Oalib Open Access Library Journal 2333-9705 2333-9721 Scientific Research Publishing 10.4236/oalib.1113566 Oalib-143121 Articles Biomedical Life Sciences, Business Economics, Chemistry Materials Science, Computer Science Communications, Earth Environmental Sciences, Engineering, Medicine Healthcare, Physics Mathematics, Social Sciences Humanities A Comparative Study of Ensemble Learning Techniques and Classification Models to Identify Phishing Websites Alvina T. Budoen Mingwu Zhang Laban Zephaniah Edwards Jr. aSchool of Computer Science, Hubei University of Technology, Wuhan, China 05 06 2025 12 06 1 22 6, February 2025 2, February 2025 2, June 2025 © Copyright 2014 by authors and Scientific Research Publishing Inc. 2014 This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/ The advent of the internet, as we all know, has brought about a significant change in human interaction and business operations around the world; yet, this evolution has also been marked by security issues, including phishing attacks that represent one of the biggest problems to internet users, leading to financial loss and identity theft. The ability of Machine learning and ensemble learning models to process large datasets and complex relationships, and to learn from data have made it easier to detect phishing websites, which have become one of the major problems in modern-day security findings. In this study, a comprehensive analysis of various ensemble techniques is carried out, particularly focusing on algorithms like Random Forest, Gradient Boosting, and AdaBoost, in addition to traditional classification techniques like Logistic Regression, Decision Trees, and Support Vector Machines (SVM). In order to evaluate the effectiveness of these machine learning and ensemble models, the benchmarks dataset having phishing and normal site samples, the study assesses the performance of the mentioned models using distinct evaluation metrics, including accuracy, precision, recall, F1-score, and AUC-ROC. The study focuses its attention on the performance of the Random Forest and Gradient Boosting ensemble models compared to their single classifier counterparts. The findings revealed that ensemble techniques have a better performance in terms of true positive rate, false positive rate, and overall performance. Consequently, the research reinforces that these ensemble learning methods possess the capability of providing strength, flexibility, and efficiency under practical conditions of application. However, there are still some areas for improvement in developing and applying more advanced algorithms.Subject AreasMachine Learning, Cybersecurity, Data Science Ensemble Learning Phishing Detection Classification Models Cybersecurity Website Security 1. 引言近年来，随着多媒体技术和经济多元全球化的不断发展，跨界思维不断冲击着传统的品牌联合，电商、餐饮、服装等行业相继掀起跨界联合风潮，营销学之父菲利普·科特勒曾经说过：除了主流品牌和小众品牌以外，混合品牌也将在未来占据一席之地，其中这个混合品牌与后面的“跨界品牌联合”在某种程度上是一样的，跨界品牌联合是指处于不同行业边界的品牌跨边界进行合作共同推出新产品 [1] 。