Автор: Alhassan Seiba, Gaddafi Abdul-Salaam, Yaw Missah, Mohammad Hossein Anisi
Организация: Kwame Nkrumah University of Science and Technology, University of Essex


Ключевые слова: Autoencoder, Intrusion Detection, Deep Learning, Feature Selection
Аннотация. Network Security has become a major concern to governments, businesses and individuals all over the world as cybercriminals continuously attack networks and cause harm to personal and organizational data. Different forms of Intrusion Detection Systems(IDSs) have been proposed over the years to minimize these cyberattacks. Several researchers have tried to improve upon the detection accuracy and thus, reducing false alarm rates posed by some of the IDSs. In this paper, we conducted a chronological systematic review of hybrid intrusion detection systems covering all domains. In all, about 300 recent research articles were selected in the area but only 146 articles were able to meet the given quality assurance test. A critical review of the selected articles revealed that 61% did not carry out proper feature selection as a data preprocessing step and as low as 35% handled an imbalanced dataset. We have also done extensive discussions, spanning eleven years of research works on the existing Intrusion Detection Systems.


Ahmad, I. et al. (2022) ‘An Efficient Network Intrusion Detection and Classification System’, pp. 1–15.
Ahmad, Z. et al. (2021) ‘Network intrusion detection system: A systematic study of machine learning and deep learning approaches’, Transactions on Emerging Telecommunications Technologies, 32(1), pp. 1–29. Available at: https://doi.org/10.1002/ett.4150.
Ahmim, A., Derdour, M. and Ferrag, M.A. (2018) ‘An intrusion detection system based on combining probability predictions of a tree of classifiers’, International Journal of Communication Systems, 31(9), pp. 1–17. Available at: https://doi.org/10.1002/dac.3547.
Alghayadh, F. and Debnath, D. (2020) ‘A Hybrid Intrusion Detection System for Smart Home Security’, IEEE International Conference on Electro Information Technology, 2020-July, pp. 319–323. Available at: https://doi.org/10.1109/EIT48999.2020.9208296.
Ali, M.H. et al. (2018) ‘A hybrid Particle swarm optimization -Extreme Learning Machine approach for Intrusion Detection System’, 2018 IEEE 16th Student Conference on Research and Development, SCOReD 2018, pp. 1–4. Available at: https://doi.org/10.1109/SCORED.2018.8711287
Ali, M.H. and Aasi, A. (no date) ‘Improved Intrusion Detection Accuracy Based on Optimization Fast Learning Network Model’.
Aljamal, I. et al. (2019) ‘Hybrid intrusion detection system using machine learning techniques in cloud computing environments’, Proceedings - 2019 IEEE/ACIS 17th International Conference on Software Engineering Research, Management and Application, SERA 2019, pp. 84–89. Available at: https://doi.org/10.1109/SERA.2019.8886794.
Almiani, M. et al. (2020) ‘Cascaded hybrid intrusion detection model based on SOM and RBF neural networks’, Concurrency and Computation: Practice and Experience, 32(21), pp. 1–14. Available at: https://doi.org/10.1002/cpe.5233
Anderson, J.P. (1980) ‘Computer security threat monitoring and surveillance’, Technical Report James P Anderson Co Fort Washington Pa, p. 56. Available at: https://doi.org/citeulike-article-id:592588.
Aravamudhan, P. (2022) ‘using Hybrid Deep Learning’.
Atefi, K. (2016) ‘Anomaly Detection Based on Profile Signature in Network Using Machine Learning Technique’, pp. 71–76.
Atefi, K. (2020) ‘A Hybrid Anomaly Classification with Deep Learning ( DL ) and Binary Algorithms ( BA ) as Optimizer in the Intrusion Detection System ( IDS )’, (Cspa), pp. 28–29.
Aung, Y.Y. (2017) ‘An Analysis of Random Forest Algorithm Based Network Intrusion Detection System’, pp. 127–132
Aung, Y.Y. (2018a) ‘Hybrid Intrusion Detection System using K-means and K-Nearest Neighbors Algorithms’, 2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS), pp. 34–38
Aung, Y.Y. (2018b) ‘Hybrid Intrusion Detection System using K-means and Random Tree Algorithms’, 2018 19th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), pp. 218–223
Ayyagari, M.R. et al. (2021) ‘Intrusion detection techniques in network environment: a systematic review’, Wireless Networks, 27(2), pp. 1269–1285. Available at: https://doi.org/10.1007/s11276-020-02529-3.
