Benchmarking framework for IDS classifiers in term of security and performance based on multicriteria analysis

<p>This research aims to assist the developers of intrusion detection systems (IDS) to make the right</p><p>selection decision of a suitable classification model. Many classification algorithms have been</p><p>developed to be used...

Full description

Saved in:
Bibliographic Details
Main Author: Alamleh, Amneh Hussein Mohd
Format: thesis
Language:eng
Published: 2022
Subjects:
Online Access:https://ir.upsi.edu.my/detailsg.php?det=9163
Tags: Add Tag
No Tags, Be the first to tag this record!
id oai:ir.upsi.edu.my:9163
record_format uketd_dc
institution Universiti Pendidikan Sultan Idris
collection UPSI Digital Repository
language eng
topic QA Mathematics
spellingShingle QA Mathematics
Alamleh, Amneh Hussein Mohd
Benchmarking framework for IDS classifiers in term of security and performance based on multicriteria analysis
description <p>This research aims to assist the developers of intrusion detection systems (IDS) to make the right</p><p>selection decision of a suitable classification model. Many classification algorithms have been</p><p>developed to be used in an IDS detection engine. Developers of IDS have been facing challenges</p><p>in how to evaluate and benchmark classifiers. Different perspectives and multiple, conflicting</p><p>importance evaluation criteria represent the challenges in evaluation, benchmarking and selecting</p><p>suitable IDS classifiers. The current evaluation studies depend on evaluating the IDS classifier</p><p>from a single incomplete perspective. In each study, the evaluations have been achieved with</p><p>reference to some security-related evaluation criteria and ignore performance criteria. Furthermore,</p><p>the weighting process that reflects the importance of each criterion depended on a personal</p><p>subjective perspective. The goal of this thesis is to set a new standardisation and benchmarking</p><p>framework based on a set of standardised criteria and set of unified multi-criteria decision-making</p><p>(MCDM) methods that overcome the shortage. This study attempts to establish and standardise</p><p>IDS classifier evaluation criteria and construct a decision matrix (DM) based on crossover of the</p><p>standardised criteria and 12 classifiers. This DM was evaluated using datasets consist of 125,973</p><p>records; each record consists of 41 features. Subsequently, the classifiers are evaluated and ranked</p><p>using unified MCDM techniques. The proposed framework consists of three main parts: the first</p><p>for standardising evaluation criteria, the second for constructing the DM and the third for</p><p>developing weighting and ranking unified MCDM methods and IDS classifiers evaluation and</p><p>benchmarking. The fuzzy Delphi method (FDM) has been used for criteria standardisation.</p><p>Integrated weighting methods using direct rating and the entropy objective method are developed</p><p>to calculate the weights of the criteria. The Vlse Kriterijumska Optimizacija Kompromisno Resenje</p><p>(VIKOR) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) ranking</p><p>methods were integrated into a unified method for ranking the selected classifiers. The Borda voting</p><p>method was used to unify the different ranks and perform a group ranking context. An objective</p><p>validation process has been used to validate the ranking results. The mean standard deviation was</p><p>computed to ensure that the classifier ranking underwent systematic ranking. The following results</p><p>were confirmed. (1) FDM is a suitable way to reach a standard set of evaluation criteria. (2) Using</p><p>an integrated (subjective, objective) weighting method can find the suitable criteria weights. (3) A</p><p>unified ranking method that integrates VIKOR and TOPSIS effectively solves the classifier</p><p>selection problem and (4) the objective validation shows significant differences between the</p><p>groups scores, indicating indicates that the ranking results of the proposed framework were valid.</p><p>(5) The evaluation of the proposed framework shows an advantage over the benchmarked works</p><p>with a percentage of 100%. The implications of this study benefit IDS developers in making the</p><p>right decisions in selecting the best classification model. Researchers can use the proposed</p><p>framework for evaluation and selection in similar evaluation problems.</p>
format thesis
qualification_name
qualification_level Doctorate
author Alamleh, Amneh Hussein Mohd
author_facet Alamleh, Amneh Hussein Mohd
author_sort Alamleh, Amneh Hussein Mohd
title Benchmarking framework for IDS classifiers in term of security and performance based on multicriteria analysis
title_short Benchmarking framework for IDS classifiers in term of security and performance based on multicriteria analysis
title_full Benchmarking framework for IDS classifiers in term of security and performance based on multicriteria analysis
title_fullStr Benchmarking framework for IDS classifiers in term of security and performance based on multicriteria analysis
title_full_unstemmed Benchmarking framework for IDS classifiers in term of security and performance based on multicriteria analysis
title_sort benchmarking framework for ids classifiers in term of security and performance based on multicriteria analysis
granting_institution Universiti Pendidikan Sultan Idris
granting_department Fakulti Seni, Komputeran dan Industri Kreatif
publishDate 2022
url https://ir.upsi.edu.my/detailsg.php?det=9163
_version_ 1776104597115371520
