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Rasheed Z, Ma YK, Ullah I, Ghadi YY, Khan MZ, Khan MA, Abdusalomov A, Alqahtani F, Shehata AM. Brain Tumor Classification from MRI Using Image Enhancement and Convolutional Neural Network Techniques. Brain Sci 2023; 13:1320. [PMID: 37759920 PMCID: PMC10526310 DOI: 10.3390/brainsci13091320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 09/10/2023] [Accepted: 09/12/2023] [Indexed: 09/29/2023] Open
Abstract
The independent detection and classification of brain malignancies using magnetic resonance imaging (MRI) can present challenges and the potential for error due to the intricate nature and time-consuming process involved. The complexity of the brain tumor identification process primarily stems from the need for a comprehensive evaluation spanning multiple modules. The advancement of deep learning (DL) has facilitated the emergence of automated medical image processing and diagnostics solutions, thereby offering a potential resolution to this issue. Convolutional neural networks (CNNs) represent a prominent methodology in visual learning and image categorization. The present study introduces a novel methodology integrating image enhancement techniques, specifically, Gaussian-blur-based sharpening and Adaptive Histogram Equalization using CLAHE, with the proposed model. This approach aims to effectively classify different categories of brain tumors, including glioma, meningioma, and pituitary tumor, as well as cases without tumors. The algorithm underwent comprehensive testing using benchmarked data from the published literature, and the results were compared with pre-trained models, including VGG16, ResNet50, VGG19, InceptionV3, and MobileNetV2. The experimental findings of the proposed method demonstrated a noteworthy classification accuracy of 97.84%, a precision success rate of 97.85%, a recall rate of 97.85%, and an F1-score of 97.90%. The results presented in this study showcase the exceptional accuracy of the proposed methodology in accurately classifying the most commonly occurring brain tumor types. The technique exhibited commendable generalization properties, rendering it a valuable asset in medicine for aiding physicians in making precise and proficient brain diagnoses.
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Affiliation(s)
- Zahid Rasheed
- School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China
| | - Yong-Kui Ma
- School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China
| | - Inam Ullah
- Department of Computer Engineering, Gachon University, Sujeong-gu, Seongnam-si 13120, Republic of Korea
| | - Yazeed Yasin Ghadi
- Department of Computer Science, Al Ain University, Abu Dhabi P.O. Box 112612, United Arab Emirates
| | - Muhammad Zubair Khan
- Faculty of Basic Sciences, Balochistan University of Information Technology Engineering and Management Sciences, Quetta 87300, Pakistan
| | - Muhammad Abbas Khan
- Department of Electrical Engineering, Balochistan University of Information Technology, Engineering and Management Sciences, Quetta 87300, Pakistan
| | - Akmalbek Abdusalomov
- Department of Artificial Intelligence, Tashkent State University of Economics, Tashkent 100066, Uzbekistan;
| | - Fayez Alqahtani
- Software Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 12372, Saudi Arabia
| | - Ahmed M. Shehata
- Computer Science and Engineering Department, Faculty of Electronic Engineering, Menoufia University, Menofia 32511, Egypt
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2
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Pal R, Adhikari D, Heyat MBB, Ullah I, You Z. Yoga Meets Intelligent Internet of Things: Recent Challenges and Future Directions. Bioengineering (Basel) 2023; 10:459. [PMID: 37106646 PMCID: PMC10135646 DOI: 10.3390/bioengineering10040459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 03/30/2023] [Accepted: 04/06/2023] [Indexed: 04/29/2023] Open
Abstract
The physical and mental health of people can be enhanced through yoga, an excellent form of exercise. As part of the breathing procedure, yoga involves stretching the body organs. The guidance and monitoring of yoga are crucial to ripe the full benefits of it, as wrong postures possess multiple antagonistic effects, including physical hazards and stroke. The detection and monitoring of the yoga postures are possible with the Intelligent Internet of Things (IIoT), which is the integration of intelligent approaches (machine learning) and the Internet of Things (IoT). Considering the increment in yoga practitioners in recent years, the integration of IIoT and yoga has led to the successful implementation of IIoT-based yoga training systems. This paper provides a comprehensive survey on integrating yoga with IIoT. The paper also discusses the multiple types of yoga and the procedure for the detection of yoga using IIoT. Additionally, this paper highlights various applications of yoga, safety measures, various challenges, and future directions. This survey provides the latest developments and findings on yoga and its integration with IIoT.
