Hybrid Classification Research Articles

Convolutional-free Malware Image Classification using Self-attention Mechanisms

Malware analysis is a critical aspect of cybersecurity, aiming to identify and differentiate malicious software from benign programmes to protect computer systems from security threats. Despite advancements in cybersecurity measures, malware continues to pose significant risks in cyberspace, necessitating accurate and rapid analysis methods. This paper introduces an innovative approach to malware classification using image analysis, involving three key phases: converting operation codes into RGB image data, employing a Generative Adversarial Network (GAN) for synthetic oversampling, and utilising a simplified Vision Transformer (ViT)-based classifier for image analysis. The method enhances feature richness and explainability through visual imagery data and addresses imbalanced classification using GAN-based oversampling techniques. The proposed framework combines the strengths of convolutional autoencoders, hybrid classifiers, and adapted ViT models to achieve a balance between accuracy and computational efficiency. As shown in the experiments, our convolutional-free approach possesses excellent accuracy and precision compared with convolutional models and outperforms CNN models on two datasets, thanks to the multi-head attention mechanism. On the Big2015 dataset, our model outperforms other CNN models with an accuracy of 0.8369 and an AUC of 0.9791. Specifically, our model reaches an accuracy of 0.9697 and an F1 score of 0.9702 on MALIMG, which is extraordinary.

METAHEURISTIC-ASSISTED HYBRID RECOGNITION MODEL FOR BRAIN ACTIVITY DETECTION

Electroencephalogram (EEG) recordings typically capture the integrals of active brain potentials, which vary in latencies and populations. Anomalies in EEG data, often associated with epilepsy, play a crucial role in identifying conditions such as brain death, encephalopathy, coma, depth of anesthesia, and sleep disturbances. To get early warnings for these diseases, this work intends to propose a novel approach for brain activity detection from EEG signals. A new Hybrid Classification of Combined Coot Blue Monkey Optimization (HC-CCBO) method is proposed in this work. Initially, improved [Formula: see text]-score normalization was used to preprocess the EEG signal. Further, Discrete Wavelet Transform (DWT), improved correlation and statistical features were extracted. After that, we set up hybrid classification, which exploited Bi-Directional Gated Recurrent Unit (Bi-GRU) and Deep Max Out (DMO) models. Further, weights of BI-GRU and DMO were optimized via Combined Coot Blue Monkey Optimization (CCBO) optimization. Finally, we obtain the output scores of the rest, left fist, both fists, right fist, and both feet from the suggested hybrid brain activity recognition model. The effectiveness of the suggested HC-CCBO model is compared to conventional techniques using a variety of metrics. Compared to the existing models, the suggested model obtains a high maximum accuracy of 0.93 at the 90th learning percentage.

Hybrid Classifier for Detecting Zero-Day Attacks on IoT Networks

Recently, Internet of Things (IoT) networks have been exposed to many electronic attacks, giving rise to concerns about the security of these networks, where their weaknesses and gaps can be exploited to access or steal data. These networks are threatened by several cyberattacks, one of which is the zero-day distributed denial-of-service (DDoS) attack, which is considered one of the dangerous attacks targeting network security. As such, it is necessary to find smart solutions to address such attacks swiftly. To address these attacks, this research proposed a hybrid IDS to detect cyber-attacks on IoT networks via machine learning (ML) algorithms, namely, XGBoost, K-nearest neighbors, and stochastic gradient descent (SGD), while classifiers are combined via an ML ensemble. Grid search CV was used to find the best hyperparameters for each classifier at each classification stage. Random projection was used to select the relevant features for training the model. In the evaluation and performance testing phase of the model, two cybersecurity datasets (CIC-IDS2017 and CIC-DDoS2019) were used to test the efficiency of the model in detecting zero-day threats. The best results were obtained for the CIC-DDoS2019 dataset, where 20 features out of the total selection were used. The model was able to achieve an accuracy of 99.91% and an intrusion detection time of 0.22 seconds. The confusion matrix results also revealed a reduction in false alarms. The results and their comparison with those of recent relevant studies demonstrated the effectiveness of the hybrid model in securing IoT networks from zero-day attacks as well as its superiority in terms of accuracy and intrusion detection time. This study is an important step in enhancing security in the IoT environment by presenting a new hybrid model that is capable of dealing with zero-day attacks that are difficult to detect with traditional models.