spelling oai:ir.upsi.edu.my:91632023-07-10 Benchmarking framework for IDS classifiers in term of security and performance based on multicriteria analysis 2022 Alamleh, Amneh Hussein Mohd QA Mathematics <p>This research aims to assist the developers of intrusion detection systems (IDS) to make the right</p><p>selection decision of a suitable classification model. Many classification algorithms have been</p><p>developed to be used in an IDS detection engine. Developers of IDS have been facing challenges</p><p>in how to evaluate and benchmark classifiers. Different perspectives and multiple, conflicting</p><p>importance evaluation criteria represent the challenges in evaluation, benchmarking and selecting</p><p>suitable IDS classifiers. The current evaluation studies depend on evaluating the IDS classifier</p><p>from a single incomplete perspective. In each study, the evaluations have been achieved with</p><p>reference to some security-related evaluation criteria and ignore performance criteria. Furthermore,</p><p>the weighting process that reflects the importance of each criterion depended on a personal</p><p>subjective perspective. The goal of this thesis is to set a new standardisation and benchmarking</p><p>framework based on a set of standardised criteria and set of unified multi-criteria decision-making</p><p>(MCDM) methods that overcome the shortage. This study attempts to establish and standardise</p><p>IDS classifier evaluation criteria and construct a decision matrix (DM) based on crossover of the</p><p>standardised criteria and 12 classifiers. This DM was evaluated using datasets consist of 125,973</p><p>records; each record consists of 41 features. Subsequently, the classifiers are evaluated and ranked</p><p>using unified MCDM techniques. The proposed framework consists of three main parts: the first</p><p>for standardising evaluation criteria, the second for constructing the DM and the third for</p><p>developing weighting and ranking unified MCDM methods and IDS classifiers evaluation and</p><p>benchmarking. The fuzzy Delphi method (FDM) has been used for criteria standardisation.</p><p>Integrated weighting methods using direct rating and the entropy objective method are developed</p><p>to calculate the weights of the criteria. The Vlse Kriterijumska Optimizacija Kompromisno Resenje</p><p>(VIKOR) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) ranking</p><p>methods were integrated into a unified method for ranking the selected classifiers. The Borda voting</p><p>method was used to unify the different ranks and perform a group ranking context. An objective</p><p>validation process has been used to validate the ranking results. The mean standard deviation was</p><p>computed to ensure that the classifier ranking underwent systematic ranking. The following results</p><p>were confirmed. (1) FDM is a suitable way to reach a standard set of evaluation criteria. (2) Using</p><p>an integrated (subjective, objective) weighting method can find the suitable criteria weights. (3) A</p><p>unified ranking method that integrates VIKOR and TOPSIS effectively solves the classifier</p><p>selection problem and (4) the objective validation shows significant differences between the</p><p>groups scores, indicating indicates that the ranking results of the proposed framework were valid.</p><p>(5) The evaluation of the proposed framework shows an advantage over the benchmarked works</p><p>with a percentage of 100%. The implications of this study benefit IDS developers in making the</p><p>right decisions in selecting the best classification model. Researchers can use the proposed</p><p>framework for evaluation and selection in similar evaluation problems.</p> 2022 thesis https://ir.upsi.edu.my/detailsg.php?det=9163 https://ir.upsi.edu.my/detailsg.php?det=9163 text eng closedAccess Doctoral Universiti Pendidikan Sultan Idris Fakulti Seni, Komputeran dan Industri Kreatif <p>Abdulkareem, K. H., Arbaiy, N., Zaidan, A., Zaidan, B., Albahri, O., Alsalem, M., & Salih, M. M. (2021). A new standardisation and selection framework for real-time imagedehazing algorithms from multi-foggyscenes based on fuzzy Delphi and hybrid multi-criteria decision analysis methods. Neural Computing and Applications, 33, 1029-1054.</p><p>Adler, M., & Ziglio, E. (1996). Gazing into the oracle: The Delphi method and its application to social policy and public health: Jessica Kingsley Publishers.</p><p>Ahmad, I., Abdullah, A., & Alghamdi, A. (2010). Towards the selection of best neural network system for intrusion detection. International Journal of Physical Sciences, 5(12), 1830-1839.</p><p>Aksu, D., stebay, S., Aydin, M. A., & Atmaca, T. (2018). Intrusion detection with comparative analysis of supervised learning techniques and fisher score feature selection algorithm. Paper presented at the International Symposium on Computer and Information Sciences.</p><p>Albahri, A. S., Albahri, O. S., Zaidan, A. A., Zaidan, B. B., Hashim, M., Alsalem, M. A., . . . Baqer, M. J. (2019). Based Multiple Heterogeneous Wearable Sensors: A Smart Real-Time Health Monitoring Structured for Hospitals Distributor. IEEE Access, 7, 37269-37323. doi: 10.1109/ACCESS.2019.2898214</p><p>Albahri, O. S., Al-Obaidi, J. R., Zaidan, A. A., Albahri, A. S., Zaidan, B. B., Salih, M. M., . . . Zulkifli, C. Z. (2020). Helping doctors hasten COVID-19 treatment: Towards arescueframework forthe transfusion of best convalescent plasma to the most critical patients based on biological requirements via ml and novel MCDM methods. Comput Methods Programs Biomed, 196, 105617. doi: 10.1016/j.cmpb.2020.105617</p><p>Albahri, O. S., Zaidan, A. A., Albahri, A. S., Alsattar, H. A., Mohammed, R., Aickelin, U., . . . Al-Obaidi, J. R. (2021). Novel dynamic fuzzy Decision-Making framework for COVID-19 vaccine dose recipients. Journal of Advanced Research. doi: https://doi.org/10.1016/j.jare.2021.08.009</p><p>Albin, E., & Rowe, N. C. (2012). A realistic experimental comparison of the Suricata and Snort intrusion-detection systems. Paper presented at the Advanced Information Networking and Applications Workshops (WAINA), 2012 26th International Conference on.</p><p>AlOmary, R. Y., & Khan, S. A. (2013). Goal programming based multi-criteria decision-making for distributed denial of service attacks in wireless sensor networks. Paper presented at the Computer, Control, Informatics and Its Applications (IC3INA), 2013 International Conference on.</p><p>AlOmary, R. Y., & Khan, S. A. (2014). Fuzzy logic based multi-criteria decision-making using Dubois and Prade's operator for distributed denial of serviceattacks in wireless sensor networks. Paper presented at the Information and Communication Systems (ICICS), 2014 5th International Conference on.</p><p>Alsaedi, N., Hashim, F., Sali, A., & Rokhani, F. Z. (2017). Detecting sybil attacks in clustered wireless sensor networks based on energy trust system (ETS). Computer Communications, 110, 75-82.