## 2 Related Works  
Numerous works related to cybersecurity-based solutions are available in the literature [9; 10]. The authors in [11] concentrate on implementing a DL structure for detectingphishing WSs. This work initially designs two kinds of features for web phishing, such as original and interaction features. The detection method dependent upon DBN is then projected. In [12], it can be projected a manner for detecting malicious URL addresses with accuracy, utilizing CNNs. In contrast to the preceding mechanism, whereas URL or traffic statistics or web contents are analyzed, it can be analyzed only the URL text. Therefore, this technique is faster and detects zero-day attacks. [PERSON] et al. [13] establish the model of phishing and DL from the context of cybersecurity. Afterward, classifications of phishing detection and DL techniques are offered for classifying the recent works into several types. Then, taking the presented classification as baseline, this research widely analyzes the recent DL approaches and examines their benefits and drawbacks. The authors in [14] examine a novel technique for identifying phishing WSs utilizing hyperlinks accessible from the source code of HTML webpage from the equivalent WS. This feature is utilized for training the supervised DNN approach with Adam optimizing to differentiate fraudulent WSs from genuine WSs. The presented DL approach with Adam optimizer utilizes a listwise method for classifying phishing as well as genuine WSs. [PERSON] et al. [15] propose the recent methods for phishing WS recognition utilizing the ML approaches. The popularly studied methods are concentrated on classical ML approaches. Ada Boosting, SVM, RF, and NB are the powerful ML approaches studied in the works. This review work also recognizes DL-based approaches with optimum efficiency to detect phishing WSs related to the conventional ML approaches. [PERSON] and [PERSON] [16] examine a cognitive spammer structure that eliminates spam pages if the search engines compute the webpage rank score. The structure identifies web spam with the assistance of the LSTM network by training the link features. In [17], a real-time anti-phishing model that utilizes seven distinct classifier approaches and NLP based features is presented. The model has the subsequent differentiating property in other studies in analyses: language independence, utilization of massive size of legitimate and phishing datasets, real-time implementation, recognition of novel WSs, independence in third-party service, and utilization of feature-rich classifications. [PERSON] et al. [18] propose an effective phishing page detection model by the use of multiple models, where every model is trained by the insertion of (controlled) noises in a subset of arbitrarily elected features from entire set of features. [PERSON] et al. [19] introduce a 2-stage ensemble learning approach with the integration of random forest (RF) based pre-classification and multilayer perceptron (MLP) based decision making. The trained MLP classification model substitutes the majority voting method of the three trained RF models to make decisions. [PERSON] et al. [20] present the initial examination of the man-in-the-middle (MITM) phishing toolkit. With the detailed investigation of the toolkit, the implicit network level characteristics are identified, which can be employed for the detection process. In addition, an ML-based classification model is derived to find the existence of toolkits for online communication purposes. [PERSON] et al. [21] introduce an anti-phishing model named PhisherCop, which is based on the stochastic gradient descent (SGD) and a support vector classifier (SVC) model. The authors in [22] introduce the Crawlphish model to automatically detect and categorize client-side cloaking utilized by recognized phishing websites. The authors also present a taxonomy of eight distinct kinds of evasion over three high-level classes. ML-based phishing website detection utilizes ML models for the detection of manually extracted phishing website URL features.