Azawii, A. and Lateef, A. (2020) ‘Hybrid Intrusion Detection System Based on Deep Learning’
Azzaoui, H. (no date) ‘Two-Stages Intrusion Detection System Based On Hybrid Methods’
Bangui, H. (2021) ‘A hybrid machine learning model for intrusion detection in VANET’, Computing [Preprint]. Available at: https://doi.org/10.1007/s00607-021-01001-0
Bangui, H. et al. (2021) ‘ScienceDirect A Hybrid Hybrid Data-driven Data-driven Model Model for for Intrusion Intrusion Detection Detection in in VANET’, Procedia Computer Science, 184, pp. 516–523. Available at: https://doi.org/10.1016/j.procs.2021.03.065
Barati, M. et al. (2014) ‘Distributed Denial of Service Detection Using Hybrid Machine Learning Technique’, pp. 268–273
Batiha, T. and Krömer, P. (2020) ‘Design and analysis of efficient neural intrusion detection for wireless sensor networks’, Concurrency Computation , (June), pp. 1–12. Available at: https://doi.org/10.1002/cpe.6152
Bhati, B.S. et al. (2021) ‘An improved ensemble based intrusion detection technique using XGBoost’, Transactions on Emerging Telecommunications Technologies, 32(6), pp. 1–15. Available at: https://doi.org/10.1002/ett.4076
Borisenko, B.B. et al. (2018) ‘Intrusion Detection Using Multilayer Perceptron and Neural Networks with Long Short-Term Memory
Bouzar-benlabiod, L. et al. (2020) ‘RNN-VED for Reducing False Positive Alerts in Host-based Anomaly Detection Systems’, pp. 17–24. Available at: https://doi.org/10.1109/IRI49571.2020.00011.
Bovenzi, G. et al. (2020) ‘A Hierarchical Hybrid Intrusion Detection Approach in IoT Scenarios
Camacho, J. et al. (2016) ‘PCA-based multivariate statistical network monitoring for anomaly detection’, Computers and Security, 59, pp. 118–137. Available at: https://doi.org/10.1016/j.cose.2016.02.008
Can, H. and Albayrak, Z. (2023) ‘Engineering Science and Technology , an International Journal A hybrid CNN + LSTM-based intrusion detection system for industrial IoT networks’, Engineering Science and Technology, an International Journal, 38, p. 101322. Available at: https://doi.org/10.1016/j.jestch.2022.101322
‘Cascaded classifier approach based on Adaboost to increase detection rate of rare network attack categories’ (2012), pp. 417–422.
Chen, W. (2020) ‘A hybrid feature extraction network for intrusion detection based on global attention mechanism’, pp. 481–485. Available at: https://doi.org/10.1109/CIBDA50819.2020.00114
Chitrakar, R. and Chuanhe, H. (2012a) ‘Anomaly based Intrusion Detection using Hybrid Learning Approach of combining k-Medoids Clustering and Naïve Bayes Classification’, (September). Available at: https://doi.org/10.1109/WiCOM.2012.6478433
Chitrakar, R. and Chuanhe, H. (2012b) ‘Anomaly Detection using Support Vector Machine Classification with k-Medoids Clustering’, pp. 1–5
Chkirbene, Z. et al. (2020) ‘Hybrid Machine Learning For Network Anomaly Intrusion Detection’, pp. 163–170.
Computing, N., Sheikhan, M. and Jadidi, Z. (2014) ‘Mansour Sheikhan & Zahra Jadidi’, (November). Available at: https://doi.org/10.1007/s00521-012-1263-0.
Das, I. (2021) ‘Serial and Parallel based Intrusion Detection System using Machine Learning’, pp. 19–20.
Devan, P. and Khare, N. (2020) ‘An efficient XGBoost – DNN-based classification model for network intrusion detection system’, Neural Computing and Applications, 0123456789. Available at: https://doi.org/10.1007/s00521-020-04708-x.
Donkol, A.A.B.D.E. et al. (2023) ‘Optimization of Intrusion Detection Using Likely Point PSO and Enhanced LSTM-RNN Hybrid Technique in Communication Networks’, IEEE Access, 11(February), pp. 9469–9482. Available at: https://doi.org/10.1109/ACCESS.2023.3240109
Dwivedi, S., Vardhan, M. and Tripathi, S. (2020) ‘An effect of chaos grasshopper optimization algorithm for protection of network infrastructure’, 176(August 2019). Available at: https://doi.org/10.1016/j.comnet.2020.107251
Dwivedi, S., Vardhan, M. and Tripathi, S. (2021) ‘Building an efficient intrusion detection system using grasshopper optimization algorithm for anomaly detection’, Cluster Computing, 24(3), pp. 1881–1900. Available at: https://doi.org/10.1007/s10586-020-03229-5.