</p><p>Alsalem, M., Albahri, O., Zaidan, A., Al-Obaidi, J. R., Alnoor, A., Alamoodi, A., . . . Jumaah, F. (2022). Rescuing emergency cases of COVID-19 patients: An intelligent real-time MSC transfusion framework based on multicriteria decision-making methods. Applied Intelligence, 1-25.</p><p>Alsalem, M., Zaidan, A., Zaidan, B., Albahri, O., Alamoodi, A., Albahri, A., . . . Mohammed, K. (2019). Multiclass benchmarking framework for automated acute Leukaemia detection and classification based on BWM and group-VIKOR. Journal of medical systems, 43(7), 1-32.</p><p>Amudha, P., Karthik, S., & Sivakumari, S. (2013). Classification techniques for intrusion detection-an overview. International Journal of Computer Applications, 76(16).</p><p>Antunes, C. H., & Henriques, C. O. (2016). Multi-objective optimization and multicriteriaanalysis models and methods forproblems in the energy sector Multiple criteria decision analysis (pp. 1067-1165): Springer.</p><p>Anwar, S., Mohamad Zain, J., Zolkipli, M. F., Inayat, Z., Khan, S., Anthony, B., & Chang, V. (2017). From intrusion detection to an intrusion response system: fundamentals, requirements, and future directions. Algorithms, 10(2), 39.</p><p>Arnaboldi, L., & Morisset, C. (2021). A Review of Intrusion Detection Systems and Their Evaluation in the IoT. arXiv preprint arXiv:2105.08096.</p><p>Arshad, J., Azad, M. A., Amad, R., Salah, K., Alazab, M., & Iqbal, R. (2020). A Review of Performance, Energy and Privacy of Intrusion Detection Systems for IoT. Electronics, 9(4), 629.</p><p>Aruldoss, M., Lakshmi, T. M., & Venkatesan, V. P. (2013). A survey on multi criteria decision making methods and its applications. American Journal of Information Systems, 1(1), 31-43.</p><p>Azmi, R., & Pishgoo, B. (2013). SHADuDT: Secure hypervisor-based anomaly detection using danger theory. computers & security, 39, 268-288.</p><p>Bache, K., & Lichman, M. (2013). UCI machine learning repository, 2013. URL http://archive. ics. uci. edu/ml, 5.</p><p>Baig, Z. A., & Khan, S. A. (2010). Fuzzy logic-based decision making for detecting distributed node exhaustion attacks in wireless sensor networks. Paper presented at the Future Networks, 2010. ICFN'10. Second International Conference on.</p><p>Bao, S.-q., Ding, Z.-j., Wu, Y.-y., & Shi, Y.-t. (2016). Machine Learning Algorithm For Efficiency Management Of Oil Well. Paper presented at the 2nd International Conference on Electronics, Network and Computer Engineering (ICENCE 2016).</p><p>Barry, B. I., & Chan, H. A. (2008). A signature database for Intrusion Detection Systems targeting Voice over Internet Protocol environments. Paper presented at the Military Communications Conference, 2008. MILCOM 2008. IEEE.</p><p>Barry, B. I., & Chan, H. A. (2013). Architectureand performanceevaluation of ahybrid intrusion detection system for IP telephony. Security and Communication Networks, 6(12), 1539-1555.</p><p>Basu, S., Biswas, A., Roy, S., & DasBit, S. (2018). Wise-PRoPHET: A Watchdog supervised PRoPHET for reliable dissemination of post disaster situational information over smartphone based DTN. Journal of Network and Computer Applications, 109, 11-23.</p><p>Bernieri, G., Damiani, S., Del Moro, F., Faramondi, L., Pascucci, F., & Tambone, F. (2016). A Multiple-Criteria Decision Making method as support for critical infrastructure protection and Intrusion Detection System. Paper presented at the Industrial Electronics Society, IECON 2016-42nd Annual Conference of the IEEE.</p><p>bin Othman, M. F., & Yau, T. M. S. (2007). Comparison of different classification techniques using WEKA for breast cancer. Paper presented at the 3rd Kuala Lumpur International Conference on Biomedical Engineering 2006.</p><p>Bodjanova, S. (2006). Median alpha-levels of afuzzy number. Fuzzy Sets and Systems, 157(7), 879-891.</p><p>Bottomley, P. A., & Doyle, J. R. (2001). A comparison of three weight elicitation methods: good, better, and best. Omega, 29(6), 553-560.</p><p>Bruneau, G. (2001). The history and evolution of Intrusion Detection. SANS Institute, 1.</p><p>Bueno, S., & Salmeron, J. L. (2008). Fuzzymodeling enterpriseresourceplanning tool selection. Computer Standards & Interfaces, 30(3), 137-147.</p><p>Campos, L. M. d., Cano, A., Castellano, J. G., & Moral, S. (2011, 22-24 Nov. 2011). Bayesian networks classifiers for gene-expression data. Paper presented at the 2011 11th International Conference on Intelligent Systems Design and Applications.</p><p>Carvalho, L. F., Abro, T., de Souza Mendes, L., & Proena Jr, M. L. (2018). An ecosystem for anomaly detection and mitigation in software-defined networking. Expert Systems with Applications, 104, 121-133.</p><p>Cavallini, C., Giorgetti, A., Citti, P., & Nicolaie, F. (2013). Integral aided method for material selection based on quality function deployment and comprehensive VIKOR algorithm. Materials & Design, 47, 27-34. doi: https://doi.org/10.1016/j.matdes.2012.12.009</p><p>Chang, P.-L., Hsu, C.-W., & Chang, P.-C. (2011). Fuzzy Delphimethod for evaluating hydrogen production technologies. International Journal of Hydrogen Energy, 36(21), 14172-14179.</p><p>Chen, Q., Abdelwahed, S., & Erradi, A. (2014). A model-based validated autonomic approach to self-protect computing systems. IEEE Internet of things Journal, 1(5), 446-460.</p><p>Chen, Z., Xu, G., Mahalingam, V., Ge, L., Nguyen, J., Yu, W., & Lu, C. (2016). A cloud computing based network monitoring and threat detection system for critical infrastructures. Big Data Research, 3, 10-23.</p><p>Cheng, C.-H., & Lin, Y. (2002). Evaluating the best main battle tank using fuzzy decision theory with linguistic criteria evaluation. European journal of operational research, 142(1), 174-186.</p><p>Chiu, W.-Y., Tzeng, G.-H., & Li, H.-L. (2013). A new hybrid MCDM model combining DANP with VIKOR to improve e-store business. Knowledge-Based Systems, 37, 48-61. doi: https://doi.org/10.1016/j.knosys.2012.06.017</p><p>Chokkalingam, S. P., & Komathy, K. (2013, 23-24 Aug. 2013). Comparison of different classifier in WEKA for rheumatoid arthritis. Paper presented at the 2013 International Conference on Human Computer Interactions (ICHCI).</p><p>Dash, S. (2013, 4-6 Dec. 2013). Hill-climber based fuzzy-rough feature extraction with an application to cancer classification. Paper presented at the13th International Conference on Hybrid Intelligent Systems (HIS 2013).