Hybrid Prediction Model with Improved Score Level Fusion for Heart Disease Diagnosis

Heart disease diagnosis is a challenging task, which provides an automated forecast of the patient's heart illness to make future treatment simpler. This has led to extensive interest in heart disease diagnostics in the medical sector. However, as there are various risks, the prediction must be more appropriate to avoid death. This work intends to develop the Hybrid Prediction Model with Improved Score Level Fusion (HPISLF) for Heart Disease Prediction. Preprocessing is the first process, where improved min-max normalization is done to preprocess the input data. Feature extraction plays a major role as it extracts additional information from the input data via extracting HOS, Improved Holoentropy-based features, and MI are extracted. Also, proposing a hybrid classification model for diagnosis, which trains the model with the extracted feature set. The final classification outcome is determined by the improved score level fusion that fuses the classification outcomes from both the classifiers, CNN and DeepMaxout. The performance of the proposed work is validated and compared over the conventional methods in terms of accuracy, precision, and other measures.

Modified U-Net and Convolutional Networks for Breast Cancer Segmentation and Classification with New Texture Descriptors

Effective techniques for detection and classification are necessary to enhance the results of patients with common fatal diseases like breast cancer. With benefits including increased effectiveness and precision, DL and ML methods are currently being used as effective tools in the breast cancer diagnosis process. However, the efficiency and stability constraints of current approaches highlight the necessity for additional study. Therefore, this work suggested a new model of modified U-Net and convolutional networks for Breast Cancer (BC) segmentation and classification with new texture descriptors and the proposed model is named “U-TexBCNet”. First, mammography images are preprocessed by enhancing the image using the CLAHE filter and after inputting the preprocessed image into the modified U-Net-based segmentation for the segmentation process, the modified U-Net segmentation is named as “HexpNet”. Once the segmentation process is completed, the feature extraction process takes place. Here, the features include shape feature, color feature, VGG-16 feature, Inception V3 feature, GoogLeNet and modified LGTP feature extracted from the segmented image. Then, the extracted features are inputted into the hybrid classification model including modified CNN and LSTM. Lastly, the outcome from the classifiers (Modified CNN and LSTM) is averaged together to get the outcome of the proposed model. Ultimately, the results of the simulation are evaluated using both positive and negative criteria by contrasting them with the conventional models.

Improving Clinical Preparedness: Community Health Nurses and Early Hypoglycemia Prediction in Type 2 Diabetes Using Hybrid Machine Learning Techniques.

The aim of the study was to analyze the data of diabetic patients regarding warning signs of hypoglycemia to predict it at an early stage using various novel machine learning (ML) algorithms. Individual interviews with diabetic patients were conducted over 6 months to acquire information regarding their experience with hypoglycemic episodes. This information included warning signs of hypoglycemia, such as incoherent speech, exhaustion, weakness, and other clinically relevant cases of low blood sugar. Researchers used supervised, unsupervised, and hybrid techniques. In supervised techniques, researchers applied regression, while in hybrid classification ML techniques were used. In a 5-fold cross-validation approach, the prediction performance of seven models was examined using the area under the receiver operating characteristic curve (AUROC). We analyzed the data of 290 diabetic patients with low blood sugar episodes. Our investigation discovered that gradient boosting and neural networks performed better in regression, with accuracies of 0.416 and 0.417, respectively. In classification models, gradient boosting, AdaBoost, and random forest performed better overall, with AUC scores of 0.821, 0.814, and 0.821, individually. Precision values were 0.779, 0.775, and 0.776 for gradient boosting, AdaBoost, and random forest, respectively. AdaBoost and Gradient Boosting models, in particular, outperformed all others in predicting the probability of clinically severe hypoglycemia. These techniques enable community health nurses to predict hypoglycemia at an early stage and provide the necessary therapies to patients to prevent complications resulting from hypoglycemia.

A Robust and Efficient Hybrid Classification Model for Early Diagnosis of Chest X-Ray Images of COVID-19

