoT Datasets
Moustafa, N., Ahmed, M., & Ahmed, S. (2021) · UNSWorks (University of New South Wales, Sydney, Australia) · doi:10.1109/trustcom50675.2020.00100 · Cited by 92
Mendonça, R. V., Silva, J. C., Rosa, R. L., Saadi, M., Rodríguez, D. Z., & Farouk, A. (2021) · Expert Systems · doi:10.1111/exsy.12917 · Cited by 98
“In this work, two datasets are used, an open-source dataset named DS2OS (Pahl et al., 2016) for training and testing, and another dataset for performance evaluation, the CICIDS2017 (Sharafaldin et al., 2018).”
BackgroundMethodInfluentialA Novel Multi-Stage Approach for Hierarchical Intrusion Detection
Verkerken, M., D’hooge, L., Sudyana, D., et al. (2023) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2023.3259474 · Cited by 92
Intrusion Detection in SDN-Based Networks: Deep Recurrent Neural Network Approach
Tang, T. A., McLernon, D., Mhamdi, L., Zaidi, S. A. R., & Ghogho, M. (2019) · Advanced sciences and technologies for security applications · doi:10.1007/978-3-030-13057-2_8 · Cited by 104
Javeed, D., Gao, T., Khan, M. T., & Ahmad, I. (2021) · Sensors · doi:10.3390/s21144884 · Cited by 91
“Therefore, the proposed work used state of art publicly available data set, CICIDS 2018 [47].”
Anomaly Detection Dataset for Industrial Control Systems
Dehlaghi-Ghadim, A., Moghadam, M. H., Balador, A., & Hansson, H. (2023) · IEEE Access · doi:10.1109/access.2023.3320928 · Cited by 101
“To protect ICSs against the increasing risk of cyberattacks, Intrusion Detection Systems (IDS) are introduced [11].”
InfluentialIntrusion detection system for cyberattacks in the Internet of Vehicles environment
Korium, M. S., Saber, M., Beattie, A., Narayanan, A., Sahoo, S., & Nardelli, P. H. J. (2023) · Ad Hoc Networks · doi:10.1016/j.adhoc.2023.103330 · Cited by 95
A, K. V., & Jacob, T. P. (2020) · ICT Express · doi:10.1016/j.icte.2020.12.004 · Cited by 90
Attention based multi-agent intrusion detection systems using reinforcement learning
Sethi, K., Madhav, Y. V., Kumar, R., & Bera, P. (2021) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2021.102923 · Cited by 95
Split Federated Learning for 6G Enabled-Networks: Requirements, Challenges, and Future Directions
Hafi, H., Brik, B., Frangoudis, P. A., Ksentini, A., & Bagaa, M. (2024) · IEEE Access · doi:10.1109/access.2024.3351600 · Cited by 90
“• CIC-IDS2017 [202]: It is an intrusion detection labeled dataset published by the Canadian Institute for Cybersecurity in 2017.”
An Empirical Evaluation of Deep Learning for Network Anomaly Detection
Malaiya, R. K., Kwon, D., Suh, S. C., Kim, H., Kim, I., & Kim, J. (2019) · IEEE Access · doi:10.1109/access.2019.2943249 · Cited by 99
“Similar with the result collected using UNSW-NB15, Seq2Seq outperforms the other methods for IDS2017.”
MethodResultInfluentialQaddoura, R., Al-Zoubi, A. M., Almomani, I., & Faris, H. (2021) · Applied Sciences · doi:10.3390/app11073022 · Cited by 93
“The model utilizes the Synthetic Minority Oversampling Technique (SMOTE) to apply the oversampling technique on CICIDS2017 and UNSW-15 datasets.”
MethodAn Intrusion Detection System for Edge-Envisioned Smart Agriculture in Extreme Environment
Javeed, D., Gao, T., Saeed, M. S., et al. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3288544 · Cited by 87
“The IoT-based datasets CIC-IDS2018 [28], ToN-IoT [29], and Edge-IIoTset [30] datasets are used to evaluate the performance and efficacy of the proposed IDS.”
MethodSaheed, Y. K., & Chukwuere, J. E. (2024) · Results in Engineering · doi:10.1016/j.rineng.2024.103171 · Cited by 104
Network traffic fusion and analysis against DDoS flooding attacks with a novel reversible sketch
Jing, X., Yan, Z., Jiang, X., & Pedrycz, W. (2018) · Information Fusion · doi:10.1016/j.inffus.2018.10.013 · Cited by 87
Metadata
- Authors
- Sharafaldin, I., Habibi Lashkari, A., & Ghorbani, A. A.
- Affiliations
- University of New Brunswick (CA)
- University of New Brunswick (UNB), Canada
- Venue
- Proceedings of the 4th International Conference on Information Systems Security and Privacy
- Published
- 2018
- Pages
- 108-116
- OpenAlex
- W2789828921
- Cited by
- 5,318
- References
- 27
- Citations by year
- 2026: 1170 · 2025: 1032 · 2024: 803 · 2023: 704 · 2022: 558 · 2021: 518 · 2020: 332 · 2019: 165 · 2018: 35
- FWCI
- 215.46
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- International Conference on Information Systems Security and Privacy (ICISSP) (publishedVersion, cc-by-nc-nd)
- 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 › Internet Traffic Analysis and Secure E-voting
- Keywords
- intrusion detection, intrusion detection datasets, network traffic characterization, network security, dataset construction
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