</p><p>Dawood, K. A., Sharif, K. Y., GHANI, A. A., Zulzalil, H., Zaidan, A., & Zaidan, B. (2020). Towards a Unified Criteria Model for Usability Evaluation in the Context of Open Source Software Based on a Fuzzy Delphi Method. Information and Software Technology, 106453.</p><p>De Keyser, W., & Peeters, P. (1996). A note on the use of PROMETHEE multicriteria methods. European journal of operational research, 89(3), 457-461.</p><p>De la Hoz, E., de la Hoz, E., Ortiz, A., Ortega, J., & Martnez-lvarez, A. (2014). Feature selection by multi-objective optimisation: Application to network anomaly detection by hierarchical self-organising maps. Knowledge-Based Systems, 71, 322-338.</p><p>Deegalla, S., & Bostrm, H. (2009, 13-15 Dec. 2009). Improving Fusion of Dimensionality Reduction Methods for Nearest Neighbor Classification. Paper presented at the 2009 International Conference on Machine Learning and Applications.</p><p>Desai, A., & Rai, S. (2013). Analysis of Machine Learning Algorithms using Weka.</p><p>Dobravec, T. (2019). Implementation and Evaluation of Algorithms with ALGator. Informatica, 43(1).</p><p>Donchev, D., Vassilev, V., & Tonchev, D. (2021). Impact of False Positives and False Negatives on Security Risks in Transactions Under Threat. Paper presented at the International Conference on Trust and Privacy in Digital Business.</p><p>Donkal, G., & Verma, G. K. (2018). A multimodal fusion based framework to reinforce IDS for securing Big Data environment using Spark. Journal of information security and applications, 43, 1-11.</p><p>Doyle, J. R., Green, R. H., & Bottomley, P. A. (1997). Judging relative importance: direct rating and point allocation are not equivalent. Organizational behavior and human decision processes, 70(1), 65-72.</p><p>Dwivedi, S., Kasliwal, P., & Soni, S. (2016, 18-19 March 2016). Comprehensive study of data analytics tools (RapidMiner, Weka, R tool, Knime). Paper presented at the 2016 Symposium on Colossal Data Analysis and Networking (CDAN).</p><p>El-Alfy, E.-S. M., & Al-Obeidat, F. N. (2014). A multicriterion fuzzy classification method with greedy attribute selection for anomaly-based intrusion detection. Procedia computer science, 34, 55-62.</p><p>El-Alfy, E.-S. M., & Al-Obeidat, F. N. (2015). Detecting cyber-attacks on wireless mobile networks using multicriterion fuzzy classifier with genetic attribute selection. Mobile Information Systems, 2015.</p><p>Emerson, P. (2013). The original Borda count and partial voting. Social Choice and Welfare, 40(2), 353-358.</p><p>Fessi, B. A., Benabdallah, S., Boudriga, N., & Hamdi, M. (2014). A multi-attribute decision model for intrusion response system. Information Sciences, 270, 237254.</p><p>Fragkiadakis, A. G., Siris, V. A., & Traganitis, A. P. (2010). Effective and robust detection of jamming attacks. Paper presented at the 2010 Future Network & Mobile Summit.</p><p>Gbanie, S. P., Tengbe, P. B., Momoh, J. S., Medo, J., & Kabba, V. T. S. (2013). Modelling landfill location using geographic information systems (GIS) and multi-criteriadecision analysis (MCDA): casestudyBo, Southern SierraLeone. Applied Geography, 36, 3-12.</p><p>Gul, M., Celik, E., Aydin, N., Taskin Gumus, A.,& Guneri, A. F. (2016). A stateof the art literaturereview of VIKOR and its fuzzy extensions on applications.Applied Soft Computing, 46, 60-89. doi: https://doi.org/10.1016/j.asoc.2016.04.040</p><p>Guo, S., & Zhao, H. (2017). Fuzzy best-worst multi-criteria decision-making method and its applications. Knowledge-Based Systems, 121(Supplement C), 23-31. doi: https://doi.org/10.1016/j.knosys.2017.01.010</p><p>Guo, W., Chen, Y., Cai, Y., Wang, T., & Tian, H.(2017). DIntrusion Detection in WSN with an Improved NSA Based onthe DE-CMOP. KSII Transactions on Internet & Information Systems, 11(11).</p><p>Guo, Y., Wang, B., Zhao, X., Xie, X., Lin, L., & Zhou, Q. (2010). Feature selection based on Rough set and modified genetic algorithm for intrusion detection. Paper presented at the Computer Science and Education (ICCSE), 2010 5th International Conference on.</p><p>Habibi, A., Jahantigh, F. F., & Sarafrazi, A. (2015). Fuzzy Delphi Technique for Forecasting and Screening Items. Asian Journal of Research in Business Economics and Management, 5(2), 130-143.</p><p>Han, M. L., Kwak, B. I., & Kim, H. K. (2018). Anomaly intrusion detection method for vehicular networks based on survival analysis. Vehicular Communications, 14, 52-63.</p><p>Haralambopoulos, D., & Polatidis, H. (2003). Renewable energy projects: structuring a multi-criteria group decision-making framework. Renewable energy, 28(6), 961-973.</p><p>Hassan, M.M., Gumaei, A., Alsanad,A.,Alrubaian, M., & Fortino, G.(2020). A hybrid deep learning model for efficient intrusion detection in big data environment. Information Sciences, 513, 386-396. doi: https://doi.org/10.1016/j.ins.2019.10.069</p><p>Hassanzadeh, A., Altaweel, A., & Stoleru, R. (2014). Traffic-and-resource-aware intrusion detection in wireless mesh networks. Ad hoc networks, 21, 18-41.</p><p>Hassanzadeh, A., & Stoleru, R. (2013). On the optimality of cooperative intrusion detection forresourceconstrained wireless networks. computers & security, 34, 16-35.</p><p>Hassanzadeh, A., Xu, Z., Stoleru, R., Gu, G., & Polychronakis, M. (2016). PRIDE: A practical intrusion detection system for resource constrained wireless mesh networks. computers & security, 62, 114-132.</p><p>Herrera-Semenets, V., Bustio-Martnez, L., Hernndez-Len, R., & van den Berg, J. (2021). A multi-measure feature selection algorithm for efficacious intrusion detection. Knowledge-Based Systems, 227, 107264. doi: https://doi.org/10.1016/j.knosys.2021.107264</p><p>Hindy, H., Bayne, E., Bures, M., Atkinson, R., Tachtatzis, C., & Bellekens, X. (2020). Machine learning based IoT Intrusion Detection System: an MQTT case study (MQTT-IoT-IDS2020 Dataset). Paper presented at the International Networking Conference.</p><p>Hindy, H., Brosset,D., Bayne, E.,Seeam, A.,Tachtatzis, C., Atkinson, R., &Bellekens,X. (2018). A taxonomy and survey of intrusion detection system design techniques, network threats and datasets.</p><p>Hodo, E., Bellekens, X., Hamilton, A., Tachtatzis, C., & Atkinson, R. (2017). Shallow and deep networks intrusion detection system: A taxonomy and survey. arXiv preprint arXiv:1701.02145.</p><p>Hwang, C. L., & Yoon, K. (1981). Multiplecriteriadecision making. Lecture Notes in Economics and Mathematical Systems.