لقد كانت هناك جائحة كوفيد-19 منذ ديسمبر 2019، ويتطلب العلاج الطبي الناجح لمرضى كوفيد-19 تشخيصًا سريعًا ودقيقًا. تتطلب مكافحة جائحة كوفيد-19 نظامًا آليًا يستخدم التعلم بالانتقال العميق لتشخيص الفيروس باستخدام الأشعة السينية على الصدر (CXR). يتم استخدام CXR بشكل متكرر في الرعاية الصحية لأنها توفر إمكانية التشخيص السريع والدقيق للأمراض. تتضمن أنظمة التشخيص الآلي بمساعدة الكمبيوتر (CAD) التعلم الآلي أو التعلم العميق لتعزيز الكفاءة والدقة، وبالتالي تقليل المشكلات المستقبلية. يمكن استخدام العديد من أنظمة الذكاء الاصطناعي القائمة على التعلم العميق للتشخيص؛ ومن بين أكثر هذه الشبكات استخدامًا شبكة CNN، التي تم تطويرها لأول مرة وأظهرت دقة مشجعة في تحديد المرضى المؤكد إصابتهم بفيروس كوفيد-19 باستخدام صور CXR. ومن خلال استخدام صور الأشعة السينية، سيعمل هذا العمل على تصميم التعلم الآلي والتعلم العميق لتوفير تشخيص أسرع لعدوى كوفيد-19. ونتيجة لذلك، تستخدم تقنية التعلم بالنقل العميق نموذجًا موجودًا أولاً، ثم تطبق البيانات المطلوبة عليه مرة أخرى. حيث تم استخدام نموذج التعلم Densenet201transfer، وهو أحد تقنيات التعلم عن بعد، كاستخراج الميزة ودمجها مع خوارزمية الإدراك الحسي متعدد الطبقات؛ تم تطبيق هذه التقنية على مجموعة بيانات تابعة للمعهد الوطني للصحة (NIH)، حيث تم استخدام العديد من مقاييس الأداء مثل الدقة والدقة والنوعية والحساسية، حيث أثبتت التجربة كفاءة الخوارزمية المستخدمة من حيث الدقة بنسبة 98.82 %. تعتبر هذه النتائج مشجعة عند مقارنتها بنماذج التعلم عن بعد الأخرى التي تم تدريبها على مجموعة البيانات المماثلة

A hybrid approach for Bengali sentence validation

Bengali is the official language of Bangladesh and is widely used in Bangladesh and West Bengal in India. Due to the growing accessibility of the internet and smart devices, the use of digital text material and documents in Bengali is growing with time. An automated Bengali Sentence Validation System is proposed in this study to effectively determine the correctness of sentences in such extensively available Bengali content. As far as we know, no substantial work has been done in the field of Bengali Sentence Validation utilizing deep learning approaches. Due to the lack of linguistic resources, sophisticated Natural Language Processing tools, and benchmark datasets, developing an automated Sentence Validation System for a limited-resource language like Bengali is challenging. Additionally, Bengali Sentences come in two morphological varieties (Sadhu-bhasha and Cholito-bhasha), making the validation process more challenging. The proposed automated Bengali Sentence Validation system contains the CNN-BiLSTM hybrid classifier model. As of now, there is no standard dataset for Bengali sentence validation. Due to the lack of a standard dataset, we collected Bengali sentences from different sources in Bangladesh and developed a Bengali Sentence Validation (BSV) Dataset with around 5000 labelled sentences arranged into two categories such as correct and incorrect. Experimental results demonstrate that the proposed system outperformed other classifier models and existing approaches for Bengali Sentence Validation and is able to categorize a wide range of Bengali sentences based on their correctness. The system’s F1 score for the Bengali Sentence Validation is 98%.

HCO-RLF: Hybrid classification optimization using recurrent learning and fuzzy for COVID-19 detection on CT images

COVID-19 infection detection through initial lesion classification provides early diagnosis and prevents breathing difficulties. Detecting the infectious part of the lungs using computerized tomography (CT) images has become an instantaneous detection method. In this article, a Hybrid Classification Optimization (HCO) using Recurrent Leaning and Fuzzy (RLF) is proposed. The neural network classifies infected and non-infected regions using pixel distributions and their variations. This is performed by identifying missing features and training the recurrent network using regional differences. Based on the feature availability, recurrent learning classifies the region through the input dataset and recurrent training correlation. The fuzzy predicts missing features through substituted derivatives obtained between wide ranges of variations. In this process, the maximum fuzzy derivatives for feature substitution are used for infected region prediction. The least fuzzy derivatives are prevented from the training layer to reduce false rates in region classification. This joint process improves the training consistency to leverage the detection and region classification accuracies. The proposed HCO-RLF improves detection and classification accuracy, and precision by 11.96 %, 9.98 %, and 13.42 % for the varying classification rates. Besides, the results are obtained in comparison with the existing DR-MIL, DSAE, and BS-FSA methods discussed later in the article.