在跨界联合的实践过程中有的品牌跨界备受关注，溢出效应明显，例如Rio华为手机结合保时捷设计出的超级旗舰MateRS设计好评如潮，大白兔品牌奶糖与加美净这类日化品牌推出的奶糖味的唇膏，上架即一扫而光，然而有的跨界联合却反响平平，甚至出现负面溢出效果，例如喜茶与杜蕾斯的联合产品没有得到消费者的积极评价，甚至使消费者产生反感情绪。之前关于品牌联合效果影响因素的分析上主要体现在联合匹配性、在产品关系和逻辑上的契合度，以及品牌要素互补性方面，但是在研究跨界联合时没有考虑到跨界品牌双方本身就具有较大的差异性，所以在一定程度上对跨界联合的成败现象并不能完全解释。因此，本文聚焦品牌跨界联合情景下，根据形式与功能的整合程度将品牌跨界联合的整合度分为高、低两种，基于SOR模型 [2] ，进而分析品牌跨界联合时的整合度高低对消费者品牌联合评价的影响机制，并且在这个基础上研究消费者感知价值在品牌跨界联合时的整合度对消费者品牌联合评价的中介作用。 2. 文献回顾及研究假设(一) 品牌跨界联合整合度与消费者品牌联合评价相关研究有关品牌跨界联合的概念大多围绕跨界营销进行，在市场营销中有共生营销的概念，它是指一种合作联盟关系，合作双方及多方为实现资源共享，提升竞争能力而形成的一种长期或者短期联盟合作关系，而品牌跨界联合则是指两个异质行业、互相独立但拥有平等商业地位的品牌通过活化老品牌，共享互补资源，降低成本等方式以推出新产品，从而实现销售额的增加 [3] 。两者具有一定的相似性，因此根据前人的论述及本文的研究内容将品牌跨界联合大致定义为品牌联合的特殊形式，是指同一个新的产品中存有两个来自不同且没有竞争关系行业的品牌。由于合作品牌双方的合作程度对消费者的品牌评价会产生相应的影响，Newmeyer等(2018) [4] 根据联合产品在形式和功能上的结合程度提出了六种整合度不同的品牌联合形式，从低到高依次为同地品牌联合、共同促销、捆绑联合、成分联合、要素联合、共同研发联合，本文根据形式和功能的结合程度，将共同研发联合和元素联合划分为高度整合，成分联合和捆绑联合划分为中度整合，共同促销和同地品牌联合划分为低度整合。一个品牌产品所带来的功能或者形式较为单一，不能满足消费者日益多元的功能需求和价值需求，如果将两个异质性产品进行跨界联合，不仅能够带来冲击固有认知的创新感，还有益于提升消费者的忠诚度，但是差异过大的品牌联合可能会产生负面溢出效应 [5] 。所以大多学者聚焦于品牌跨界联合前的联合匹配性和契合度的研究，忽视了品牌跨界联合过程中的整合程度，而消费者对跨界品牌联合的评价在一定层面上反映出跨界联合双方合作的效果，较高的评价不仅有助于维持对品牌资产较好品牌的忠诚度，还有利于提升品牌资产较弱一方的品牌形象(Lin, 2013)，在跨界品牌联合中合作双方的交融程度越深，即产品整合度越高，越有益于消费者体验到品牌双方各自的优势，感受到跨界联合产品所带来的新奇与价值感，从而促进消费者对跨界品牌联合作出积极评价。综上所述，本文提出假设1：H1：在品牌跨界联合时，产品整合度对品牌联合评价有正向影响。(二) 在品牌跨界联合时，感知价值在产品联合整合度对品牌联合评价中的中介作用消费者感知价值这一概念是基于现代营销中的消费者价值理论，该理论认为营销其实是交换价值与感知价值的过程，消费者在得到产品后会对其付出成本与感知价值效用进行比较，从而给出整体评价 [6] 。消费者感知价值时往往有三个特征，对产品价值会根据自身主观性进行感知，并且倾向于在比较中感知价值，其感知的价值还具有阶梯性 [7] 。因此，范秀成等(2003) [8] 将消费者感知价值概述为消费者对企业提供的产品和服务价值的主观评价，当消费者感知到价值时会对品牌联合产生积极评价，而产品整合度越高，即跨界联合产品无论是形式还是功能方面都高度融合，没有令消费者产生突兀感，能够驱动消费者产生价值感知，将异质的产品进行创新融合，起初的不匹配性能够打破了对原有品牌的刻板印象，再通过后面的高度整合能够使消费者产生创新感知的同时降低跨界创新产品的不确定性，感受到品牌联合带来的综合价值，而消费者的感知价值会影响消费者对品牌的选择，进而促进消费者对品牌联合作出正向评价 [9] 。因此，本文假设在品牌跨界联合过程中，合作双方后期在形式和功能上的产品整合度将影响消费者感知价值，又因为高整合度与品牌联合评价具有正向影响，推测消费者感知价值也将正向影响品牌联合评价。综上所述，本文提出假设2：H2：在品牌跨界联合时，消费者感知价值在产品整合度对消费者品牌联合评价的影响中起到中介作用。基于此，本文以SOR模型为研究框架，将品牌框架联合时的产品整合度作为刺激源，消费者从中获得的价值感知作为个体心理呈现，消费者对品牌联合的评价作为消费者者反应，构建在品牌跨界联合中产品整合程度对品牌联合评价的影响模型，模型如 图1 所示。 <xref ref-type="bibr" rid="oalib.143121-"></xref>Figure 1. Research model图1. 研究模型 图1. 研究模型 3. 实证设计与研究发现1) 研究设计与数据收集这部分由前测和正式实验组成，前测主要是通过了解市场上影响较广的跨界联合产品，并由此确定实验对象，正式实验根据产品组合利用单因素两水平，即产品整合程度高低的组间设计，检验品牌跨界联合时产品整合程度对品牌联合评价的主要影响以及消费者价值感知对其中的中介效应。① 前测。通过网络咨询了解与品牌跨界词条有关的内容，其中包含了有关跨界产品组合功能与形式的324条记录。选择了6组市面上真实的跨界组合案例，分别为蔻驰皮革的贝克家具、加入当尼柔顺剂的汰渍洗衣液、安装劳斯莱斯发动机的空客机、戴尔电脑与佳能打印机、配有迪士尼玩具的儿童餐、出售赛百味三明治的沃尔玛。通过变动产品整合度的操纵方法，让受访者填写整合度的7级量表 [4] ，对这些组合的产品整合度进行打分。此次前测收回有效问卷78份，并通过统计数据分析得出，蔻驰皮革的贝克家具这组共同研发组合的整合度评分均值为M 整合度 = 5.26，出售赛百味三明治的沃尔玛这组同地品牌联合销售的整合度评分均值为M 整合度 = 4.14，两者之间的差异很明显，将蔻驰皮革的贝克家具作为高整合度组合，而将出售赛百味三明治的沃尔玛作为低整合度组合，并将两组组合作为实验的刺激源。② 正式实验。该实验采用单因素两水平的设计，包括了102名消费者，其中人员年龄集中于26~35岁，男性占比42.15%，女性占比57.85%，教育程度占比主要是本科学历，将被试进行随机等量分配，其中高低整合度组各自均为61人。让产品高整合度组的被试浏览蔻驰皮革的贝克家具在功能和形式上的整合材料信息，同时让产品低整合度组的被试浏览沃尔玛出售赛百味三明治的销售整合信息，两组被试在看到品牌跨界联合刺激材料后如实填写产品感知价值量表以及品牌联合评价量表，具体量表题项如 表1 所示。 <xref ref-type="bibr" rid="oalib.143121-"></xref>Table 1. Measurement scaleTable 1. Measurement scale 表1. 测量量表

