检索结果-南通市图书馆

URL phishing Detection Using Particle Swarm Optimization and Data Mining

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2022年第12期73卷 5625-5640页

作者： Saeed M.Alshahrani Nayyar Ahmed Khan Jameel Almalki Waleed Al Shehri College of Computing and IT-Shaqra University Shaqra155572Saudi Arabia Department of Computer Science College of Computer in Al-LeithUmm Al-Qura UniversityMakkahSaudi Arabia

The continuous destruction and frauds prevailing due to phishing URLs make it an indispensable area for *** techniques are adopted in the detection process,including neural networks,machine learning,or hybrid techniques.A novel detection model is proposed that uses data mining with the Particle Swarm Optimization technique(PSO)to increase and empower the method of detecting phishing *** selection based on various techniques to identify the phishing candidates from the URL is *** this approach,the features mined from the URL are extracted using data mining *** features are selected on the basis of URL *** classification of these features identified by the data mining rules is done using PSO *** selection of features with PSO optimization makes it possible to identify phishing *** a large number of rule identifiers,the true positive rate for the identification of phishing URLs is maximized in this *** experiments show that feature selection using data mining and particle swarm optimization helps tremendously identify the phishing URLs based on the structure of the URL ***,it can minimize processing time for identifying the phishing website ***,the approach can be beneficial to identify suchURLs over the existing contemporary detecting models proposed before.

关键词： phishing particle swarm optimization feature selection data mining classification cloud application

Hunger Search Optimization with Hybrid Deep Learning Enabled phishing Detection and Classification Model

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2022年第12期73卷 6425-6441页

作者： Hadil Shaiba Jaber S.Alzahrani Majdy M.Eltahir Radwa Marzouk Heba Mohsen Manar Ahmed Hamza Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

phishing is one of the simplest ways in cybercrime to hack the reliable data of users such as passwords,account identifiers,bank details,*** general,these kinds of cyberattacks are made at users through phone calls,emails,or instant *** anti-phishing techniques,currently under use,aremainly based on source code features that need to scrape the webpage *** third party services,these techniques check the classification procedure of phishing Uniform Resource Locators(URLs).Even thoughMachine Learning(ML)techniques have been lately utilized in the identification of phishing,they still need to undergo feature engineering since the techniques are not well-versed in identifying phishing *** tremendous growth and evolution of Deep Learning(DL)techniques paved the way for increasing the accuracy of classification *** this background,the current research article presents a Hunger Search Optimization with Hybrid Deep Learning enabled phishing Detection and Classification(HSOHDL-PDC)*** presented HSOHDL-PDC model focuses on effective recognition and classification of phishing based on website *** addition,SOHDL-PDC model uses character-level embedding instead of word-level embedding since the URLs generally utilize words with no ***,a hybrid Convolutional Neural Network-Long Short Term Memory(HCNN-LSTM)technique is also applied for identification and classification of *** hyperparameters involved in HCNN-LSTM model are optimized with the help of HSO algorithm which in turn produced improved *** performance of the proposed HSOHDL-PDC model was validated using different datasets and the outcomes confirmed the supremacy of the proposed model over other recent approaches.

关键词： Uniform resource locators phishing cyberattacks machine learning deep learning hyperparameter optimization

A systematic review:Detecting phishing websites using data mining models

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Intelligent and Converged Networks 2023年第4期4卷 326-341页

作者： Dina Jibat Sarah Jamjoom Qasem Abu Al-Haija Abdallah Qusef the Department of Business Intelligence Technology Princess Sumaya University for TechnologyAmman 11941Jordan the Department of Cybersecurity Princess Sumaya University for TechnologyAmman 11941Jordan the Department of Software Engineering Princess Sumaya University for TechnologyAmman 11941Jordan

As internet technology use is on the rise globally,phishing constitutes a considerable share of the threats that may attack individuals and organizations,leading to significant losses from personal and confidential information to substantial financial ***,much research has been dedicated in recent years to developing effective and robust mechanisms to enhance the ability to trace illegitimate web pages and to distinguish them from non-phishing sites as accurately as *** to conclude whether a universally accepted model can detect phishing attempts with 100%accuracy,we conduct a systematic review of research carried out in 2018-2021 published in well-known journals published by Elsevier,IEEE,Springer,and *** researchers studied different Data Mining(DM)algorithms,some of which created a whole new model,while others compared the performance of several *** studies combined two or more algorithms to enhance the detection *** reveal that while most algorithms achieve accuracies higher than 90%,only some specific models can achieve 100%accurate results.

关键词： phishing data mining machine learning algorithm classification

Preventing phishing Attack on Voting System Using Visual Cryptography

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Computer and Communications 2022年第10期10卷 149-161页

作者： Ahood Alotaibi Lama Alhubaidi Alghala Alyami Leena Marghalani Bashayer Alharbi Naya Nagy College of Cybersecurity and Digital Forensics Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia

phishing is one of the most common social engineering attacks that users over the internet fall for. An example is voting systems, and because such systems should be accurate and error free, phishing prevention techniques are crucial. Visual Cryptography (VC) is utilized for efficient voting system authentication to cast votes. VC is one of the most secure approaches for privacy protection as it ensures the confidentiality of the voting system. This paper discusses proposed phishing prevention methods and compares different proposed methods.