Clinical knowledge-guided hybrid classification network for automatic periodontal disease diagnosis in X-ray image

Accurate classification of periodontal disease through panoramic X-ray images carries immense clinical importance for effective diagnosis and treatment. Recent methodologies attempt to classify periodontal diseases from X-ray images by estimating bone loss within these images, supervised by manual radiographic annotations for segmentation or keypoint detection. However, these annotations often lack consistency with the clinical gold standard of probing measurements, potentially causing measurement inaccuracy and leading to unstable classifications. Additionally, the diagnosis of periodontal disease necessitates exceptional sensitivity. To address these challenges, we introduce HC-Net, an innovative hybrid classification framework devised for accurately classifying periodontal disease from X-ray images. This framework comprises three main components: tooth-level classification, patient-level classification, and a learnable adaptive noisy-OR gate. In the tooth-level classification, we initially employ instance segmentation to individually identify each tooth, followed by tooth-level periodontal disease classification. For patient-level classification, we utilize a multi-task strategy to concurrently learn patient-level classification and a Classification Activation Map (CAM) that signifies the confidence of local lesion areas within the panoramic X-ray image. Eventually, our adaptive noisy-OR gate acquires a hybrid classification by amalgamating predictions from both levels. In particular, we incorporate clinical knowledge into the workflows used by professional dentists, targeting the enhanced handling of sensitivity of periodontal disease diagnosis. Extensive empirical testing on a dataset amassed from real-world clinics demonstrates that our proposed HC-Net achieves unparalleled performance in periodontal disease classification, exhibiting substantial potential for practical application.

Fast Accurate Detection and Classification of Kidney Diseases from CT Images using Hybrid Classifiers

Fast Accurate Detection and Classification of Kidney Diseases from CT Images using Hybrid Classifiers

HYBRID ARCHITECTURE WITH IMPROVED SCORE LEVEL FUSION FOR PATIENT WAITING TIME PREDICTION

Hospitals are overcrowded in proportion to the massive increase in population, making it difficult for hospitals to manage or arrange or shorten patient wait time during medical care. However, it is difficult to overstate the significance of patient waiting time management and predictability. In most of the hospitals, the clinical workflow is defined and driven by the patient queues. Predicting the incoming patients and waiting times has thus emerged as one of the most crucial clinical management techniques. Our proposed work aims to address the limitations of current waiting time prediction models by providing a more sophisticated, data-driven approach that can adapt to the complexities of healthcare environments. This work focuses on developing a new model for predicting the patient’s waiting time. The primary aim of this research is to create a reliable predictive model capable of accurately anticipating patient waiting times. This innovation aims to enhance both patient satisfaction and operational efficiency within healthcare settings. By offering healthcare administrators a tool to better manage patient flow, allocate resources efficiently, and minimize waiting times, this model seeks to optimize overall healthcare service delivery. The initial process carried out is pre-processing which includes data acquisition and improved [Formula: see text]-score normalization. Subsequently, entropy features, correlation features, and improved mutual information (MI) features are extracted. Then, prediction is carried out using hybrid classifier (HC) that involves a convolutional neural network (CNN) and bidirectional gated recurrent unit (Bi-GRU). Finally, improved score level fusion (ISLF) is introduced to get absolute outcomes on WTP. The analysis outcomes on varied error metrics show the efficacy of the proposed WTP model.

Aspect Based Feature Extraction in Sentiment Analysis using Bi-GRU-LSTM Model

In Natural Language Processing (NLP), Sentiment Analysis (SA) is a fundamental process which predicts the sentiment expressed in sentences. In contrast to conventional sentiment analysis, Aspect-Based Sentiment Analysis (ABSA) employs a more nuanced approach to assess the sentiment of individual aspects or components within a document or sentence. Its objective is to identify the sentiment polarity, such as positive, neutral, or negative, associated with particular elements disclosed within a sentence. This research introduces a novel sentiment analysis technique that proves to be more efficient in sentiment analysis compared to current methods. The suggested sentiment analysis method undergoes three key phases: 1. Pre-processing 2. Extraction of aspect sentiment and 3. Sentiment analysis classification. The input text data undergoes pre-processing through the implementation of four typical text normalization techniques, which include stemming, stop word elimination, lemmatization, and tokenization. By employing these methods, the provided text data is prepared and fed into the aspect sentiment extraction phase. During the aspect sentiment extraction phase, features are obtained through a series of steps, including enhanced ATE (Aspect Term Extraction), assessment of word length, and determination of cosine similarity. By following these steps, the relevant features are extracted on the basis of aspects and sentiments involved in the text data. Further, a hybrid classification model is proposed to classify sentiments. In this work, two of the Deep Learning (DL) classifiers, Bi-directional Gated Recurrent Unit (Bi-GRU) and Long Short-Term memory (LSTM) are used in proposing a hybrid classification model which classifies the sentiments effectively and provides accurate final predicted results. Moreover, the performance of proposed sentiment analysis technique is analyzed experimentally to show its efficacy over other models.