测量维度

测量题项参考来源

消费者感知价值

讨论/拥有/分享这项联名让我很开心

李慧，周雨(2021)

讨论/拥有/分享这项联名让我给他人留下了好印象

这项联名让我获得了自我满足

Sweeney and Soutar (2001)

该联名是我喜欢的

讨论/拥有/分享这项联名让我结识了很多朋友

品牌联合评价

我认为该产品很吸引人

Shih等(2013)

我非常喜欢该联合产品

该联合产品表达了我的个性
2) 数据分析① 问卷的信效度检验。利用SPSS 24.0对实验中的数据进行信度与效度的检验，其中产品感知价值量表以及品牌联合评价量表的Cronbach’ α系数分别为0.891、0.912，两者均在0.8以上，说明问卷信度较好。同时，通过验证性因子分析得出组合的CR和AVE值，从中发现组合效度AR值大于0.7，说明所选题项的一致性好，AVE值也达到了推荐的标准，所以该变量具有较好的效度。②主效应检验。在品牌跨界联合中，通过独立样本T检验验证产品整合度对消费者品牌联合评价有显著影响，即主效应检验。如 表2 所示，产品整合度不同，消费者对品牌跨界联合评价也显著不同，在产品整合度较高的跨界联合中，消费者对品牌联合评价(M = 2.811)显著高于产品整合度低的跨界联合产品(M = 1.649)，因此本文的假设1得到验证，即在品牌跨界联合时，产品整合度对品牌联合评价有正向影响。 <xref ref-type="bibr" rid="oalib.143121-"></xref>Table 2. Comparison of consumers’ evaluation of co-brand under different product integration degreesTable 2. Comparison of consumers’ evaluation of co-brand under different product integration degrees 表2. 不同产品整合度下消费者对品牌联合评价的差异比较