</p><p>Iannucci, S., & Abdelwahed, S. (2016). Towards autonomic intrusion response systems. Paper presented at the Autonomic Computing (ICAC), 2016 IEEE International Conference on.</p><p>Inayat, Z., Gani, A., Anuar, N. B., Khan, M. K., &Anwar, S. (2016). Intrusion response systems: Foundations, design, and challenges. Journal of Network and Computer Applications, 62, 53-74.</p><p>Jahan, A., Mustapha, F., Ismail, M. Y., Sapuan, S. M., & Bahraminasab, M. (2011). A comprehensive VIKOR method for material selection. Materials & Design, 32(3), 1215-1221. doi: https://doi.org/10.1016/j.matdes.2010.10.015</p><p>Jahan, A., Mustapha, F., Sapuan, S. M., Ismail, M. Y., & Bahraminasab, M. (2012). A framework for weighting of criteria in ranking stage of material selection process. The International Journal of Advanced Manufacturing Technology, 58(1), 411-420. doi: 10.1007/s00170-011-3366-7</p><p>Jedari, B., Xia, F., Chen, H., Das, S. K., Tolba, A., & Zafer, A.-M. (2019). A social-based watchdog system to detect selfish nodes in opportunistic mobile networks. Future Generation Computer Systems, 92, 777-788.</p><p>Jianjian, D., Yang, T., & Feiyue, Y. (2018). A Novel Intrusion Detection System based on IABRBFSVM for Wireless Sensor Networks. Procedia computer science, 131, 1113-1121.</p><p>Kabir, M. E., & Hu, J. (2014). A statistical framework for intrusion detection system. Paper presentedat theFuzzy Systems andKnowledgeDiscovery (FSKD), 2014 11th International Conference on.</p><p>Kalid, N., Zaidan, A., Zaidan, B., Salman, O. H., Hashim, M., Albahri, O. S.,& Albahri,A. S. (2018). Based on real time remote health monitoring systems: a new approach for prioritization large scales data patients with chronic heart diseases usingbody sensors and communication technology. Journal of medical systems, 42(4), 1-37.</p><p>Kang, K., & Michalak, J. (2018). Enhanced version of AdaBoostM1 with J48 Tree learning method.</p><p>Katkar, A., Shukla, S., Shaikh, D., & Dange, P. (2021, 25-27 June 2021). Malware Intrusion Detection For System Security. Paper presented at the 2021 International Conference on Communication information and Computing Technology (ICCICT).</p><p>Kerrache, C. A., Lakas, A., Lagraa, N., & Barka, E. (2018). UAV-assisted technique for the detection of malicious and selfish nodes in VANETs. Vehicular Communications, 11, 1-11.</p><p>Khan, S. A., & Baig, Z. A. (2010). On the use of unified and-or fuzzy operator for distributed node exhaustion attack decision-making in wireless sensor networks. Paper presented at the Fuzzy Systems (FUZZ), 2010 IEEE International Conference on.</p><p>Khatari, M., Zaidan, A. A., Zaidan, B. B., Albahri, O. S., & Alsalem, M. A. (2019). Multi-Criteria Evaluation and Benchmarking for Active Queue Management Methods: Open Issues, Challenges and Recommended Pathway Solutions. Int.J. Inf. Technol. Decis. Mak., 18(4), 1187-1242. doi: 10.1142/S0219622019300039</p><p>Koch, R., & Golling, M. (2013). Architecture for evaluating and correlating NIDS in real-World networks. Paper presented at the Cyber Conflict (CyCon), 2013 5th International Conference on.</p><p>KP, V. (2019). Jamming detection approach based on fuzzy assisted multicriteria decision-making system for wireless sensor networks. International Journal of Communication Systems, 32(12), e4010.</p><p>Kumar, G. (2014). Evaluation metrics for intrusion detection systems-A study. Evaluation, 2(11), 11-17.</p><p>Kyaw, A. K., Chen, Y., & Joseph, J. (2015). Pi-IDS: evaluation of open-source intrusion detection systems on Raspberry Pi 2. Paper presented at the Information Security and Cyber Forensics (InfoSec), 2015 Second International Conference on.</p><p>Lee, H.-C., & Chang, C.-T. (2018). Comparative analysis of MCDM methods for ranking renewable energy sources in Taiwan. Renewable and sustainable energy reviews, 92, 883-896.</p><p>Li, L., Yu, Y., Bai, S., Hou, Y., & Chen, X. (2017). An Effective Two-Step Intrusion Detection Approach Based on Binary Classification and $ k $-NN. IEEE Access, 6, 12060-12073.</p><p>Liang, J., Ma, M., Sadiq, M., & Yeung, K.-H. (2019). A filter model for intrusion detection system in VehicleAd HocNetworks: A hidden Markov methodology. Knowledge-Based Systems, 163, 611-623.</p><p>Ligmann-Zielinska, A., & Jankowski, P. (2012). Impact of proximity-adjusted preferences on rank-order stability in geographical multicriteria decision analysis. Journal of Geographical Systems, 14(2), 167-187.</p><p>Liou, J. J. H., Tsai, C.-Y., Lin, R.-H., & Tzeng, G.-H. (2011). A modified VIKOR multiple-criteria decision method for improving domestic airlines service quality. Journal of Air Transport Management, 17(2), 57-61. doi: https://doi.org/10.1016/j.jairtraman.2010.03.004</p><p>Lo, H.-W., Shiue, W., Liou, J. J. H., & Tzeng, G.-H. (2020). A hybrid MCDM-based FMEA model foridentification of critical failure modes in manufacturing. Soft Computing, 24(20), 15733-15745. doi: 10.1007/s00500-020-04903-x</p><p>Lu, H., Yang, L., Yan, K., Xue, Y., & Gao, Z. (2017). A cost-sensitive rotation forest algorithm for gene expression data classification. Neurocomputing, 228, 270276. doi: https://doi.org/10.1016/j.neucom.2016.09.077</p><p>Ludwig, S. A., Jakobovic, D., & Picek, S. (2015, 2-5 Aug. 2015). Analyzing gene expression data: Fuzzy decision tree algorithm applied to the classification of cancer data. Paper presented at the 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).</p><p>Lupia, A., & Marano, S. (2016). A dynamic monitoring for energy consumption reduction of a trust-based intrusion detection system in mobile Ad-hoc networks. Paper presented at the Performance Evaluation of Computer and Telecommunication Systems (SPECTS), 2016 International Symposium on.</p><p>Lv, J.-J., Zhou, Y.-S., & Wang, Y.-Z. (2011). A multi-criteria evaluation method of information security controls. Paper presented at the Computational Sciences and Optimization (CSO), 2011 Fourth International Joint Conference on.</p><p>Magn-Carrin, R., Urda, D., Daz-Cano, I., & Dorronsoro, B. (2020). Towards a reliable comparison and evaluation of network intrusion detection systems based on machine learning approaches. Applied Sciences, 10(5), 1775.</p><p>Maggi, F., Matteucci, M., & Zanero, S. (2010). Detecting intrusions through system call sequence and argument analysis. IEEE Transactions on Dependable and Secure Computing, 7(4), 381-395.