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Modeling Hybrid Feature-Based Phishing Websites Detection Using Machine Learning Techniques

Annals of Data science 2024年第1期11卷 217-242页

作者： Das Guptta, Sumitra Shahriar, Khandaker Tayef Alqahtani, Hamed Alsalman, Dheyaaldin Sarker, Iqbal H. Department of Computer Science and Engineering Chittagong University of Engineering & ampTechnology Chittagong4349 Bangladesh Unit of Cybersecurity Department of Computer Science Center of Artificial Intelligence King Khalid University Abha Saudi Arabia School of Engineering Computing and Informatics Dar Al-Hekma University Jeddah Saudi Arabia

In this paper, we mainly present a machine learning based approach to detect real-time phishing websites by taking into account URL and hyperlink based hybrid features to achieve high accuracy without relying on any third-party systems. In phishing, the attackers typically try to deceive internet users by masking a webpage as an official genuine webpage to steal sensitive information such as usernames, passwords, social security numbers, credit card information, etc. Anti-phishing solutions like blacklist or whitelist, heuristic, and visual similarity based methods cannot detect zero-hour phishing attacks or brand-new websites. Moreover, earlier approaches are complex and unsuitable for real-time environments due to the dependency on third-party sources, such as a search engine. Hence, detecting recently developed phishing websites in a real-time environment is a great challenge in the domain of cybersecurity. To overcome these problems, this paper proposes a hybrid feature based anti-phishing strategy that extracts features from URL and hyperlink information of client-side only. We also develop a new dataset for the purpose of conducting experiments using popular machine learning classification techniques. Our experimental result shows that the proposed phishing detection approach is more effective having higher detection accuracy of 99.17% with the XG Boost technique than traditional approaches. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.

关键词： Search engines

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Using Machine Learning to Identify and Categorize Personally Identifiable Information and Payment Card Industry Data in Textual Content 4

Using Machine Learning to Identify and Categorize Personally...

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4th International Conference on Advanced Research in computing, ICARC 2024

作者： Arambawela, Milinda Aponso, Achala University of Westminster Computer Science and Engineering London United Kingdom Informatics Institute of Technology Department of Computing Colombo Sri Lanka

ISBN: (纸本)9798350384864

The advent of the Internet has significantly stream-lined daily tasks through the rapid increase of online services. Everyday activities, such as purchasing goods and scheduling appointments with healthcare professionals, have become more speedy, efficient and user-friendly with the integration of the Internet. The continuous improvement of online services has led to many people moving towards digital activities. As a result, it has heightened the recording of personal and payment transaction data across various storage mediums, including databases and log files. The protection and regulation of this sensitive data are imperative, aligning with the guidelines outlined in GDPR and PCI-DSS compliances. Recognizing exposed personal data poses a considerable challenge. This research introduces a novel approach to identifying payment card industry data (PCI) and personally identifiable information (PII). The research project proposes a machine learning-based text classification model utilizing the Convolutional Neural Network (CNN) model to discern PII and PCI data within a given text. The CNN model has been constructed and compared against Naive Bayes, Gradient Boost, Random Forest, and Support Vector Machine (SVM) models. The CNN model achieved the highest accuracy at 0.96 (96%). Additionally, the F1 scores for each class were significant, with PII scoring 0.94, PCI scoring 0.95, and Normal scoring 0.99. Following the model's construction and training, it was employed with the saved tokenizer's word indexes and label encoders in the developed classification tool. This tool successfully delivered the promised results, identifying exposed PII and PCI data. © 2024 IEEE.