整合度分组

个案数

平均值

标准差

T值

品牌联合评价

高整合度

61

2.811

0.678

−5.650***

低整合度

61

1.649

0.718
③ 中介效应检验。本文以跨界产品整合度为自变量、消费者感知价值为中介变量、品牌联合评价为因变量，运用PROCESS程序中的model 4，通过Bootstrap方法对感知价值的中介效应进行检验，其中控制性别、年龄等人口特征变量。结果显示，产品整合程度对品牌联合评价的总效应为0.942，95%置信区间为(0.642, 1.193)，直接效应为0.401，95%置信区间为(0.076, 0.723)，均不包括0，产品整合程度对消费者感知价值具有显著的正向影响[B = 1.231, 95%置信区间为(0.844, 1.713), SE = 0.231, t = 5.895, p < 0.01]，消费者感知价值对品牌联合评价具有显著正向影响[B = 0.449, 95%置信区间为(0.289, 0.589), SE = 0.071, t = 6.334, p < 0.01]，从产品整合度到感知价值，再到品牌联合评价，其中的间接效应为0.673，95%的置信区间(0.302, 0.937)，不含0，由此得以验证感知价值在产品整合程度与品牌联合评价关系里起到中介作用。 4. 结论与启示本文通过实证分析研究了处于品牌跨界联合中，产品整合度对品牌联合评价的影响关系，主要得到两个结论：首先，在品牌跨界联合时，相比于产品整合度较低的跨界组合，产品整合度高的跨界组合更能够带来积极的品牌联合评价，即产品整合度对品牌联合评价有正向影响；其次，消费者感知价值在跨界产品整合度对品牌联合评价的影响中起到中介作用。通过实证研究，发现产品整合度越高，越有利于消费者感知价值，同时当消费者感知价值以后会对品牌联合作出积极评价，即消费者感知价值在跨界产品整合度和品牌联合评价中起到中介作用。而SOR理论正是关于刺激对个体心理产生影响，从而产生反应的研究模型，因此本文基于SOR理论，在品牌跨界联合时，产品整合度高这一刺激源使得消费者根据偏好流畅性的心理会降低对跨界创新产品的不确定性，从而增强消费者对该类商品的接受度，并从中感知到联合的综合价值。当消费者感受到情绪价值或者社会价值时会产生积极反应，即较高的品牌联合评价，这不仅维持了消费者对原有品牌产品的忠诚度，又在一定程度上提升了品牌资产，为企业有针对性地进行品牌跨界联合提供了感知价值角度的理论支持。本文的研究为企业进行跨界创新实践活动提供了相应指导。首先，尽管市场上出现的跨界创新现象层出不穷，与不同的品牌进行跨界创新能够在短期内快速吸引消费者，并从长期唤起品牌活力，提升品牌创新能力，但是在这个过程中企业应该结合自身的发展情况和战略模式进行调整，不要跟风进行盲目跨界。合适的跨界能够带来正面效应，但是如果缺少跨界的前提情景和适宜时机，跨界将给企业的发展带来无论是品牌形象还是消费者受众都产生消极影响。其次，在品牌跨界联合时，除了考虑前期匹配性，还需要进行整合度的规划。许多企业在进行品牌联合时往往考虑合作双方固有的形象或者市场是否匹配，匹配性较大可以提升消费者的可接受度，但是也可能固化消费者认知。对于需要进行品牌跨界转型的企业来说，同质品牌联合对于公司的发展战略来说意义不大，如果品牌联合异质性较大，品牌联合发行起初可能会吸引眼球，赚取热度，收获许多受众；对于品牌资产不对等的双方来说，影响也不尽相同，但后期消费者感知的功能和形式方面的异质性过于突出，可能会引起消费者的排斥心理，进而降低消费者评价。企业可以通过提升联合产品在功能和形式方面的交融整合度，产品整合度高时消费者更加容易理解品牌联合的意义和价值，也能够避免品牌联合带来的不适感，降低消费者对冲击固有认知的排斥，进而更加理解品牌融合的真正目的以及给自身带来的价值，有利于提升消费者对品牌联合的认同感，对品牌联合做出积极评价，将不匹配产品转换为一次具有创新意义的匹配产品。最后，如果将跨界产品匹配性作为消费者是否接受的前提条件，那么整合度的提升将有益于消费者从中感受到该跨界联合所带来的综合价值。好的品牌联合除了考虑前期双方的联合匹配度，也要形成良好的产品整合度，如果前期的匹配度不能达到预期，那么可以通过后期从产品和功能方面对匹配联合整合度进行调整，从而激发消费者的购买意愿以及对该品牌联合的积极评价。所以企业在实践中要注意消费者感知价值在产品整合度对品牌联合评价中的中介作用，通过良好的整合促使消费者感知产品价值，进而提升品牌联合评价。 References Jari, M. (2022) A Comprehensive Survey of Phishing Attacks and Defences: Human Factors, Training and the Role of Emotions. International Journal of Network Security&Its Applications, 14, 11-24. >https://doi.org/10.5121/ijnsa.2022.14502 Stojnic, T., Vatsalan, D. and Arachchilage, N.A.G. (2021) Phishing Email Strategies: Understanding Cybercriminals’ Strategies of Crafting Phishing Emails. Security and Privacy, 4, e165. >https://doi.org/10.1002/spy2.165 Alkhalil, Z., Hewage, C., Nawaf, L. and Khan, I. (2021) Phishing Attacks: A Recent Comprehensive Study and a New Anatomy. Frontiers in Computer Science, 3, Article 563060. >https://doi.org/10.3389/fcomp.2021.563060 Putra, F.P.E., Ubaidi, U., Zulfikri, A., Arifin, G. and Ilhamsyah, R.M. (2024) Analysis of Phishing Attack Trends, Impacts and Prevention Methods: Literature Study. Brilliance: Research of Artificial Intelligence, 4, 413-421. >https://doi.org/10.47709/brilliance.v4i1.4357 Sharma, K., Rai, P. and Chandel, J. (2023) Review Paper Real-Time Phishing Website with Machine Learning. 