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Affiliation(s)
- Rishi Pal
- Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Deepak Adhikari
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610056, China
| | - Md Belal Bin Heyat
- IoT Research Center, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
| | - Inam Ullah
- Department of Computer Engineering, Gachon University, Sujeong-gu, Seongnam 13120, Republic of Korea
| | - Zili You
- Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
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Rasheed Z, Ma YK, Ullah I, Al Shloul T, Tufail AB, Ghadi YY, Khan MZ, Mohamed HG. Automated Classification of Brain Tumors from Magnetic Resonance Imaging Using Deep Learning. Brain Sci 2023; 13:brainsci13040602. [PMID: 37190567 DOI: 10.3390/brainsci13040602] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 03/28/2023] [Accepted: 03/28/2023] [Indexed: 04/05/2023] Open
Abstract
Brain tumor classification is crucial for medical evaluation in computer-assisted diagnostics (CAD). However, manual diagnosis of brain tumors from magnetic resonance imaging (MRI) can be time-consuming and complex, leading to inaccurate detection and classification. This is mainly because brain tumor identification is a complex procedure that relies on different modules. The advancements in Deep Learning (DL) have assisted in the automated process of medical images and diagnostics for various medical conditions, which benefits the health sector. Convolutional Neural Network (CNN) is one of the most prominent DL methods for visual learning and image classification tasks. This study presents a novel CNN algorithm to classify the brain tumor types of glioma, meningioma, and pituitary. The algorithm was tested on benchmarked data and compared with the existing pre-trained VGG16, VGG19, ResNet50, MobileNetV2, and InceptionV3 algorithms reported in the literature. The experimental results have indicated a high classification accuracy of 98.04%, precision, recall, and f1-score success rate of 98%, respectively. The classification results proved that the most common kinds of brain tumors could be categorized with a high level of accuracy. The presented algorithm has good generalization capability and execution speed that can be helpful in the field of medicine to assist doctors in making prompt and accurate decisions associated with brain tumor diagnosis.
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Affiliation(s)
- Zahid Rasheed
- School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China
| | - Yong-Kui Ma
- School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China
| | - Inam Ullah
- Department of Computer Engineering, Gachon University, Sujeong-gu, Seongnam 13120, Republic of Korea
| | - Tamara Al Shloul
- Department of General Education, Liwa College of Technology, Abu Dhabi P.O. Box 41009, United Arab Emirates
| | - Ahsan Bin Tufail
- Department of Computer Science, National University of Science and Technology, Balochistan Campus, Quetta 87300, Pakistan
| | - Yazeed Yasin Ghadi
- Department of Computer Science, Al Ain University, Abu Dhabi P.O. Box 112612, United Arab Emirates
| | | | - Heba G. Mohamed
- Department of Electrical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
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Classification and diagnostic prediction of breast cancer metastasis on clinical data using machine learning algorithms. Sci Rep 2023; 13:485. [PMID: 36627367 PMCID: PMC9831019 DOI: 10.1038/s41598-023-27548-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Accepted: 01/04/2023] [Indexed: 01/12/2023] Open
Abstract
Metastatic Breast Cancer (MBC) is one of the primary causes of cancer-related deaths in women. Despite several limitations, histopathological information about the malignancy is used for the classification of cancer. The objective of our study is to develop a non-invasive breast cancer classification system for the diagnosis of cancer metastases. The anaconda-Jupyter notebook is used to develop various python programming modules for text mining, data processing, and Machine Learning (ML) methods. Utilizing classification model cross-validation criteria, including accuracy, AUC, and ROC, the prediction performance of the ML models is assessed. Welch Unpaired t-test was used to ascertain the statistical significance of the datasets. Text mining framework from the Electronic Medical Records (EMR) made it easier to separate the blood profile data and identify MBC patients. Monocytes revealed a noticeable mean difference between MBC patients as compared to healthy individuals. The accuracy of ML models was dramatically improved by removing outliers from the blood profile data. A Decision Tree (DT) classifier displayed an accuracy of 83% with an AUC of 0.87. Next, we deployed DT classifiers using Flask to create a web application for robust diagnosis of MBC patients. Taken together, we conclude that ML models based on blood profile data may assist physicians in selecting intensive-care MBC patients to enhance the overall survival outcome.