</p><p>Malik, R., Zaidan, A., Zaidan, B., Ramli, K., Albahri, O., Kareem, Z., . . . Zaidan, R. (2021). Novel roadside unitpositioning framework in the context of the vehicle-to-infrastructurecommunication system based on AHPEntropy forweighting and bordaVIKOR for uniform ranking. International Journal of Information Technology & Decision Making, 1-34.</p><p>Manickavasagam, V., & Padmanabhan, J. (2016). A mobility optimized SPRT based distributed security solution for replica node detection in mobile sensor networks. Ad hoc networks, 37, 140-152.</p><p>Manoliadis, O., Tsolas, I., & Nakou, A. (2006). Sustainable construction and drivers of change in Greece: a Delphi study. Construction Management and Economics, 24(2), 113-120.</p><p>Markovic, V., Stajic, L., Stevic, ., Mitrovic, G., Novarlic, B., & Radojicic, Z. (2020). A novel integrated subjective-objective mcdm model for alternative ranking in order to achieve business excellence and sustainability. Symmetry, 12(1), 164.</p><p>Maseer, Z. K., Yusof, R., Bahaman, N., Mostafa, S. A., & Foozy, C. F. M. (2021). Benchmarking of machine learning for anomaly based intrusion detection systems in the CICIDS2017 dataset. IEEE Access, 9, 22351-22370.</p><p>MeeraGandhi, G. (2010). Machine learning approach for attack prediction and classification using supervised learning algorithms. Int. J. Comput. Sci. Commun, 1(2).</p><p>Mehetrey, P., Shahriari, B., & Moh, M. (2016). Collaborative Ensemble-Learning Based Intrusion Detection Systems for Clouds. Paper presented at the Collaboration Technologies and Systems (CTS), 2016 International Conference on.</p><p>Menahem, E., Shabtai, A., Rokach, L., & Elovici, Y. (2009). Improving malware detection by applying multi-inducer ensemble. Computational Statistics & Data Analysis, 53(4), 1483-1494.</p><p>Mendona, R. V., Teodoro, A. A. M., Rosa, R. L., Saadi, M., Melgarejo, D. C., Nardelli,</p><p>P. H. J., & Rodrguez, D. Z. (2021). Intrusion Detection System Based on Fast Hierarchical Deep Convolutional Neural Network. IEEE Access, 9, 6102461034. doi: 10.1109/ACCESS.2021.3074664</p><p>Midi, D., Rullo, A., Mudgerikar, A., & Bertino, E. (2017). KalisA System for Knowledge-Driven Adaptable Intrusion Detection for the Internet of Things. Paper presented at the Distributed Computing Systems (ICDCS), 2017 IEEE 37th International Conference on.</p><p>Milenkoski, A., Vieira, M., Kounev, S., Avritzer, A., & Payne, B. D. (2015).Evaluating computer intrusion detection systems: A survey of common practices. ACM Computing Surveys (CSUR), 48(1), 1-41.</p><p>Mir, N. M., Khan, S.,Butt, M. A., &Zaman, M. (2016).An experimental evaluation of bayesian classifiers applied to intrusion detection. Indian Journal of Science and Technology, 9(12), 1-7.</p><p>Mohamad, S. N. A., Embi, M. A., & Nordin, N. (2015). Determining e-Portfolio Elements in Learning Process Using Fuzzy Delphi Analysis. International Education Studies, 8(9), 171.</p><p>Mohammed, R., Yaakob, R., Zaidan, A., Sharef, N., Abdullah, R., Zaidan, B., & Dawood, K. (2020). Review of the Research Landscape of Multi-Criteria Evaluation and Benchmarking Processes for Many-Objective Optimization Methods: Coherent Taxonomy, Challenges and Recommended Solution. International Journal of Information Technology & Decision Making (IJITDM), 19(06), 1619-1693.</p><p>Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2010). Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Int J Surg, 8(5), 336-341.</p><p>Moon, S., Lee, J., Sun, X., & Kee, Y.-s. (2015). Optimizing the hadoop mapreduce framework with high-performance storage devices. The Journal of Supercomputing, 71(9), 3525-3548.</p><p>Mullen, P. M. (2003). Delphi: myths and reality. Journal of health organization and management.</p><p>Murray, T. J., Pipino, L. L., & Van Gigch, J. P. (1985). A pilot study of fuzzy set modification of Delphi. Human Systems Management, 5(1), 76-80.</p><p>Nespoli, P., Papamartzivanos, D., Mrmol, F. G., & Kambourakis, G. (2017). Optimal countermeasures selection against cyber attacks: A comprehensive survey on reaction frameworks. IEEE Communications Surveys & Tutorials, 20(2),13611396.</p><p>Novakovic, J. D., Veljovic, A., Ilic, S. S., Papic, ., & Milica, T. (2017). Evaluation of classification models in machine learning. Theory and Applications of Mathematics & Computer Science, 7(1), 3946-3946.</p><p>Ogami, K., Kula, R. G., Hata, H., Ishio, T., & Matsumoto, K. (2017). Using high-rising cities to visualize performance in real-time. Paper presented at the 2017 IEEE Working Conference on Software Visualization (VISSOFT).</p><p>Opricovic, S., & Tzeng, G.-H. (2004). Compromise solution by MCDM methods: A comparativeanalysis of VIKORand TOPSIS. European journal of operational research, 156(2), 445-455.</p><p>Ou Yang, Y.-P., Shieh, H.-M., & Tzeng, G.-H. (2013). A VIKOR technique based on DEMATEL and ANP for information security risk control assessment. Information Sciences, 232, 482-500. doi: https://doi.org/10.1016/j.ins.2011.09.012</p><p>Pamucar, D., Stevic, ., & Sremac, S. (2018). A new model for determining weight coefficients of criteria in mcdm models: Full consistency method (fucom). Symmetry, 10(9), 393.</p><p>Pandey, A. K., Rajpoot, D. S., & Rajpoot, D. S. (2016, 26-28 Dec. 2016). A comparative study of classification techniques by utilizing WEKA. Paper presented at the 2016 International Conference on Signal Processing and Communication (ICSC).</p><p>Panigrahi, R., & Borah, S. (2018). Rank allocation to J48 group of decision tree classifiers using binary and multiclass intrusion detection datasets. Procedia computer science, 132, 323-332.</p><p>Panigrahi, R., Borah, S., Bhoi, A. K., Ijaz, M. F., Pramanik, M., Kumar,Y., & Jhaveri,</p><p>R. H. (2021). Aconsolidated decision tree-based intrusion detection systemfor binary and multiclass imbalanced datasets. Mathematics, 9(7), 751.</p><p>Patil, B. M., Toshniwal, D., & Joshi, R. C. (2009, 6-7 March 2009). Predicting Burn Patient Survivability Using Decision Tree In WEKA Environment. Paper presented at the 2009 IEEE International Advance Computing Conference.</p><p>Patsariya, P., & Singh, R.R. (2019). Classifier Rank Identification usingMulti-Criteria Decision Making Method for Intrusion Detection Dataset.