2023 11th International Conference on Intelligent Systems and Embedded Design (ISED), Dehradun, 15-17 December 2023, 1-5. >https://doi.org/10.1109/ised59382.2023.10444574 Tang, L. and Mahmoud, Q.H. (2021) A Survey of Machine Learning-Based Solutions for Phishing Website Detection. Machine Learning and Knowledge Extraction, 3, 672-694. >https://doi.org/10.3390/make3030034 Garapati, D.P., Maddipati, L.V.A.P., Swaroop, K.P., Samyuktha, B., Sowmya, G.H. and Valli, B.H.N. (2024) A Comparative Analysis of Logistic Regression, Support Vector Machines, and Random Forest for Phishing Website Identification. 2024 International Conference on Computational Intelligence for Green and Sustainable Technologies (ICCIGST), Vijayawada, 18-19 July 2024, 1-5. >https://doi.org/10.1109/iccigst60741.2024.10717628 Alharbi, A.A. (2024) Classification Performance Analysis of Decision Tree-Based Algorithms with Noisy Class Variable. Discrete Dynamics in Nature and Society, 2024, Article ID: 6671395. >https://doi.org/10.1155/2024/6671395 Jain, A.K. and Gupta, B.B. (2021) A Survey of Phishing Attack Techniques, Defence Mechanisms and Open Research Challenges. Enterprise Information Systems, 16, 527-565. >https://doi.org/10.1080/17517575.2021.1896786 Asadi, M., Jamali, M.A.J., Heidari, A. and Navimipour, N.J. (2024) Botnets Unveiled: A Comprehensive Survey on Evolving Threats and Defense Strategies. Transactions on Emerging Telecommunications Technologies, 35, e5056. >https://doi.org/10.1002/ett.5056 Mallick, M.A.I. and Nath, R. (2024) Navigating the Cyber Security Landscape: A Comprehensive Review of Cyber-Attacks, Emerging Trends, and Recent Developments. World Scientific News, 190, 1-69. Abroshan, H., Devos, J., Poels, G. and Laermans, E. (2021) Phishing Happens Beyond Technology: The Effects of Human Behaviors and Demographics on Each Step of a Phishing Process. IEEE Access, 9, 44928-44949. >https://doi.org/10.1109/access.2021.3066383 Ogutu, R.V.A., Rimiru, R.M. and Otieno, C. (2022) Target Sentiment Analysis Ensemble for Product Review Classification. Journal of Information Technology Research, 15, 1-13. >https://doi.org/10.4018/jitr.299382 Salman, H.A., Kalakech, A. and Steiti, A. (2024) Random Forest Algorithm Overview. Babylonian Journal of Machine Learning, 2024, 69-79. >https://doi.org/10.58496/bjml/2024/007 Mendonça, F., Mostafa, S.S., Morgado-Dias, F., Ravelo-García, A.G. and Figueiredo, M.A.T. (2022) ProBoost: A Boosting Method for Probabilistic Classifiers. arXiv: 2209.01611. Ganaie, M.A., Hu, M., Malik, A.K., Tanveer, M. and Suganthan, P.N. (2022) Ensemble deep learning: A review. Engineering Applications of Artificial Intelligence, 115, Article ID: 105151. >https://doi.org/10.1016/j.engappai.2022.105151 Naidu, G., Zuva, T. and Sibanda, E.M. (2023) A Review of Evaluation Metrics in Machine Learning Algorithms. In: Silhavy, R. and Silhavy, P., Eds., Artificial Intelligence Application in Networks and Systems, Springer, 15-25. >https://doi.org/10.1007/978-3-031-35314-7_2 Alahmed, Y., Abadla, R. and Ansari, M.J.A. (2024) Exploring the Potential Implications of AI-Generated Content in Social Engineering Attacks. 