Intrusion Detection System Using Chaotic Walrus Optimization-based Convolutional Echo State Networks for IoT-assisted Wireless Sensor Networks

Wireless Sensor Networks (WSN) based Internet of Things (IoT) networks have achieved greater research interest due to their multi-purpose data collection and transmission over different geographical locations. However, the fabrication of different cyber-attacks in these networks has been a severe concern for applications such as remote healthcare, military communications, etc. These attacks question the integrity and security of WSN-IOT networks for such applications, and the traditional Intrusion Detection Systems (IDS) have shown increased false alarm rates because of their substantial processing overhead, resource constraints, and low detection rates. This paper presents an intelligent IDS model using Chaotic Walrus Optimization-based Convolutional Echo State Networks (CWO-CESN) to solve existing problems. It increases the detection accuracy of different attacks. In this CWO-CESN-based IDS model, the data are gathered from the sensor/IoT nodes and are pre-processed. Then, the proposed CWO-CESN learns the features from these data and classifies them into attacks and standard data classes. This proposed CWO-CESN is a hybrid classifier model that integrates Convolutional Neural Networks (CNN) and Echo State Networks (ESN) as a single model and employs Chaotic map-based population initialized Walrus Optimizer for optimizing the hyperparameters. Validated on benchmark datasets, the proposed CWO-CESN-based IDS model attained accuracies of 99%, 99.5%, and 99.8% for the detection of different attacks for NSL-KDD, WSN-DS and IoT-23 datasets, respectively, and ensured secured and reliable application in significant fields.

Early prediction of grape disease attack using a hybrid classifier in association with IoT sensors

Machine learning with IoT practices in the agriculture sector has the potential to address numerous challenges encountered by farmers, including disease prediction and estimation of soil profile. This paper extensively explores the classification of diseases in grape plants and provides detailed information about the conducted experiments. It is important to keep track of each crop's current environmental conditions because different environmental conditions, such as humidity, temperature, moisture, leaf wetness, light intensity, wind speed, and wind direction, can affect or sustain the quality of a crop. IoT will increasingly be used in precision agriculture and smart environments to detect, gather, and share data about environmental occurrences. The environmental factor that is active at all times and has an effect on a crop from its cultivation to harvest. With the aid of an IoT, we will monitor the following factors: temperature, humidity, and leaf wetness, all of which have an impact on the overall quality and lifespan of grapes. A Self-created database of weather parameter using sensors is introduced in this article. It consists of 5 categories with a total of 10,000 records. Here, experiment has been carried out using our dataset to predict grape diseases on various machines learning algorithm. The system receives overall accuracy of 98.25 % for Powdery Mildew, 98.85 % for Downy Mildew and 93.95 % for Bacterial Leaf Spot.

Detection of Sleeping Pattern Using EEG Signals with Deep Learning

Background: Recorded brain activity analysis is a major tool used in modern medicine and sleep-based research. However, the volume and complexity of the data make manual investigation difficult. Furthermore, high inter-subject variability presents a challenge to deep learning techniques, especially in the detection of sleep-related EEG patterns and artifacts. Methods: Sleep Pattern Identifier (SPI), a hybrid classifier, was used in this study to address the aforementioned issues. SPI tests the efficacy of artifact detection while examining strategies and tactics used in real-world sleep research. A focus was on inter-subject variability, especially in data gathered from participants suffering from sleep disorders. For comparison and visual inspection of the data, formal statistical measures like accuracy, model loss, precision, recall, and ROC were employed. Results: With an accuracy of 94.85%, the suggested model outperformed current techniques and demonstrated higher accuracy. Further, the Sleep-EDF dataset was used in this investigation. Conclusion: The conclusion highlights the effectiveness of the SPI hybrid classifier in identifying EEG patterns, especially in situations where sleep research is conducted in the real world. Research methodologies based on sleep have made significant progress in handling artifacts and adapting to inter-subject variability.