关键词： Remote Voting System (RVS) Voting System (VS) Shares Ballots Authentication Visual Cryptography phishing Captcha

phishing行为及网络金融机构应对策略的博弈分析(英文)

维普期刊数据库博看期刊评论

在线全文

学校读者我要写书评

暂无评论

电子科技大学学报 2009年第S1期38卷 37-44页

作者：刘业政丁正平袁雨飞合肥工业大学管理学院合肥230009 The Degroote School of Business Mcmaster University Canada

phishing是近年来新出现的一种网络欺诈,是指欺诈者(Phisher)通过大量发送欺骗性垃圾邮件或采用其他的方式,意图引诱疏于防范的网络用户登陆假冒的知名站点,从而窃取个人敏感信息的一种攻击方式。这种欺诈行为给网络用户尤其是网络金融... 详细信息

phishing是近年来新出现的一种网络欺诈,是指欺诈者(Phisher)通过大量发送欺骗性垃圾邮件或采用其他的方式,意图引诱疏于防范的网络用户登陆假冒的知名站点,从而窃取个人敏感信息的一种攻击方式。这种欺诈行为给网络用户尤其是网络金融机构的用户带来了大量的损失,也给网络金融机构带来了危害。该文在分析Phisher和网络金融机构的损益函数的基础上,建立了它们之间的二阶段动态博弈模型,并通过对纳什均衡的分析,求出了网络金融机构面对phishing欺诈的最优策略。

关键词：基博弈网络金融机构 phishing

phishing Techniques in Mobile Devices

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Journal of Computer and Communications 2018年第2期6卷 27-35页

作者： Belal Amro College of Information Technology Hebron University Hebron Palestine

The rapid evolution in mobile devices and communication technology has increased the number of mobile device users dramatically. The mobile device has replaced many other devices and is used to perform many tasks ranging from establishing a phone call to performing critical and sensitive tasks like money payments. Since the mobile device is accompanying a person most of his time, it is highly probably that it includes personal and sensitive data for that person. The increased use of mobile devices in daily life made mobile systems an excellent target for attacks. One of the most important attacks is phishing attack in which an attacker tries to get the credential of the victim and impersonate him. In this paper, analysis of different types of phishing attacks on mobile devices is provided. Mitigation techniques—anti-phishing techniques—are also analyzed. Assessment of each technique and a summary of its advantages and disadvantages is provided. At the end, important steps to guard against phishing attacks are provided. The aim of the work is to put phishing attacks on mobile systems in light, and to make people aware of these attacks and how to avoid them.

关键词： Malware phishing Anti-phishing Mobile Device Mobile Application Security Privacy

A Multi-Classifier Based Prediction Model for phishing Emails Detection Using Topic Modelling, Named Entity Recognition and Image Processing

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Circuits and Systems 2016年第9期7卷 2507-2520页

作者： C. Emilin Shyni S. Sarju S. Swamynathan Department of Information Technology KCG College of Technology Chennai India Department of Computer Science St. Joseph’s College of Engineering and Technology Kerala India Department of Information Science and Technology Anna University Chennai India

phishing is the act of attempting to steal a user’s financial and personal information, such as credit card numbers and passwords by pretending to be a trustworthy participant, during online communication. Attackers may direct the users to a fake website that could seem legitimate, and then gather useful and confidential information using that site. In order to protect users from Social Engineering techniques such as phishing, various measures have been developed, including improvement of Technical Security. In this paper, we propose a new technique, namely, “A Prediction Model for the Detection of phishing e-mails using Topic Modelling, Named Entity Recognition and Image Processing”. The features extracted are Topic Modelling features, Named Entity features and Structural features. A multi-classifier prediction model is used to detect the phishing mails. Experimental results show that the multi-classification technique outperforms the single-classifier-based prediction techniques. The resultant accuracy of the detection of phishing e-mail is 99% with the highest False Positive Rate being 2.1%.

关键词： phishing Conditional Random Field Classifier Latent Dirichlet Allocation Natural Language Processing Machine Learning Image Segmentation Image Processing

Artificial Neural Network for Websites Classification with phishing Characteristics

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Social Networking 2018年第2期7卷 97-109页

作者： Ricardo Pinto Ferreira Andréa Martiniano Domingos Napolitano Marcio Romero Dacyr Dante De Oliveira Gatto Edquel Bueno Prado Farias Renato José Sassi Graduate Program in Informaticsand Knowledge Management Universidade Nove de Julho S&atilde o Paulo Brazil

Several threats are propagated by malicious websites largely classified as phishing. Its function is important information for users with the purpose of criminal practice. In summary, phishing is a technique used on the Internet by criminals for online fraud. The Artificial Neural Networks (ANN) are computational models inspired by the structure of the brain and aim to simu-late human behavior, such as learning, association, generalization and ab-straction when subjected to training. In this paper, an ANN Multilayer Per-ceptron (MLP) type was applied for websites classification with phishing cha-racteristics. The results obtained encourage the application of an ANN-MLP in the classification of websites with phishing characteristics.

关键词： Artificial Intelligence Artificial Neural Network Pattern Recognition phishing Characteristics Social Engineering

Mobile phishing Attacks and Mitigation Techniques

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Information Security 2015年第3期6卷 206-212页

作者： Hossain Shahriar Tulin Klintic Victor Clincy Kennesaw State University Marietta Georgia USA

Mobile devices have taken an essential role in the portable computer world. Portability, small screen size, and lower cost of production make these devices popular replacements for desktop and laptop computers for many daily tasks, such as surfing on the Internet, playing games, and shopping online. The popularity of mobile devices such as tablets and smart phones has made them a frequent target of traditional web-based attacks, especially phishing. Mobile device-based phishing takes its share of the pie to trick users into entering their credentials in fake websites or fake mobile applications. This paper discusses various phishing attacks using mobile devices followed by some discussion on countermeasures. The discussion is intended to bring more awareness to emerging mobile device-based phishing attacks.

关键词： Mobile Application phishing Smishing Vishing