2024 International Conference on Multimedia Computing, Networking and Applications (MCNA), Valencia, 17-20 September 2024, 64-73. >https://doi.org/10.1109/mcna63144.2024.10703950 Sahingoz, O.K., Buber, E., Demir, O. and Diri, B. (2019) Machine Learning Based Phishing Detection from URLs. Expert Systems with Applications, 117, 345-357. >https://doi.org/10.1016/j.eswa.2018.09.029 Mohd Ariffin, N.H., Mohamed Iqbal, M.I., Yusoff, M. and Mohd Zulkefli, N.A. (2025) A Study on the Best Classification Method for an Intelligent Phishing Website Detection System. ASEAN Artificial Intelligence Journal, 1, 20-33. >https://doi.org/10.37934/aaij.1.1.2033 Kavya, S. and Sumathi, D. (2024) Staying Ahead of Phishers: A Review of Recent Advances and Emerging Methodologies in Phishing Detection. Artificial Intelligence Review, 58, Article No. 50. >https://doi.org/10.1007/s10462-024-11055-z Villanueva, A., Atibagos, C., De Guzman, J., Dela Cruz, J.C., Rosales, M. and Francisco, R. (2022) Application of Natural Language Processing for Phishing Detection Using Machine and Deep Learning Models. 2022 International Conference on ICT for Smart Society (ICISS), Bandung, 10-11 August 2022, 1-6. >https://doi.org/10.1109/iciss55894.2022.9915037 Ozcan, A., Catal, C., Donmez, E. and Senturk, B. (2023) A Hybrid DNN-LSTM Model for Detecting Phishing URLs. Neural Computing and Applications, 35, 4957-4973. Shah, M., Gandhi, K., Patel, K.A., Kantawala, H., Patel, R. and Kothari, A. (2023) Theoretical Evaluation of Ensemble Machine Learning Techniques. 2023 5th International Conference on Smart Systems and Inventive Technology (ICSSIT), Tirunelveli, 23-25 January 2023, 829-837. >https://doi.org/10.1109/icssit55814.2023.10061139 Jawad, S.K. and Alnajjar, S.H. (2024) Optimizing Phishing Threat Detection: A Comprehensive Study of Advanced Bagging Techniques and Optimization Algorithms in Machine Learning. Al-Iraqia Journal for Scientific Engineering Research, 3, 64-74. Varriale, L. (2024) Predictive Model for Humanitarian Aid-Research on a Conflict Early Warning System for the Sahel Region. Politecnico di Torino. Beja-Battais, P. (2023) Overview of AdaBoost: Reconciling Its Views to Better Understand Its Dynamics. arXiv: 2310.18323. Mehta, A.A., Padaria, A.A., Bavisi, D.J., Ukani, V., Thakkar, P., Geddam, R., et al. (2024) Securing the Future: A Comprehensive Review of Security Challenges and Solutions in Advanced Driver Assistance Systems. IEEE Access, 12, 643-678. >https://doi.org/10.1109/access.2023.3347200 Baliyan, H. and Prasath, A.R. (2024) Enhancing Phishing Website Detection Using Ensemble Machine Learning Models. 