Elhefnawy, R., Abounaser, H. and Badr, A.M.R. (2020) ‘A Hybrid Nested Genetic-Fuzzy Algorithm Framework for Intrusion Detection and Attacks’, 8. Available at: https://doi.org/10.1109/ACCESS.2020.2996226.
Enigo, F. (2021) ‘New Attacks Using Machine Learning’, (June 2020). Available at: https://doi.org/10.1109/ICCES48766.2020.9137888
Feng, W. et al. (2013) ‘Mining Network Data for Intrusion Detection through Combining SVM with Ant Colony’, Future Generation Computer Systems [Preprint]. Available at: https://doi.org/10.1016/j.future.2013.06.027
Foroushani, Z.A. and Li, Y. (2018) ‘Intrusion detection system by using hybrid algorithm of data mining technique’, ACM International Conference Proceeding Series, pp. 119–123. Available at: https://doi.org/10.1145/3185089.3185114
Gadal, S.M.A.M. and Mokhtar, R.A. (2017) ‘Anomaly detection approach using hybrid algorithm of data mining technique’, Proceedings - 2017 International Conference on Communication, Control, Computing and Electronics Engineering, ICCCCEE 2017 [Preprint]. Available at: https://doi.org/10.1109/ICCCCEE.2017.7867661
Gao, P., Yue, M. and Wu, Z. (2021) ‘A Novel Intrusion Detection Method Based on WOA Optimized Hybrid Kernel RVM’, pp. 1063–1069.
Garg, A. and Maheshwari, P. (2016) ‘A hybrid intrusion detection system: A review’, Proceedings of the 10th International Conference on Intelligent Systems and Control, ISCO 2016 [Preprint]. Available at: https://doi.org/10.1109/ISCO.2016.7726909
Ghanem, W.A.H.M. et al. (2020) ‘An Efficient Intrusion Detection Model Based on Hybridization of Artificial Bee Colony and Dragonfly Algorithms for Training Multilayer Perceptrons’. Available at: https://doi.org/10.1109/ACCESS.2020.3009533.
Ghanem, W.A.H.M. and Jantan, A. (2019) A new approach for intrusion detection system based on training multilayer perceptron by using enhanced Bat algorithm, Neural Computing and Applications. Springer London. Available at: https://doi.org/10.1007/s00521-019-04655-2.
Ghazi, A. El (2020) ‘Machine learning and datamining methods for hybrid IoT intrusion detection’.
Haghnegahdar, L. and Wang, Y. (2019) ‘A whale optimization algorithm-trained artificial neural network for smart grid cyber intrusion detection’, Neural Computing and Applications, 0123456789. Available at: https://doi.org/10.1007/s00521-019-04453-w.
Hajisalem, V. and Babaie, S. (2018) ‘A hybrid intrusion detection system based on ABC-AFS algorithm for misuse and anomaly detection’, Computer Networks, 136, pp. 37–50. Available at: https://doi.org/10.1016/j.comnet.2018.02.028
He, Haitao et al. (2019) ‘A Novel Multimodal-Sequential Approach Based on Multi-View Features for Network Intrusion Detection’, IEEE Access, 7, pp. 183207–183221. Available at: https://doi.org/10.1109/ACCESS.2019.2959131.
Hedar, A.R. et al. (2015) ‘Hybrid evolutionary algorithms for data classification in intrusion detection systems’, 2015 IEEE/ACIS 16th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2015 - Proceedings [Preprint]. Available at: https://doi.org/10.1109/SNPD.2015.7176208
Henry, A. et al. (2023) ‘Composition of Hybrid Deep Learning Model and Feature Optimization for Intrusion Detection System’.
Hosseini, S. and Azizi, M. (2019) ‘The Hybrid Technique for DDoS Detection with Supervised Learning Algorithms’, Computer Networks [Preprint]. Available at: https://doi.org/10.1016/j.comnet.2019.04.027
Hosseini, S., Mohammad, B. and Zade, H. (2020) ‘New Hybrid Method for Attack Detection Using Combination of Evolutionary Algorithms , SVM , and ANN’, Computer Networks, p. 107168. Available at: https://doi.org/10.1016/j.comnet.2020.107168
Jiang, J. and Lv, B. (no date) ‘RST-RF : A Hybrid Model based on Rough Set Theory and Random Forest for Network Intrusion Detection’, pp. 77–81.