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Ogundokun RO, Misra S, Akinrotimi AO, Ogul H. MobileNet-SVM: A Lightweight Deep Transfer Learning Model to Diagnose BCH Scans for IoMT-Based Imaging Sensors. SENSORS (BASEL, SWITZERLAND) 2023; 23:656. [PMID: 36679455 PMCID: PMC9863875 DOI: 10.3390/s23020656] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 12/02/2022] [Accepted: 12/16/2022] [Indexed: 06/17/2023]
Abstract
Many individuals worldwide pass away as a result of inadequate procedures for prompt illness identification and subsequent treatment. A valuable life can be saved or at least extended with the early identification of serious illnesses, such as various cancers and other life-threatening conditions. The development of the Internet of Medical Things (IoMT) has made it possible for healthcare technology to offer the general public efficient medical services and make a significant contribution to patients' recoveries. By using IoMT to diagnose and examine BreakHis v1 400× breast cancer histology (BCH) scans, disorders may be quickly identified and appropriate treatment can be given to a patient. Imaging equipment having the capability of auto-analyzing acquired pictures can be used to achieve this. However, the majority of deep learning (DL)-based image classification approaches are of a large number of parameters and unsuitable for application in IoMT-centered imaging sensors. The goal of this study is to create a lightweight deep transfer learning (DTL) model suited for BCH scan examination and has a good level of accuracy. In this study, a lightweight DTL-based model "MobileNet-SVM", which is the hybridization of MobileNet and Support Vector Machine (SVM), for auto-classifying BreakHis v1 400× BCH images is presented. When tested against a real dataset of BreakHis v1 400× BCH images, the suggested technique achieved a training accuracy of 100% on the training dataset. It also obtained an accuracy of 91% and an F1-score of 91.35 on the test dataset. Considering how complicated BCH scans are, the findings are encouraging. The MobileNet-SVM model is ideal for IoMT imaging equipment in addition to having a high degree of precision. According to the simulation findings, the suggested model requires a small computation speed and time.
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Affiliation(s)
- Roseline Oluwaseun Ogundokun
- Department of Multimedia Engineering, Kaunas University of Technology, 44249 Kaunas, Lithuania
- Department of Computer Science, Landmark University, Omu Aran 251103, Kwara, Nigeria
| | - Sanjay Misra
- Department of Computer Science and Communication, Østfold University College, 1757 Halden, Norway
| | | | - Hasan Ogul
- Department of Computer Science and Communication, Østfold University College, 1757 Halden, Norway
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6
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FRE-Net: Full-region enhanced network for nuclei segmentation in histopathology images. Biocybern Biomed Eng 2023. [DOI: 10.1016/j.bbe.2023.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
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7
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Wahab F, Ullah I, Shah A, Khan RA, Choi A, Anwar MS. Design and implementation of real-time object detection system based on single-shoot detector and OpenCV. Front Psychol 2022; 13:1039645. [PMID: 36405169 PMCID: PMC9666404 DOI: 10.3389/fpsyg.2022.1039645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 10/05/2022] [Indexed: 11/24/2022] Open
Abstract
Computer vision (CV) and human-computer interaction (HCI) are essential in many technological fields. Researchers in CV are particularly interested in real-time object detection techniques, which have a wide range of applications, including inspection systems. In this study, we design and implement real-time object detection and recognition systems using the single-shoot detector (SSD) algorithm and deep learning techniques with pre-trained models. The system can detect static and moving objects in real-time and recognize the object's class. The primary goals of this research were to investigate and develop a real-time object detection system that employs deep learning and neural systems for real-time object detection and recognition. In addition, we evaluated the free available, pre-trained models with the SSD algorithm on various types of datasets to determine which models have high accuracy and speed when detecting an object. Moreover, the system is required to be operational on reasonable equipment. We tried and evaluated several deep learning structures and techniques during the coding procedure and developed and proposed a highly accurate and efficient object detection system. This system utilizes freely available datasets such as MS Common Objects in Context (COCO), PASCAL VOC, and Kitti. We evaluated our system's accuracy using various metrics such as precision and recall. The proposed system achieved a high accuracy of 97% while detecting and recognizing real-time objects.