2024 OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.0, Raigarh, 5-7 June 2024, 1-8. >https://doi.org/10.1109/otcon60325.2024.10687754 Adane, K., Beyene, B. and Abebe, M. (2023) Single and Hybrid-Ensemble Learning-Based Phishing Website Detection: Examining Impacts of Varied Nature Datasets and Informative Feature Selection Technique. Digital Threats: Research and Practice, 4, 1-27. >https://doi.org/10.1145/3611392 Li, J. (2024) Area under the ROC Curve Has the Most Consistent Evaluation for Binary Classification. PLOS ONE, 19, e0316019. >https://doi.org/10.1371/journal.pone.0316019 Ovi, M.S.I., Rahman, M.H. and Hossain, M.A. (2024) PhishGuard: A Multi-Layered Ensemble Model for Optimal Phishing Website Detection. arXiv: 2409.19825. Bentéjac, C., Csörgő, A. and Martínez-Muñoz, G. (2020) A Comparative Analysis of Gradient Boosting Algorithms. Artificial Intelligence Review, 54, 1937-1967. >https://doi.org/10.1007/s10462-020-09896-5 Talekar, B. (2020) A Detailed Review on Decision Tree and Random Forest. Bioscience Biotechnology Research Communications, 13, 245-248. >https://doi.org/10.21786/bbrc/13.14/57 Utubor, S. (2023) Improving Detection of Attacks in Cyber-Physical Systems: Applying Gradient Boosting Based Machine Learning Techniques. Ph.D. Thesis, The George Washington University. Ashar Ahmed Fazal, and Maryam Daud, (2023) Detecting Phishing Websites Using Decision Trees: A Machine Learning Approach. International Journal for Electronic Crime Investigation, 7, 232-250. >https://doi.org/10.54692/ijeci.2023.0702155 Kara, I., Ok, M. and Ozaday, A. (2022) Characteristics of Understanding URLs and Domain Names Features: The Detection of Phishing Websites with Machine Learning Methods. IEEE Access, 10, 124420-124428. >https://doi.org/10.1109/access.2022.3223111 Gopal, R.D., Hojati, A. and Patterson, R.A. (2022) Analysis of Third-Party Request Structures to Detect Fraudulent Websites. Decision Support Systems, 154, Article ID: 113698. >https://doi.org/10.1016/j.dss.2021.113698 Pandey, N., Patnaik, P.K. and Gupta, S. (2020) Data Pre Processing for Machine Learning Models Using Python Libraries. International Journal of Engineering and Advanced Technology, 9, 1995-1999. >https://doi.org/10.35940/ijeat.d9057.049420 Tiu, E.S.K., Huang, Y.F., Ng, J.L., AlDahoul, N., Ahmed, A.N. and Elshafie, A. (2021) An Evaluation of Various Data Pre-Processing Techniques with Machine Learning Models for Water Level Prediction. Natural Hazards, 110, 121-153. >https://doi.org/10.1007/s11069-021-04939-8 Zhu, W., Qiu, R. and Fu, Y. (2024) Comparative Study on the Performance of Categorical Variable Encoders in Classification and Regression Tasks. arXiv: 2401.09682. Mohammed, M.A. (2024) Effect of Using Numerical Data Scaling on Supervised Machine Learning Performance. Fazil, A.W., Hakimi, M., Akbari, R., Quchi, M.M. and Khaliqyar, K.Q. (2023) Comparative Analysis of Machine Learning Models for Data Classification: An In-Depth Exploration. Journal of Computer Science and Technology Studies, 5, 160-168. >https://doi.org/10.32996/jcsts.2023.5.4.16 Levy, J.J. and O’Malley, A.J. (2020) Don’t Dismiss Logistic Regression: The Case for Sensible Extraction of Interactions in the Era of Machine Learning. BMC Medical Research Methodology, 20, Article No. 171. >https://doi.org/10.1186/s12874-020-01046-3 Priyanka, N.A. and Kumar, D. (2020) Decision Tree Classifier: A