关键词： Neural network models

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Survey on Video Security:Examining Threats,Challenges,and Future Trends

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computers, Materials & Continua 2024年第9期80卷 3591-3635页

作者： Ali Asghar Amna Shifa Mamoona Naveed Asghar Department of Artificial Intelligence Faculty of ComputingThe Islamia University of BahawalpurBahawalpur63100Pakistan School of Computer Science College of Science and EngineeringUniversity of GalwayGalwayH91 TK33Ireland

Videos represent the most prevailing form of digital media for communication,information dissemination,and ***,theirwidespread use has increased the risks of unauthorised access andmanipulation,posing significant *** response,various protection approaches have been developed to secure,authenticate,and ensure the integrity of digital *** study provides a comprehensive survey of the challenges associated with maintaining the confidentiality,integrity,and availability of video content,and examining how it can be *** then investigates current developments in the field of video security by exploring two critical research ***,it examine the techniques used by adversaries to compromise video data and evaluate their *** these attack methodologies is crucial for developing effective defense ***,it explores the various security approaches that can be employed to protect video data,enhancing its transparency,integrity,and *** compares the effectiveness of these approaches across different use cases,including surveillance,video on demand(VoD),and medical videos related to disease ***,it identifies potential research opportunities to enhance video data protection in response to the evolving threat *** this investigation,this study aims to contribute to the ongoing efforts in securing video data,providing insights that are vital for researchers,practitioners,and policymakers dedicated to enhancing the safety and reliability of video content in our digital world.

关键词： Attacks threats security services video manipulation video security

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Early fire danger monitoring system in smart cities using optimization-based deep learning techniques with artificial intelligence

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Journal of Reliable Intelligent Environments 2024年第2期10卷 197-210页

作者： Reddy, P. Dileep Kumar Margala, Martin Shankar, S. Siva Chakrabarti, Prasun Telangana Secunderabad India School of Computing and Informatics University of Louisiana at Lafayette Lafayette United States Department of CSE KG Reddy College of Engineering and Technology Telangana Hyderabad501504 India Department of Computer Science and Engineering Sir Padampat Singhania University Rajasthan Udaipur313601 India

One primary safety concern for smart cities is fire. Traditional techniques are not appropriate because of their high false alarm rates, delayed characteristics, and susceptibility in situations with heritage buildings. Smart cities must develop sophisticated methods to mitigate the severe effects of fires and achieve early fire detection in real time. An artificial intelligence-based recurrent neural network with a whale optimization framework (AI-RNN-WO) was introduced to estimate the risk of fire hazards early on. IoT sensor devices are first deployed in smart cities to continuously monitor environmental parameters such as temperature, smoke, flame, relative humidity, fuel moisture, and duff moisture code. These sensed data are then saved in the cloud storage system Firebase. Then, the sensed dataset is updated to the designed model, which pre-processes the data and extracts relevant features from the dataset. The RNN parameters are tuned using whale optimization, which improves the prediction results and attains better accuracy. The performance of the proposed AI-RNN-WO model is validated using a MATLAB tool, and the performance is compared with existing models. The produced model has demonstrated its effectiveness by attaining the highest accuracy (99.5%) and lowest error rate (0.1%). © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Recurrent neural networks

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Development of a machine learning model for predicting abnormalities of commercial airplanes

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Data science and Management 2024年第3期7卷 256-265页

作者： Rossi Passarella Siti Nurmaini Muhammad Naufal Rachmatullah Harumi Veny Fara Nissya Nur Hafidzoh Department of Computer Engineering Faculty of Computer ScienceUniversitas Sriwijaya30662Indonesia Intelligent System Research Group(ISysRG) Faculty of Computer ScienceUniversitas Sriwijaya30662Indonesia Department of Informatics Faculty of Computer ScienceUniversitas Sriwijaya30662Indonesia College of Engineering Studies Universiti Teknologi MARA(UiTM)Shah Alam40450Malaysia

Airplanes are a social necessity for movement of humans,goods,and *** are generally safe modes of transportation;however,incidents and accidents occasionally *** prevent aviation accidents,it is necessary to develop a machine-learning model to detect and predict commercial flights using automatic dependent surveillance–broadcast *** study combined data-quality detection,anomaly detection,and abnormality-classification-model *** research methodology involved the following stages:problem statement,data selection and labeling,prediction-model development,deployment,and *** data labeling process was based on the rules framed by the international civil aviation organization for commercial,jet-engine flights and validated by expert commercial *** results showed that the best prediction model,the quadratic-discriminant-analysis,was 93%accurate,indicating a“good fit”.Moreover,the model’s area-under-the-curve results for abnormal and normal detection were 0.97 and 0.96,respectively,thus confirming its“good fit”.