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Affiliation(s)
- Fazal Wahab
- College of Computer Science and Technology, Northeastern University, Shenyang, China
| | - Inam Ullah
- BK21 Chungbuk Information Technology Education and Research Center, Chungbuk National University, Cheongju, South Korea
| | - Anwar Shah
- School of Computing, National University of Computer and Emerging Sciences, Faisalabad, Pakistan
| | - Rehan Ali Khan
- Department of Electrical Engineering, University of Science and Technology, Bannu, Pakistan
| | - Ahyoung Choi
- Department of AI and Software, Gachon University, Seongnam, South Korea
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Samee NA, Mahmoud NF, Atteia G, Abdallah HA, Alabdulhafith M, Al-Gaashani MSAM, Ahmad S, Muthanna MSA. Classification Framework for Medical Diagnosis of Brain Tumor with an Effective Hybrid Transfer Learning Model. Diagnostics (Basel) 2022; 12:diagnostics12102541. [PMID: 36292230 PMCID: PMC9600529 DOI: 10.3390/diagnostics12102541] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 10/13/2022] [Accepted: 10/13/2022] [Indexed: 11/16/2022] Open
Abstract
Brain tumors (BTs) are deadly diseases that can strike people of every age, all over the world. Every year, thousands of people die of brain tumors. Brain-related diagnoses require caution, and even the smallest error in diagnosis can have negative repercussions. Medical errors in brain tumor diagnosis are common and frequently result in higher patient mortality rates. Magnetic resonance imaging (MRI) is widely used for tumor evaluation and detection. However, MRI generates large amounts of data, making manual segmentation difficult and laborious work, limiting the use of accurate measurements in clinical practice. As a result, automated and dependable segmentation methods are required. Automatic segmentation and early detection of brain tumors are difficult tasks in computer vision due to their high spatial and structural variability. Therefore, early diagnosis or detection and treatment are critical. Various traditional Machine learning (ML) techniques have been used to detect various types of brain tumors. The main issue with these models is that the features were manually extracted. To address the aforementioned insightful issues, this paper presents a hybrid deep transfer learning (GN-AlexNet) model of BT tri-classification (pituitary, meningioma, and glioma). The proposed model combines GoogleNet architecture with the AlexNet model by removing the five layers of GoogleNet and adding ten layers of the AlexNet model, which extracts features and classifies them automatically. On the same CE-MRI dataset, the proposed model was compared to transfer learning techniques (VGG-16, AlexNet, SqeezNet, ResNet, and MobileNet-V2) and ML/DL. The proposed model outperformed the current methods in terms of accuracy and sensitivity (accuracy of 99.51% and sensitivity of 98.90%).
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Affiliation(s)
- Nagwan Abdel Samee
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Noha F. Mahmoud
- Rehabilitation Sciences Department, Health and Rehabilitation Sciences College, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
- Correspondence: (N.F.M.); (G.A.)
| | - Ghada Atteia
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
- Correspondence: (N.F.M.); (G.A.)
| | - Hanaa A. Abdallah
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Maali Alabdulhafith
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Mehdhar S. A. M. Al-Gaashani
- College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
| | - Shahab Ahmad
- School of Economics & Management, Chongqing University of Post and Telecommunication, Chongqing 400065, China
| | - Mohammed Saleh Ali Muthanna
- Institute of Computer Technologies and Information Security, Southern Federal University, 347922 Taganrog, Russia
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Bhuiyan MR, Abdullah J. Detection on Cell Cancer Using the Deep Transfer Learning and Histogram Based Image Focus Quality Assessment. SENSORS (BASEL, SWITZERLAND) 2022; 22:7007. [PMID: 36146356 PMCID: PMC9504738 DOI: 10.3390/s22187007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 08/15/2022] [Accepted: 08/26/2022] [Indexed: 06/16/2023]
Abstract
In recent years, the number of studies using whole-slide imaging (WSIs) of histopathology slides has expanded significantly. For the development and validation of artificial intelligence (AI) systems, glass slides from retrospective cohorts including patient follow-up data have been digitized. It has become crucial to determine that the quality of such resources meets the minimum requirements for the development of AI in the future. The need for automated quality control is one of the obstacles preventing the clinical implementation of digital pathology work processes. As a consequence of the inaccuracy of scanners in determining the focus of the image, the resulting visual blur can render the scanned slide useless. Moreover, when scanned at a resolution of 20× or higher, the resulting picture size of a scanned slide is often enormous. Therefore, for digital pathology to be clinically relevant, computational algorithms must be used to rapidly and reliably measure the picture's focus quality and decide if an image requires re-scanning. We propose a metric for evaluating the quality of digital pathology images that uses a sum of even-derivative filter bases to generate a human visual-system-like kernel, which is described as the inverse of the lens' point spread function. This kernel is then used for a digital pathology image to change high-frequency image data degraded by the scanner's optics and assess the patch-level focus quality. Through several studies, we demonstrate that our technique correlates with ground-truth z-level data better than previous methods, and is computationally efficient. Using deep learning techniques, our suggested system is able to identify positive and negative cancer cells in images. We further expand our technique to create a local slide-level focus quality heatmap, which can be utilized for automated slide quality control, and we illustrate our method's value in clinical scan quality control by comparing it to subjective slide quality ratings. The proposed method, GoogleNet, VGGNet, and ResNet had accuracy values of 98.5%, 94.5%, 94.00%, and 95.00% respectively.