</p><p>Paul, A. B., Biswas, S., Nandi, S., & Chakraborty, S. (2018). MATEM: A unified framework based on trust and MCDM forassuringsecurity, reliability and QoS in DTN routing. Journal of Network and Computer Applications, 104, 1-20.</p><p>Peng, Y., Kou, G., Wang, G., & Shi, Y. (2011). FAMCDM: A fusion approach of MCDM methods to rank multiclass classification algorithms. Omega, 39(6), 677-689.</p><p>Phillips, L. D., & e Costa, C. A. B. (2007). Transparent prioritisation, budgeting and resource allocation with multi-criteria decision analysis and decision conferencing. Annals of Operations Research, 154(1), 51-68.</p><p>Ploskas, N., & Papathanasiou, J. (2019). Adecision support system formultiple criteria alternative ranking using TOPSIS and VIKOR in fuzzy and nonfuzzy environments. Fuzzy Sets and Systems, 377, 1-30.</p><p>Qu, L., & Chen, Y. (2008). A hybrid MCDM method for route selection of multimodal transportation network. Paper presented at the International Symposium on Neural Networks.</p><p>Rahimianzarif, E., & Moradi, M. (2018). Designing integrated management criteriaof creative ideation based on fuzzy delphi analytical hierarchy process. International Journal of Fuzzy Systems, 20(3), 877-900.</p><p>Rahman, S., Ahmed, M., & Kaiser, M. S. (2016). ANFIS based cyber physical attack detection system. Paper presented at the Informatics, Electronics and Vision (ICIEV), 2016 5th International Conference on.</p><p>Rana, A., Kumar, A., & Sharma, A. (2016). Neural Network Radial Basis Function classifier for earthquake data using aFOA. International Journal of Advanced Research, 4(8), 537-540.</p><p>Rashid, T., Ali, A., & Chu, Y.-M. (2021). Hybrid BW-EDASMCDM methodology for optimal industrial robot selection. Plos one, 16(2), e0246738.</p><p>Raza, S., Wallgren, L., & Voigt, T. (2013). SVELTE: Real-time intrusion detection in the Internet of Things. Ad hoc networks, 11(8), 2661-2674.</p><p>Robinson, R. R., & Thomas, C. (2015). Ranking of machine learning algorithms based on the performance in classifying DDoS attacks. Paper presented at the 2015 IEEE Recent Advances in Intelligent Computational Systems (RAICS).</p><p>Russell, I., & Markov, Z. (2017). An introduction to the Weka data mining system. Paper presented at the Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education.</p><p>Safaldin, M., Otair, M., & Abualigah, L. (2021). Improved binary gray wolf optimizer and SVM for intrusion detection system in wireless sensor networks. Journal of ambient intelligence and humanized computing, 12(2), 1559-1576.</p><p>Salabun, W., Watrbski, J., & Shekhovtsov, A. (2020). Are MCDA Methods Benchmarkable? A Comparative Study of TOPSIS, VIKOR, COPRAS, and PROMETHEE II Methods. Symmetry, 12(9), 1549.</p><p>Salem, M., & Buehler, U. (2013). Reinforcing network security by converting massive data flow to continuous connections for IDS. Paper presented at the Internet Technology and Secured Transactions (ICITST), 2013 8th International Conference for.</p><p>Salih, M. M., Zaidan, B., & Zaidan, A. (2020). Fuzzy decision by opinion scoremethod. Applied Soft Computing, 96, 106595.</p><p>Santoso, B. I., Idrus, M. R. S., & Gunawan, I. P. (2016). Designing Network Intrusion and Detection System using signature-based method for protecting OpenStack private cloud. Paper presented at the Engineering Seminar (InAES), International Annual.</p><p>Saracino, A., Sgandurra, D., Dini, G., & Martinelli, F. (2018). Madam: Effective and efficient behavior-based android malware detection and prevention. IEEE Transactions on Dependable and Secure Computing, 15(1), 83-97.</p><p>Saraeian, S., & Shirazi, B. (2020). Process mining-based anomaly detection of additive manufacturing process activities using a game theory modeling approach. Computers & Industrial Engineering, 146, 106584.</p><p>Sedjelmaci, H., Senouci, S. M., & Bouali, T. (2017). Predict and prevent from misbehaving intruders in heterogeneous vehicular networks. Vehicular Communications, 10, 74-83.</p><p>Sen, S., & Clark, J. A. (2011). Evolutionary computation techniques for intrusion detection in mobile ad hoc networks. Computer Networks, 55(15), 3441-3457.</p><p>Shafique, S., & Tehsin, S. (2018). Computer-Aided Diagnosis of Acute Lymphoblastic Leukaemia. Computational and Mathematical Methods in Medicine. doi: 10.1155/2018/6125289</p><p>Shameli-Sendi, A., & Dagenais, M. (2015). ORCEF: Online response cost evaluation framework for intrusion response system. Journal of Network and Computer Applications, 55, 89-107. doi: https://doi.org/10.1016/j.jnca.2015.05.004</p><p>Shameli-Sendi, A., Louafi, H., He, W., & Cheriet, M. (2016). Dynamic optimal countermeasureselection forintrusion response system. IEEE Transactions on Dependable and Secure Computing, 15(5), 755-770.</p><p>Sharma, S., & Kaul, A.(2018).Hybridfuzzy multi-criteriadecision making based multi cluster head dolphin swarm optimized IDS for VANET. Vehicular Communications, 12, 23-38.</p><p>Shih, H.-S., Shyur, H.-J., & Lee, E. S. (2007). An extension of TOPSIS for group decision making. Mathematical and computer modelling, 45(7-8), 801-813.</p><p>Shirbhate, S., Sherekar, S., & Thakare, V. (2014). Performance Evaluation of PCA Filter In Clustered Based Intrusion Detection System. Paper presented at the Electronic Systems, Signal Processing and Computing Technologies (ICESC), 2014 International Conference on.</p><p>Shojaei, P., Haeri, S. A. S., & Mohammadi, S. (2018). Airports evaluation and ranking model using Taguchi loss function, best-worst method and VIKOR technique. Journal of Air Transport Management, 68, 4-13.</p><p>Shrivastava, P., & Shukla, M. (2015, 8-10 Oct. 2015). Comparative analysis of bagging, stacking and random subspace algorithms. Paper presented at the 2015 International Conference on Green Computing and Internet of Things (ICGCIoT).</p><p>Singh, D. K., & Kaushik, P. (2018). Framework for fuzzy rule based automatic intrusion response selection system (frairss) using fuzzy analytic hierarchy process and fuzzy topsis. Journal of Intelligent & Fuzzy Systems, 35(2), 25592571.</p><p>Singh, D. K., & Kaushik, P. (2019). Intrusion response prioritization based on fuzzy ELECTRE multiple criteria decision making technique. Journal of information security and applications, 48, 102359.