Jiang, K. et al. (2020) ‘Network Intrusion Detection Combined Hybrid Sampling With Deep Hierarchical Network’, IEEE Access, 8(3), pp. 32464–32476. Available at: https://doi.org/10.1109/ACCESS.2020.2973730.
Karthikeyan, S.V.P. (2019) ‘Hybrid optimization scheme for intrusion detection using considerable feature selection’, Neural Computing and Applications, 2. Available at: https://doi.org/10.1007/s00521-019-04477-2
Kaur, S. and Singh, M. (2020) ‘Hybrid intrusion detection and signature generation using Deep Recurrent Neural Networks’, Neural Computing and Applications, 32(12), pp. 7859–7877. Available at: https://doi.org/10.1007/s00521-019-04187-9.
Kec, D. (2021) ‘Feature selection using cloud-based parallel genetic algorithm for intrusion detection data classification’, 5. Available at: https://doi.org/10.1007/s00521-021-05871-5.
Kevric, J., Jukic, S. and Subasi, A. (2016) ‘An effective combining classifier approach using tree algorithms for network intrusion detection’, Neural Computing and Applications [Preprint]. Available at: https://doi.org/10.1007/s00521-016-2418-1.
Khan, I.A., Pi, D. and Khan, Z.U. (2019) ‘HML-IDS : A Hybrid-Multilevel Anomaly Prediction Approach for Intrusion Detection in SCADA Systems’, IEEE Access, 7, pp. 89507–89521. Available at: https://doi.org/10.1109/ACCESS.2019.2925838.
Khan, M.A. (2021) ‘HCRNNIDS: Hybrid convolutional recurrent neural network-based network intrusion detection system’, Processes, 9(5). Available at: https://doi.org/10.3390/pr9050834.
Khan, M.A. and Karim, R. (2019) ‘SS symmetry A Scalable and Hybrid Intrusion Detection System Based on the Convolutional-LSTM Network’.
Khraisat, A. et al. (2019) ‘Survey of intrusion detection systems: techniques, datasets and challenges’, Cybersecurity, 2(1). Available at: https://doi.org/10.1186/s42400-019-0038-7.
Khraisat, A. et al. (2020) ‘Hybrid intrusion detection system based on the stacking ensemble of C5 decision tree classifier and one class support vector machine’, Electronics (Switzerland), 9(1). Available at: https://doi.org/10.3390/electronics9010173.
Kim, G., Lee, S. and Kim, S. (2014) ‘Expert Systems with Applications A novel hybrid intrusion detection method integrating anomaly detection with misuse detection’, Expert Systems With Applications, 41(4), pp. 1690–1700. Available at: https://doi.org/10.1016/j.eswa.2013.08.066.
Kim, T. and Pak, W. (2021) ‘Hybrid Classification for High-Speed and High-Accuracy Network Intrusion Detection System’, Hybrid intelligent systems for detecting network intrusions, 9, pp. 83806–83817. Available at: https://doi.org/10.1109/ACCESS.2021.3087201.
Kumar, K.S.A. and Mohan, V.N. (2014) ‘Adaptive Fuzzy Neural Network Model for intrusion detection’, Proceedings of 2014 International Conference on Contemporary Computing and Informatics, IC3I 2014, pp. 987–991. Available at: https://doi.org/10.1109/IC3I.2014.7019811.
Kumari, A. (2020) ‘A Hybrid Intrusion Detection System Based on Decision Tree and Support Vector Machine’, pp. 396–400.
Kumari, V. (2017) ‘active learning SVM and fuzzy c-means clustering’, pp. 481–485.
Landress, A.D. (2016) ‘A Hybrid Approach to Reducing the False Positive Rate in Unsupervised Machine Learning Intrusion Detection’.
Latah, M. and Toker, L. (2020) ‘An efficient flow-based multi-level hybrid intrusion detection system for software-defined networks’, CCF Transactions on Networking, 3(3–4), pp. 261–271. Available at: https://doi.org/10.1007/s42045-020-00040-z.
Li, D. (2020) ‘Improving Attack Detection Performance in NIDS Using GAN’, pp. 817–825. Available at: https://doi.org/10.1109/COMPSAC48688.2020.0-162.
Li, K., Zhang, Y. and Wang, S. (2021) ‘An Intrusion Detection System based on PSO-GWO Hybrid Optimized Support Vector Machine’, Proceedings of the International Joint Conference on Neural Networks, 2021-July. Available at: https://doi.org/10.1109/IJCNN52387.2021.9534325.