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Wahab F, Zhao Y, Javeed D, Al-Adhaileh MH, Almaaytah SA, Khan W, Saeed MS, Kumar Shah R. An AI-Driven Hybrid Framework for Intrusion Detection in IoT-Enabled E-Health. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:6096289. [PMID: 36045979 PMCID: PMC9420579 DOI: 10.1155/2022/6096289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 07/22/2022] [Accepted: 07/26/2022] [Indexed: 11/17/2022]
Abstract
E-health has grown into a billion-dollar industry in the last decade. Its device's high throughput makes it an obvious target for cyberattacks, and these environments desperately need protection. In this scientific study, we presented an artificial intelligence (AI)-driven software-defined networking (SDN)-enabled intrusion detection system (IDS) to address increasing cyber threats in the E-health and internet of medical things (IoMT) environments. AI's success in various fields, including big data and intrusion detection systems, has prompted us to develop a flexible and cost-effective approach to protect such critical environments from cyberattacks. We present a hybrid model consisting of long short-term memory (LSTM) and gated recurrent unit (GRU). The proposed model was thoroughly evaluated using the publicly available CICDDoS2019 dataset and conventional evaluation measures. Furthermore, for proper validation, the proposed framework is compared with relevant classifiers, such as cu-GRU+ DNN and cu-BLSTM. We have further compared the proposed model with existing literature to prove its efficacy. Lastly, 10-fold cross-validation is also used to verify that our results are unbiased. The proposed approach has bypassed the current literature with extraordinary performance ramifications such as 99.01% accuracy, 99.04% precision, 98.80 percent recall, and 99.12% F1-score.
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Affiliation(s)
- Fazal Wahab
- College of Computer Science and Technology, Northeastern University, Shenyang 110169, China
| | - Yuhai Zhao
- College of Computer Science and Technology, Northeastern University, Shenyang 110169, China
| | - Danish Javeed
- Software College, Northeastern University, Shenyang 110169, China
| | - Mosleh Hmoud Al-Adhaileh
- Deanship of E-Learning and Distance Education, King Faisal University, P.O. Box 400, Al-Ahsa, Saudi Arabia
| | | | - Wasiat Khan
- Department of Software Engineering, University of Science and Technology Bannu, Bannu, Pakistan
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Detection of Distributed Denial of Service (DDoS) Attacks in IOT Based Monitoring System of Banking Sector Using Machine Learning Models. SUSTAINABILITY 2022. [DOI: 10.3390/su14148374] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Cyberattacks can trigger power outages, military equipment problems, and breaches of confidential information, i.e., medical records could be stolen if they get into the wrong hands. Due to the great monetary worth of the data it holds, the banking industry is particularly at risk. As the number of digital footprints of banks grows, so does the attack surface that hackers can exploit. This paper aims to detect distributed denial-of-service (DDOS) attacks on financial organizations using the Banking Dataset. In this research, we have used multiple classification models for the prediction of DDOS attacks. We have added some complexity to the architecture of generic models to enable them to perform well. We have further applied a support vector machine (SVM), K-Nearest Neighbors (KNN) and random forest algorithms (RF). The SVM shows an accuracy of 99.5%, while KNN and RF scored an accuracy of 97.5% and 98.74%, respectively, for the detection of (DDoS) attacks. Upon comparison, it has been concluded that the SVM is more robust as compared to KNN, RF and existing machine learning (ML) and deep learning (DL) approaches.
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