</p><p>Snousy, M. B. A., El-Deeb, H. M., Badran, K., & Khlil, I. A. A. (2011). Suite of decision tree-based classification algorithms on cancer gene expression data. Egyptian Informatics Journal, 12(2), 73-82. doi: http://dx.doi.org/10.1016/j.eij.2011.04.003</p><p>Soliman, H., Hikal, N. A., & Sakr,N. A. (2012). Acomparativeperformanceevaluation of intrusion detection techniques for hierarchical wireless sensor networks. Egyptian Informatics Journal, 13(3), 225-238.</p><p>Sonawane, H. A., & Pattewar, T. M. (2015). A comparative performance evaluation of intrusion detection based on neural network and PCA. Paper presented at the Communications and Signal Processing (ICCSP), 2015 International Conference on.</p><p>Subba, B., Biswas, S., & Karmakar, S. (2016). Intrusion detection in Mobile Ad-hoc Networks: Bayesian game formulation. Engineering Science and Technology, an International Journal, 19(2), 782-799.</p><p>Subba, B., Biswas, S., & Karmakar, S. (2017). Host based intrusion detection system using frequency analysis of n-gram terms. Paper presented at the Region 10 Conference, TENCON 2017-2017 IEEE.</p><p>Subramanian, S., Srinivasan, V. B., & Ramasa, C. (2012). Study on classification algorithms for network intrusion systems. Journal of Communication and Computer, 9(11), 1242-1246.</p><p>Sultana, M., Haider, A., & Uddin, M. S. (2016, 22-24 Sept. 2016). Analysis of data mining techniques for heart disease prediction. Paper presented at the 2016 3rd International Conference on Electrical Engineering and Information Communication Technology (ICEEICT).</p><p>Sultana, N., Chilamkurti, N., Peng, W., & Alhadad, R. (2019). Survey on SDN based network intrusion detection system using machine learning approaches. Peer-to-Peer Networking and Applications, 12(2), 493-501.</p><p>Tang, C.-W., & Wu, C.-T. (2010). Obtaining a picture of undergraduate education quality: a voice from inside the university. Higher Education, 60(3), 269-286.</p><p>Tarao, M., & Okamoto, T. (2017). Performance evaluation of an immunity-enhancing module for server applications. Procedia computer science, 112, 2165-2174.</p><p>Tavallaee, M. (2011). An adaptive hybrid intrusion detection system. University of New Brunswick, Faculty of Computer Science. </p><p>Tavallaee, M., Bagheri, E., Lu, W., & Ghorbani, A. A. (2009). A detailed analysis of the KDD CUP 99 data set. Paper presented at the 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications.</p><p>Tenrio, J. M., Hummel, A. D., Cohrs, F. M., Sdepanian, V. L.,Pisa, I. T., &deFtima Marin, H. (2011). Artificial intelligencetechniques applied to the development of a decisionsupport system for diagnosing celiac disease. International Journal of Medical Informatics, 80(11), 793-802. doi: https://doi.org/10.1016/j.ijmedinf.2011.08.001</p><p>Tian, J., Zhang, W., Wang, G., & Gao, X. (2014). 2D k-barrier duty-cycle scheduling forintruder detection in wireless sensor networks. Computer Communications, 43, 31-42.</p><p>Tsai, C.-F., Hsu, Y.-F., Lin,C.-Y., & Lin, W.-Y. (2009).Intrusion detection by machine learning: A review. Expert Systems with Applications, 36(10), 11994-12000.</p><p>Verma, A., & Mehta, S. (2017, 12-13 Jan. 2017). A comparative study of ensemble learning methods for classification in bioinformatics. Paper presented at the 2017 7th International Conference on Cloud Computing, Data Science & Engineering -Confluence.</p><p>Vijayanand, R., Devaraj, D., & Kannapiran, B. (2018). Intrusion detection system for wireless mesh network using multiple support vector machine classifiers with genetic-algorithm-based feature selection. computers & security, 77, 304-314.</p><p>Wang, J.-J., Jing, Y.-Y., Zhang, C.-F., & Zhao, J.-H. (2009). Review on multi-criteria decision analysis aid in sustainable energy decision-making. Renewable and sustainable energy reviews, 13(9), 2263-2278.</p><p>Wang, W., Wang, H., Wang, B., Wang, Y., & Wang, J. (2013). Energy-awareand selfadaptiveanomaly detection scheme based onnetwork tomography in mobilead hoc networks. Information Sciences, 220, 580-602.</p><p>Williams, B. C., & Fulp, E. W. (2010). A biologically modeled intrusion detection system for mobile networks. Paper presented at the Broadband, Wireless Computing, Communication and Applications (BWCCA), 2010 International Conference on.</p><p>Xenakis, C., Panos,C., &Stavrakakis,I. (2011). Acomparativeevaluation of intrusion detection architectures for mobile ad hoc networks. computers & security, 30(1), 63-80.</p><p>Yan, Q., Gong, Q., & Deng, F.-a. (2016). Detection of DDoSAttacks AgainstWireless SDN Controllers Based on the Fuzzy Synthetic Evaluation Decision-making Model. Adhoc & Sensor Wireless Networks, 33.</p><p>Yost, J. R. (2015). The March of IDES: Early History of Intrusion-Detection Expert Systems. IEEE Annals of the History of Computing, 38(4), 42-54.</p><p>Young, C., Zambreno, J., Olufowobi, H., & Bloom, G. (2019). Survey of Automotive Controller Area Network Intrusion Detection Systems. IEEE Design & Test.</p><p>Yusoff,N. M., & Yaakob, M. N. (2017). Analisisfuzzy delphiterhadap halangan dalam pelaksanaan mobile learning di Institut Pendidikan Guru. Jurnal Penyelidikan Dedikasi, 11.</p><p>Zaidan, A., Zaidan, B., Albahri, O., Alsalem, M., Albahri, A., Yas, Q. M., & Hashim,M. (2018). A review on smartphone skin cancer diagnosis apps in evaluation and benchmarking: coherent taxonomy, open issues and recommendation pathway solution. Health and Technology, 8(4), 223-238.</p><p>Zaidan, A., Zaidan, B., Alsalem, M., Momani, F., & Zughoul, O. (2020). Novel Multiperspective Hiring Framework forthe Selection of Software Programmer Applicants Based on AHP and Group TOPSIS Techniques. International Journal of Information Technology & Decision Making, 19(03), 775-847.</p><p>Zardari, N. H., Ahmed, K., Shirazi, S. M., & Yusop, Z. B. (2015). Weighting methods and their effects on multi-criteria decision making model outcomes in water resources management: Springer.</p><p>Zbakh, M., Elmahdi, K., Cherkaoui, R., & Enniari, S. (2015). A multi-criteria analysis of intrusion detection architectures in cloud environments. Paper presented at the Cloud Technologies and Applications (CloudTech), 2015 International Conference on.</p><p>Zhang, D., & Yeo, C. K. (2011). Distributed court system for intrusion detection in mobile ad hoc networks. computers & security, 30(8), 555-570.</p><p></p>