关键词： Automatic dependent surveillance-broadcast data Commercial airplanes accident Data-labeling Machine learning Prediction model

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Efficient Key Management in Lightweight Cryptography: A New Hashing-Based Approach 8

Efficient Key Management in Lightweight Cryptography: A New ...

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8th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2024

作者： Al-Mahmood, Haider H. Alsaad, Saad N. Informatics Institute for Postgraduate Studies Iraqi Commission for Computers and Informatics Baghdad Iraq University of Mustansiriyah Department of Computer Science College of Science Baghdad Iraq

ISBN: (纸本)9798350354423

Lightweight encryption methods are introduced to overcome the challenges of high-power consumption and high memory usage in the traditional cryptography system. The primary goal of the lightweight encryption technique is to make it suitable for use in devices provided with lower power and small memories. The importance of lightweight cryptosystem is its ability to provide suitable security for data encryption without consuming high level of power and without need for big memories. As the devices with lower battery life and supplied low memory become widespread, encryption key management problems start to emerge. This may lead to a high possibility of an attack on the encryption key and break the encryption system. In this context, this paper proposes a novel hashing- based approach for key generation in lightweight cryptography. The approach involves generating significant hash digits from a seed input to the hashing system, and then using hash code slicing to select different characters and numbers that can be used as a seed for the key generating system. The practical results, which demonstrate that a small seed length can effectively generate a large key length, are a testament to the effectiveness of the proposed approach. These results can be used in the encryption process to reduce the initial total bits, thereby enhancing the efficiency of the system. © 2024 IEEE.

关键词： Cryptographic Algorithms Encryption Key Management Hashing Techniques IoT Security Lightweight Cryptography

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Robust Prediction of the Bandwidth of Metamaterial Antenna Using Deep Learning

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computers, Materials & Continua 2022年第8期72卷 2305-2321页

作者： Abdelaziz A.Abdelhamid Sultan R.Alotaibi Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Computer Science College of Science and Human StudiesShaqra UniversitySaudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityEgypt

The design ofmicrostrip antennas is a complex and time-consuming process,especially the step of searching for the best design ***,the performance ofmicrostrip antennas can be improved usingmetamaterial,which results in a new class of antennas called metamaterial *** parameters affect the radiation loss and quality factor of this class of antennas,such as the antenna ***,the optimal values of the design parameters of metamaterial antennas can be predicted using machine learning,which presents a better alternative to simulation tools and trialand-error ***,the prediction accuracy depends heavily on the quality of the machine learning *** this paper,and benefiting from the current advances in deep learning,we propose a deep network architecture to predict the bandwidth of metamaterial *** results show that the proposed deep network could accurately predict the optimal values of the antenna bandwidth with a tiny value of mean-square error(MSE).In addition,the proposed model is comparedwith current competing approaches that are based on support vector machines,multi-layer perceptron,K-nearest neighbors,and ensemble *** results show that the proposed model is better than the other approaches and can predict antenna bandwidth more accurately.

关键词： Metamaterial antenna deep learning bandwidth prediction regression models

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Electrohysterogram Data Augmentation Using Generative Adversarial Network for Pregnancy Outcome Prediction

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IEEE Access 2025年 13卷 92468-92482页

作者： Cheema, Muhammad Omar Din, Zia Mohy Ud Imran, Azhar Khan, Fawad Salam Al-Rawi, Mahmood Basil A. El-Meligy, Mohammed Gul, Jahan Zeb Air University Department of Biomedical Engineering Islamabad44000 Pakistan Air University Faculty of Computing & AI Department of Creative Technologies Islamabad44000 Pakistan King Saud University College of Applied Medical Sciences Department of Optometry Riyadh Saudi Arabia Amman Arab University College of Computer Science and Informatics Amman Jordan Maynooth University Department of Electronic Engineering Ireland