Li, Y. et al. (2022) ‘Research on Intrusion Detection Based on Neural Network Optimized by Genetic Algorithm’, en, pp. 8–11.
Liu, C., Gu, Z. and Wang, J. (2021) ‘A Hybrid Intrusion Detection System Based on Scalable K-Means+ Random Forest and Deep Learning’, IEEE Access, 9, pp. 75729–75740. Available at: https://doi.org/10.1109/ACCESS.2021.3082147.
Madani, P. and Vlajic, N. (2018) ‘Robustness of deep autoencoder in intrusion detection under adversarial contamination’, ACM International Conference Proceeding Series [Preprint]. Available at: https://doi.org/10.1145/3190619.3190637.
Maleh, Y. et al. (2015) ‘A global hybrid intrusion detection system for Wireless Sensor Networks’, Procedia Computer Science, 52(1), pp. 1047–1052. Available at: https://doi.org/10.1016/j.procs.2015.05.108.
Malik, A.J. and Khan, F.A. (2017) ‘A hybrid technique using binary particle swarm optimization and decision tree pruning for network intrusion detection’, Cluster Computing, 21(1), pp. 667–680. Available at: https://doi.org/10.1007/s10586-017-0971-8.
Malik, J. et al. (2020) ‘Hybrid Deep Learning : An Efficient Reconnaissance and Surveillance Detection Mechanism in SDN’, pp. 134695–134706. Available at: https://doi.org/10.1109/ACCESS.2020.3009849.
Maseno, E.M., Wang, Z. and Xing, H. (2022) ‘A Systematic Review on Hybrid Intrusion Detection System’, 2022.
Matel, E.C., Sison, A.M. and Medina, R.P. (2019) ‘Optimization of Network Intrusion Detection System Using Genetic Algorithm with Improved Feature Selection Technique’.
Mazumder, M.R. et al. (no date) ‘Network Intrusion Detection Using Hybrid Machine Learning Model’.
Megantara, A.A. and Ahmad, T. (2021) ‘A hybrid machine learning method for increasing the performance of network intrusion detection systems’, Journal of Big Data, 8(1). Available at: https://doi.org/10.1186/s40537-021-00531-w.
Mendjeli, C.A. (2017) ‘A hybrid Deep Learning Strategy for an Anomaly Based N-IDS’.
Meng, F. et al. (2017) ‘An effective network attack detection method based on kernel PCA and LSTM- RNN’, 2017 International Conference on Computer Systems, Electronics and Control (ICCSEC), pp. 568–572.
Mohd, N., Singh, A. and Bhadauria, H.S. (2021) ‘Intrusion Detection System Based on Hybrid Hierarchical Classifiers’, Wireless Personal Communications [Preprint], (0123456789). Available at: https://doi.org/10.1007/s11277-021-08655-1.
Mojtaba, S. et al. (2015) ‘A New Intrusion Detection Approach using PSO based Multiple Criteria Linear Programming’, Procedia - Procedia Computer Science, 55(Itqm), pp. 231–237. Available at: https://doi.org/10.1016/j.procs.2015.07.040.
Network, I.N. (2016) ‘Improving K-Means CLUSTERING Clustering Using IMPROVING K-MEANS USING Discretization TECHNIQUE Technique In Network DISCRETIZATION Intrusion DETECTION Detection System INTRUSION’, pp. 248–252.
Nivaashini, M. and Thangaraj, P. (2018) ‘A Framework of Novel Feature Set Extraction based Intrusion Detection System for Internet of Things using Hybrid Machine Learning Algorithms’, 2018 International Conference on Computing, Power and Communication Technologies (GUCON), pp. 44–49.
Oluwaseun, R. et al. (2021) ‘ScienceDirect ScienceDirect ScienceDirect An Enhanced Intrusion Detection System using Particle Swarm Optimization Extraction Technique Science 10th International Young Feature An Enhanced Intrusion Detection System using Particle Swarm An Enhanced Intrus’, Procedia Computer Science, 193, pp. 504–512. Available at: https://doi.org/10.1016/j.procs.2021.10.052.
Om, H. (2012) ‘A Hybrid System for Reducing the False Alarm Rate of Anomaly Intrusion Detection System’.
Öney, M.U. and Peker, S. (2019) ‘The Use of Artificial Neural Networks in Network Intrusion Detection: A Systematic Review’, 2018 International Conference on Artificial Intelligence and Data Processing, IDAP 2018, pp. 1–6. Available at: https://doi.org/10.1109/IDAP.2018.8620746.