Detailed Survey. International Journal of Information and Decision Sciences, 12, 246-269. >https://doi.org/10.1504/ijids.2020.108141 Khan, S.N., Khan, S.U., Aznaoui, H., Şahin, C.B. and Dinler, Ö.B. (2023) Generalization of Linear and Non-Linear Support Vector Machine in Multiple Fields: A Review. Computer Science and Information Technologies, 4, 226-239. >https://doi.org/10.11591/csit.v4i3.p226-239 Cahyana, N.H., Fauziah, Y. and Aribowo, A.S. (2021) The Comparison of Tree-Based Ensemble Machine Learning for Classifying Public Datasets. RSF Conference Series: Engineering and Technology, 1, 407-413. >https://doi.org/10.31098/cset.v1i1.412 Pagano, T.P., Loureiro, R.B., Lisboa, F.V.N., Peixoto, R.M., Guimarães, G.A.S., Cruz, G.O.R., et al. (2023) Bias and Unfairness in Machine Learning Models: A Systematic Review on Datasets, Tools, Fairness Metrics, and Identification and Mitigation Methods. Big Data and Cognitive Computing, 7, Article 15. >https://doi.org/10.3390/bdcc7010015 Lalor, J.P., Abbasi, A., Oketch, K., Yang, Y. and Forsgren, N. (2024) Should Fairness Be a Metric or a Model? A Model-Based Framework for Assessing Bias in Machine Learning Pipelines. ACM Transactions on Information Systems, 42, 1-41. >https://doi.org/10.1145/3641276 Haghish, E.F. and Czajkowski, N. (2023) Reconsidering False Positives in Machine Learning Binary Classification Models of Suicidal Behavior. Current Psychology, 43, 10117-10121. >https://doi.org/10.1007/s12144-023-05174-z Kopparaju, S.T., Chavarriaga, C., Galarreta, E. and Bhatia, S. (2024) Natural Language Processing-Enhanced Machine Learning Framework for Comprehensive Phishing Email Identification. 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), Kamand, 24-28 June 2024, 1-6. >https://doi.org/10.1109/icccnt61001.2024.10723950 Riyanto, S., Sitanggang, I.S., Djatna, T. and Atikah, T.D. (2023) Comparative Analysis Using Various Performance Metrics in Imbalanced Data for Multi-Class Text Classification. International Journal of Advanced Computer Science and Applications, 14, 1082-1090. >https://doi.org/10.14569/ijacsa.2023.01406116 Jin, J., Shen, Y., Fu, Z. and Yang, J. (2024) Few-Shot Open-Set Recognition via Pairwise Discriminant Aggregation. Neurocomputing, 602, Article ID: 128214. >https://doi.org/10.1016/j.neucom.2024.128214 Li, J. (2023) An Exploration of Relationships between Prevalence, TPR, TNR and Model Performance Metrics. SSRN Electronic Journal, 152, 1549-1556. >https://doi.org/10.2139/ssrn.4530905 Hossain, M.R. and Timmer, D. (2021) Machine Learning Model Optimization with Hyper Parameter Tuning Approach. Global Journal of Computer Science&Technology, 21, 31. >https://gjcst.com/index.php/gjcst/article/view/2059 Yu, T. and Zhu, H. (2020) Hyper-Parameter Optimization: A Review of Algorithms and Applications. arXiv: 2003.05689. Ding, X., Liu, J., Yang, F. and Cao, J. (2021) Random Radial Basis Function Kernel-Based Support Vector Machine. Journal of the Franklin Institute, 358, 10121-10140. >https://doi.org/10.1016/j.jfranklin.2021.10.005