There are many difficulties in managing and detecting preterm pregnancies, especially in the early stages. Analyzing electrohysterogram data, which show the electrical activity of uterine muscles, is a promising non-invasive method for classifying term and preterm pregnancies. However, the effectiveness of machine learning classifiers is hampered by the inherent class imbalance in datasets. In order to overcome this constraint, this study uses a Generative Adversarial Network to produce high-quality synthetic EHG data while maintaining the statistical properties of actual signals. By successfully reducing class imbalance, the enhanced dataset makes it possible for machine learning models to be trained with resilience. Real and synthetic features did not differ significantly, according to statistical validation using a t-test (p > 0.05). With this method, the Random Forest classifier obtained a ROC-AUC of 0.99 and a 10-fold cross-validation accuracy of 98%. The suggested GAN-based method’s simplicity and efficacy demonstrate its promise for resolving class imbalance and developing reliable classification systems for diagnostic applications. © 2013 IEEE.

关键词： Generative adversarial networks

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An Intelligent Secure Adversarial Examples Detection Scheme in Heterogeneous Complex Environments

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computers, Materials & Continua 2023年第9期76卷 3859-3876页

作者： Weizheng Wang Xiangqi Wang Xianmin Pan Xingxing Gong Jian Liang Pradip Kumar Sharma Osama Alfarraj Wael Said College of Information Science and Engineering HunanWomen’s UniversityChangsha410138China School of Mathematics and Statistics Hunan First Normal UniversityChangsha410138China School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410114China Department of Computing Science University of AberdeenAberdeenAB243FXUK Department of Computer Science Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Computer Science Faculty of Computers and InformaticsZagazig UniversityZagazig44511Egypt

Image-denoising techniques are widely used to defend against Adversarial Examples(AEs).However,denoising alone cannot completely eliminate adversarial *** remaining perturbations tend to amplify as they propagate through deeper layers of the network,leading to ***,image denoising compromises the classification accuracy of original *** address these challenges in AE defense through image denoising,this paper proposes a novel AE detection *** proposed technique combines multiple traditional image-denoising algorithms and Convolutional Neural Network(CNN)network *** used detector model integrates the classification results of different models as the input to the detector and calculates the final output of the detector based on a machine-learning voting *** analyzing the discrepancy between predictions made by the model on original examples and denoised examples,AEs are detected *** technique reduces computational overhead without modifying the model structure or parameters,effectively avoiding the error amplification caused by *** proposed approach demonstrates excellent detection performance against mainstream AE *** results show outstanding detection performance in well-known AE attacks,including Fast Gradient Sign Method(FGSM),Basic Iteration Method(BIM),DeepFool,and Carlini&Wagner(C&W),achieving a 94%success rate in FGSM detection,while only reducing the accuracy of clean examples by 4%.

关键词： Deep neural networks adversarial example image denoising adversarial example detection machine learning adversarial attack

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Lightweight image super-resolution reconstruction based on mixed attention and global inductive bias network

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Multimedia Tools and Applications 2024年 1-17页

作者： Cai, Yuxi Wen, Xiaopeng Wang, Tongguan College of Computer Science Sichuan University Chengdu China Department of Electronic Information and Electrical Engineering Dalian University of Technology Dalian China College of Informatics Huazhong Agricultural University Wuhan China

Convolutional neural network has significantly advanced the field of image super-resolution reconstruction in recent years. The insufficient ability to model global information of hierarchical features, incomplete attention to information, and excessive parameters make some based on convolutional methods face challenges. To address these issues, this paper designs the lightweight image super-resolution reconstruction based on Mixed Attention and Global Inductive bias Network(MAGIN), which directly captures the global information of phased features from the horizontal and vertical directions respectively to produce global inductive bias information. Furthermore, the global spatial attention block is proposed, which can realize the perception of global feature space without redundant operation. To realize more comprehensive attention for features, a mixed attention block arises, which enables the network to attend to features across different dimensions: channel, spatial, and pixel levels. Extensive experimental results demonstrate that the MAGIN delivers more satisfactory results than some other advanced lightweight reconstruction algorithms on several standard test datasets under the condition of considering the network’s performance, parameter and computational complexity. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image reconstruction

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