Pakanzad, S.N. (2020) ‘Providing a Hybrid Approach for Detecting Malicious Traffic on the Computer Networks Using Convolutional Neural Networks’.
Pattawaro, A. (2018) ‘Anomaly-Based Network Intrusion Detection System through Feature Selection and Hybrid Machine Learning Technique’, 2018 16th International Conference on ICT and Knowledge Engineering (ICT&KE), pp. 1–6. Available at: https://doi.org/10.1109/ICTKE.2018.8612331.
Pitre, P. (2022) ‘An Intrusion Detection System for Zero-Day Attacks to Reduce False Positive Rates’, pp. 1–6.
Pokharel, P. (2020) ‘Intrusion Detection System based on Hybrid Classifier and User Profile Enhancement Techniques’, pp. 137–144.
Polat, H. and Polat, O. (2020) ‘Detecting DDoS Attacks in Software-Defined Networks Through Feature Selection Methods and Machine Learning Models’
Prabhakaran, V. and Kulandasamy, A. (2021) ‘Hybrid semantic deep learning architecture and optimal advanced encryption standard key management scheme for secure cloud storage and intrusion detection’, Neural Computing and Applications, 5. Available at: https://doi.org/10.1007/s00521-021-06085-5.
Pre-proof, J. (2019) ‘Evolving deep learning architectures for network intrusion detection using a double PSO metaheuristic’, Computer Networks, p. 107042. Available at: https://doi.org/10.1016/j.comnet.2019.107042.
Pu, G. et al. (2021) ‘A Hybrid Unsupervised Clustering-Based Anomaly Detection Method’, pp. 146–153.
Qaddoura, R. et al. (2021) ‘A Multi-Layer Classification Approach for Intrusion Detection in IoT Networks Based on Deep Learning’, pp. 1–21.
Qazanfari, K. (2017) ‘A Novel Hybrid Anomaly Based Intrusion Detection Method’, (November 2012). Available at: https://doi.org/10.1109/ISTEL.2012.6483122.
Rabbani, M. et al. (2020) ‘A Hybrid Machine Learning Approach for Malicious Behaviour Detection and Recognition in Cloud Computing’, Journal of Network and Computer Applications, p. 102507. Available at: https://doi.org/10.1016/j.jnca.2019.102507.
Rahmani, R. et al. (2015) ‘A hybrid method consisting of GA and SVM for intrusion detection system A hybrid method consisting of GA and SVM for intrusion detection system’, Neural Computing and Applications [Preprint], (August). Available at: https://doi.org/10.1007/s00521-015-1964-2.
Raja, S. and Ramaiah, S. (2017) ‘An Efficient Fuzzy-Based Hybrid System to Cloud Intrusion Detection’, International Journal of Fuzzy Systems, 19(1), pp. 62–77. Available at: https://doi.org/10.1007/s40815-016-0147-3.
Ravale, P.U., Marathe, P.N. and Padiya, P.P. (2015) ‘Feature Selection Based Hybrid Anomaly Intrusion Detection System Using K Means and RBF Kernel Function’, Procedia - Procedia Computer Science, 45, pp. 428–435. Available at: https://doi.org/10.1016/j.procs.2015.03.174.
Razib, M.A.L. et al. (2022) ‘Cyber Threats Detection in Smart Environments Using SDN-Enabled DNN-LSTM Hybrid Framework’, IEEE Access, 10, pp. 53015–53026. Available at: https://doi.org/10.1109/ACCESS.2022.3172304.
Sadiq, A.L.I.S. et al. (2018) ‘An Efficient IDS Using Hybrid Magnetic Swarm Optimization in WANETs’, IEEE Access, 6, pp. 29041–29053. Available at: https://doi.org/10.1109/ACCESS.2018.2835166.
Sagar, S., Shrivastava, A. and Gupta, C. (2018) ‘Feature Reduction and Selection Based Optimization for Hybrid Intrusion Detection System Using PGO followed by SVM’, 2018 International Conference on Advanced Computation and Telecommunication (ICACAT), pp. 1–7.
Saleh, A.I., Talaat, F.M. and Labib, L.M. (2019) ‘A hybrid intrusion detection system (HIDS) based on prioritized k-nearest neighbors and optimized SVM classifiers’, Artificial Intelligence Review, 51(3), pp. 403–443. Available at: https://doi.org/10.1007/s10462-017-9567-1.
Saleh, M. et al. (2022) ‘Towards SDN-Enabled , Intelligent Intrusion Detection System for Internet of Things ( IoT )’, 10.