Classification of complex power quality disturbances under noisy environment using Root-Music and least square techniques

In this paper, a hybrid classification approach for single and complex power quality disturbances is proposed. This approach combines the Root-Music and least square techniques. Firstly, the frequencies are extracted by the Root-Music technique, including those of harmonic components. Afterward, the amplitude components are extracted using the least square technique. Once frequency and amplitude components are extracted, the waveform distortion of voltage is analyzed using the known criteria of total harmonic distortion. The value of this criterion allows for pre-classifying the disturbance into one of two classes. Then, the value of the extracted amplitudes of signals allows the identification of the corresponding disturbance. The classifier performances are validated by several tests using real power and synthetic data. The achieved results prove the effectiveness of the classifier compared to recent state-of-the-art techniques, especially in highly noisy environments. These results show that the proposed classifier leads to the highest classification accuracy of 100% at 20 dB, compared to those of other classifiers. Furthermore, this classifier demonstrates high classification accuracy even under challenging conditions with a high noise level of 10 dB.

Assessment of Soil Physico-chemical Properties in Sugarcane Cultivation Areas of Navsari District, Gujarat, India

Sentinel 2 satellite data from the year 2021 were acquired from the Copernicus site to identify the sugarcane producing area in the Navsari district. Hybrid classification approach i.e., supervised and unsupervised with ground truth data were applied using ERDAS IMAGINE software. After image classification, 2.5 km x 2.5 km grid was prepared in Q-GIS software which along with classified sugarcane area were overlapped for site identification. Then, random soil surface and sub-surface samples were collected with reference from grid of intensive sugarcane growing area. The particle density (2.10 to 2.76 g cm-3 with the mean value of 2.58 g cm-3) and bulk density (1.10 to 1.68 g cm-3 with the mean value of 1.33 g cm-3) of surface soil were found to be lower than sub-surface soil (2.18 to 2.79 g cm-3 and 1.15 to 1.68 g cm-3 with the mean value of 2.62 g cm-3 and 1.42 g cm-3 respectively) while porosity (31.60 to 59.26% with the mean value of 48.39%) and maximum water holding capacity (22.77 to 51.92% with the mean value of 39.60%) of surface soil were found to be higher than sub-surface soil (29.54 to 57.71% and 20.98 to 48.91% with the mean value of 45.60% and 36.81% respectively). The pH of soil surface showed range from 6.08 to 8.37 while pH of soil sub-surface noted range of 6.00 to 8.50. The electrical conductivity of surface and sub-surface soil showed the range of 0.011 to 1.580 dS m-1 and 0.010 to 1.586 dS m-1. The soil organic carbon of surface soil ranged from 0.06 to 0.89% while that of sub-surface soil varied from 0.02 to 0.85%.

Charting Atomic Characteristics: A Pathway to Element Classification

The classification of elements serves as a rich source of knowledge in chemistry. This article explores the atomic model based on wave mechanics, represented by a graph characterizing the atom, to elucidate both periodic and ordered classifications of elements. Three types of classifications emerge from this graph: periodic, ordered, and hybrid classifications, with the latter resembling periodic tables but incorporating elements of the ordered form. The periodic table stands out as one of the most profound and unifying concepts in modern science. New illustration methods, such as condensed tables with orders and periods, are introduced. The results underscore the conclusiveness of the findings, revealing that classifications extend beyond periodicity, encompassing ordered types of tables as well. This research sheds light on the diverse approaches to classifying elements and opens avenues for further exploration in the field of chemistry.

HYBRIDIZED MACHINE LEARNING PREDICTION FOR THE EXPOSURE OF PHISHING$WEBSITES

One of the most popular ways that people utilise the internet for illegal activities is phishing. Phishing websites are those that impersonate trustworthy websites while still appearing and sounding authentic. The purpose of their fabrication is to trick the receiver into believing that the item is authentic. These days, phishing schemes are riskier and more intricate than ever. Artificial intelligence-based ML & deep learning techniques can be used to anticipate phishing websites. A classification system based on ML may be employed to detect possible phishing websites. We provide a mixed machine learning approach for phishing site forecast in this study. The outcomes of the studies demonstrate that the recommended process performs more effectively in categorizing malevolent URLs than more contemporary methods. A graphical illustration of the accuracy comparison of several approaches is shown in Figure 4. The simulated results demonstrate that a compared to current method, the suggested hybrid classification strategy achieves higher accuracy. While the current KNN achieves 96% accuracy, the hybrid approach achieves 98.14% accuracy.