Sayed, A. et al. (2013) ‘Multi-layer hybrid machine learning techniques for anomalies detection and classification approach Vj and a P ( at I vJ ) - n + m 2013 13th International Conference on Hybrid Intelligent Systems ( HIS )’, pp. 215–220.
Seo, W. and Pak, W. (2021) ‘Real-Time Network Intrusion Prevention System Based on Hybrid Machine Learning’, 9. Available at: https://doi.org/10.1109/ACCESS.2021.3066620.
Sharma, A. and Tyagi, U. (2021) ‘A Hybrid Approach of ANN-GWO Technique for Intrusion Detection’, pp. 1–6.
Sheikhan, M. and Sharifi, M. (2012) ‘Gravitational search algorithm – optimized neural misuse detector with selected features by fuzzy grids – based association rules mining’. Available at: https://doi.org/10.1007/s00521-012-1204-y.
Shizhao, W. and Tianbo, W. (2019) ‘A Novel Intrusion Detector Based on Deep Learning Hybrid Methods’, pp. 300–305. Available at: https://doi.org/10.1109/BigDataSecurity-HPSC-IDS.2019.00062.
Shona, D. and Kumar, M.S. (2019) ‘Efficient IDs for MANET Using Hybrid Firefly with a Genetic Algorithm’, 2018 International Conference on Inventive Research in Computing Applications (ICIRCA), (November), pp. 191–194. Available at: https://doi.org/10.1109/ICIRCA.2018.8597268
Shukla, A.K. (2020) ‘Detection of anomaly intrusion utilizing self-adaptive grasshopper optimization algorithm’, Neural Computing and Applications, 7. Available at: https://doi.org/10.1007/s00521-020-05500-7
Shukla, P. (2017) ‘ML-IDS : A Machine Learning Approach to Detect Wormhole Attacks in Internet of Things’, (September).
Singh, A., Chatterjee, K. and Satapathy, S.C. (2021) ‘An edge based hybrid intrusion detection framework for mobile edge computing’, Complex & Intelligent Systems [Preprint]. Available at: https://doi.org/10.1007/s40747-021-00498-4.
Singh, P. and Venkatesan, M. (2018) ‘Hybrid Approach for Intrusion Detection System’, Proceedings of the 2018 International Conference on Current Trends towards Converging Technologies, ICCTCT 2018, pp. 1–5. Available at: https://doi.org/10.1109/ICCTCT.2018.8551181.
Singhal, A. et al. (2021) ‘A Hybrid Machine Learning and Data Mining Based Approach to Network Intrusion Detection’, pp. 312–318.
Soheily-Khah, S., Marteau, P.F. and Bechet, N. (2018) ‘Intrusion detection in network systems through hybrid supervised and unsupervised machine learning process: A case study on the iscx dataset’, Proceedings - 2018 1st International Conference on Data Intelligence and Security, ICDIS 2018, pp. 219–226. Available at: https://doi.org/10.1109/ICDIS.2018.00043
Soheily-Khah, S., Marteau, P.F. and Bechet, N. (2018) ‘Intrusion detection in network systems through hybrid supervised and unsupervised machine learning process: A case study on the iscx dataset’, Proceedings - 2018 1st International Conference on Data Intelligence and Security, ICDIS 2018, pp. 219–226. Available at: https://doi.org/10.1109/ICDIS.2018.00043
Srikrishnan, A., Raaza, A. and Gopalakrishnan, S. (no date) ‘Machine Learning Based Intrusion Detection Systems Using HGWCSO And ETSVM Techniques’.
Subba, B., Biswas, S. and Karmakar, S. (2017) ‘Enhancing effectiveness of intrusion detection systems: A hybrid approach’, 2016 IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2016 [Preprint]. Available at: https://doi.org/10.1109/ANTS.2016.7947777.
Taher, K.A. (2019) ‘Network Intrusion Detection using Supervised Machine Learning Technique with Feature Selection’.
Tama, B.A., Comuzzi, M. and Rhee, K.H. (2019) ‘TSE-IDS: A Two-Stage Classifier Ensemble for Intelligent Anomaly-Based Intrusion Detection System’, IEEE Access, 7, pp. 94497–94507. Available at: https://doi.org/10.1109/ACCESS.2019.2928048.
Tang, Y. and Li, C. (2021) ‘An Online Network Intrusion Detection Model Based on Improved Regularized Extreme Learning Machine’, IEEE Access, PP, p. 1. Available at: https://doi.org/10.1109/ACCESS.2021.3093313.
Tekeo, A. (2019) ‘Hybrid Intrusion Detection System Using Machine Learning Techniques in Cloud Computing Environments’, pp. 84–89.
Thanigaivelan, N.K., Virtanen, S. and Isoaho, J. (2018) ‘Hybrid Internal Anomaly Detection System for IoT : Reactive Nodes with Cross-Layer Operation’, 2018.
Thaseen, S. and Kumar, A. (2017) ‘Intrusion detection model using fusion of chi-square feature selection and multi class SVM’, pp. 462–472.
Ullah, I., Mahmoud, Q.H. and Member, S. (2022) ‘Design and Development of RNN-based Anomaly Detection Model for IoT Networks’, IEEE Access, PP, p. 1. Available at: https://doi.org/10.1109/ACCESS.2022.3176317.
Umarani, C. and Kannan, S. (2020) ‘Intrusion detection system using hybrid tissue growing algorithm for wireless sensor network’, Peer-to-Peer Networking and Applications, 13(3), pp. 752–761. Available at: https://doi.org/10.1007/s12083-019-00781-9.
Varuna, S. (2015) ‘An Integration of K-Means Clustering and Naïve Bayes Classifier for Intrusion Detection’.
Velliangiri, S. and Pandey, H.M. (2020) ‘Jou rna lP’, Future Generation Computer Systems [Preprint]. Available at: https://doi.org/10.1016/j.future.2020.03.049.
Vidyapeetham, A.V. (2013) ‘A hybrid method based on Genetic Algorithm , Self-Organised Feature Map, and Support Vector Machine for better Network Anomaly Detection’.
Vu, L. et al. (2022) ‘Deep Generative Learning Models for Cloud Intrusion Detection Systems’, pp. 1–13.
Walkinshaw, N., Taylor, R. and Derrick, J. (2016) Inferring extended finite state machine models from software executions, Empirical Software Engineering. Available at: https://doi.org/10.1007/s10664-015-9367-7.
Wang, W. et al. (2020) ‘Cloud Intrusion Detection Method Based on Stacked Contractive Auto-Encoder and Support Vector Machine’, pp. 1–14. Available at: https://doi.org/10.1109/TCC.2020.3001017
Wankhade, A. (2016) ‘Distributed-Intrusion Detection System using combination of Ant Colony Optimization ( ACO ) and Support Vector Machine ( SVM )’, pp. 0–5. Available at: https://doi.org/10.1109/ICMETE.2016.94
Wisanwanichthan, T. and Thammawichai, M. (2021) ‘SVMA Double-Layered Hybrid Approach for Network Intrusion Detection System Using Combined Naive Bayes and’, IEEE Access, 9, pp. 138432–138450. Available at: https://doi.org/10.1109/ACCESS.2021.3118573
Xu, A. et al. (2020) ‘A Hybrid Deep Learning Model for Malicious Behavior Detection’, pp. 55–59. Available at: https://doi.org/10.1109/BigDataSecurity-HPSC-IDS49724.2020.00021.
Yang, L., Moubayed, A. and Shami, A. (2021) ‘MTH-IDS: A Multi-Tiered Hybrid Intrusion Detection System for Internet of Vehicles’, IEEE Internet of Things Journal, XX(XX), pp. 1–17. Available at: https://doi.org/10.1109/JIOT.2021.3084796
Zhang, C. et al. (2021) ‘A Novel Framework Design of Network Intrusion Detection Based on Machine Learning Techniques’, 2021.
Zhang, H. et al. (2019) ‘Using Machine Learning techniques to improve Intrusion Detection Accuracy’, pp. 308–310
Zhang, H. et al. (2020) ‘A Real-Time and Ubiquitous Network Attack Detection Based on Deep Belief Network and Support Vector Machine’, 7(3), pp. 790–799.
Zhang, L. et al. (2022) ‘A Hybrid Approach Toward Efficient and Accurate Intrusion Detection for In-Vehicle Networks’, 10. Available at: https://doi.org/10.1109/ACCESS.2022.3145007.
Zhang, X. (2019) ‘An Intrusion Detection System Based on Convolutional Neural Network for Imbalanced Network Traffic’, pp. 456–460
Zhang, Z. (no date) ‘XGBoosted Misuse Detection in LAN-Internal Traffic Dataset’.
Zhou, P., Zhang, H. and Liang, W. (2023) ‘Research on hybrid intrusion detection based on improved Harris Hawk optimization algorithm’. Available at: https://doi.org/10.1080/09540091.2023.2195595