The document describes research on detecting phishing websites using machine learning techniques. It provides background on phishing and how machine learning can be used to identify fraudulent websites. The researchers evaluated several algorithms (AdaBoost Classifier, XGBoost Classifier, Random Forest Classifier, Gradient Boosting Classifier, and Support Vector Machine) on a dataset of website attributes. Their results found that the Random Forest Classifier achieved the highest accuracy in determining whether a website was legitimate or a phishing site. The document discusses each algorithm and provides examples of the system's user interface and outputs classifying URLs. It concludes the Random Forest Classifier is effective for this phishing detection task and outlines opportunities for future work improving and expanding the approach.
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This document presents research on using the XGBoost (Extreme Gradient Boosting) machine learning algorithm to detect phishing websites. The researchers collected a dataset from Kaggle containing URL features and used it to train and test an XGBoost model. They found that the XGBoost model was able to accurately predict whether a URL led to a phishing website or legitimate website, achieving 86.4% accuracy according to the confusion matrix. The researchers concluded that XGBoost is a robust and efficient approach for phishing website detection due to its ability to generate highly accurate results with low bias and variance from the ensemble of decision trees.
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This document describes a Chrome browser extension that was developed to detect phishing websites using machine learning. The extension extracts features from URLs and classifies them as legitimate or phishing using a Random Forest classifier trained on a dataset of URLs. A user interface was designed for the extension using HTML, CSS and JavaScript. When a user visits a website, the extension automatically extracts 16 features from the URL and page content and inputs them into the Random Forest model to determine if the site is phishing or legitimate. The model was trained on a dataset of over 11,000 URLs labeled as phishing or legitimate. Evaluation of the model showed it achieved high accuracy in detecting phishing URLs. The goal of the extension is to help protect users from revealing
IRJET- Phishing Website Detection based on Machine LearningIRJET Journal
This document proposes a machine learning model to detect phishing websites. It discusses how data mining algorithms can be used to classify websites as legitimate or phishing based on their characteristics. The proposed system aims to optimize detection by analyzing URL features, checking blacklists, and using a WHOIS database. It claims this method could decrease the error rate of existing detection systems by 30% and provide a more efficient way to identify phishing websites.
This document presents a proposed system for detecting phishing websites using a Chrome extension. The system compares URLs to entries in two databases - the Phishtank database of known phishing sites, and a local IndexedDB of frequently visited sites. If a match is found in either database, the Chrome extension will flag the site as potentially malicious by changing color. The system was tested on 53 URLs, achieving an accuracy of 92.45% at detecting phishing sites. The proposed system aims to alert users to phishing sites and protect them from disclosing sensitive information to attackers.
IRJET - An Automated System for Detection of Social Engineering Phishing Atta...IRJET Journal
1) The document presents a machine learning approach to detect phishing URLs using logistic regression. It trains a logistic regression model on a dataset of 420,467 URLs that have been classified as either phishing or legitimate.
2) It preprocesses the URLs using tokenization before training the logistic regression model. The trained model is able to classify new URLs with 96% accuracy as either phishing or legitimate based on the URL features.
3) The proposed approach provides an automated way to detect phishing URLs in real-time and help prevent phishing attacks. Future work could involve developing a browser extension using this approach and increasing the dataset size for higher accuracy.
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This document describes a research project that aims to implement machine learning techniques to detect phishing websites. The researchers plan to test algorithms like logistic regression, SVM, decision trees and neural networks on a dataset of phishing links. They will evaluate the performance of these algorithms and develop a browser plugin using the best model. This plugin will detect malicious URLs and protect users from phishing attacks. The document provides background on phishing and outlines the proposed approach, dataset, algorithms to be tested, planned Chrome extension implementation, and expected results sections of the project.
Phishing Website Detection Using Machine LearningIRJET Journal
This document describes a study that used machine learning to detect phishing websites. The researchers used the XGBoost algorithm and analyzed 48 features of websites to train a classifier. Their goal was to develop a browser extension that could automatically detect phishing sites and notify users. They achieved 99% accuracy in classifying websites as phishing or legitimate. The document provides background on phishing and reviews related literature using machine learning for detection. It then describes the researchers' proposed solution, methodology, and the scope for improving phishing detection given increased online activity during the COVID-19 pandemic.
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This document discusses a machine learning approach to detecting phishing attacks based on URL analysis. It begins with an abstract that outlines using machine learning classifiers to analyze URL features to determine if a webpage is legitimate or a phishing attack. It then provides background on phishing attacks and discusses previous research on detection techniques, including whitelist, blacklist, content-based, and visual similarity approaches. The document focuses on using a URL-based detection technique with machine learning. It evaluates popular classifiers like SVM, Naive Bayes, and Decision Trees on their accuracy in detecting phishing URLs based on analyzed features like length, special characters, and IP addresses.
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This document presents research on using the XGBoost (Extreme Gradient Boosting) machine learning algorithm to detect phishing websites. The researchers collected a dataset from Kaggle containing URL features and used it to train and test an XGBoost model. They found that the XGBoost model was able to accurately predict whether a URL led to a phishing website or legitimate website, achieving 86.4% accuracy according to the confusion matrix. The researchers concluded that XGBoost is a robust and efficient approach for phishing website detection due to its ability to generate highly accurate results with low bias and variance from the ensemble of decision trees.
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This document describes a Chrome browser extension that was developed to detect phishing websites using machine learning. The extension extracts features from URLs and classifies them as legitimate or phishing using a Random Forest classifier trained on a dataset of URLs. A user interface was designed for the extension using HTML, CSS and JavaScript. When a user visits a website, the extension automatically extracts 16 features from the URL and page content and inputs them into the Random Forest model to determine if the site is phishing or legitimate. The model was trained on a dataset of over 11,000 URLs labeled as phishing or legitimate. Evaluation of the model showed it achieved high accuracy in detecting phishing URLs. The goal of the extension is to help protect users from revealing
IRJET- Phishing Website Detection based on Machine LearningIRJET Journal
This document proposes a machine learning model to detect phishing websites. It discusses how data mining algorithms can be used to classify websites as legitimate or phishing based on their characteristics. The proposed system aims to optimize detection by analyzing URL features, checking blacklists, and using a WHOIS database. It claims this method could decrease the error rate of existing detection systems by 30% and provide a more efficient way to identify phishing websites.
This document presents a proposed system for detecting phishing websites using a Chrome extension. The system compares URLs to entries in two databases - the Phishtank database of known phishing sites, and a local IndexedDB of frequently visited sites. If a match is found in either database, the Chrome extension will flag the site as potentially malicious by changing color. The system was tested on 53 URLs, achieving an accuracy of 92.45% at detecting phishing sites. The proposed system aims to alert users to phishing sites and protect them from disclosing sensitive information to attackers.
IRJET - An Automated System for Detection of Social Engineering Phishing Atta...IRJET Journal
1) The document presents a machine learning approach to detect phishing URLs using logistic regression. It trains a logistic regression model on a dataset of 420,467 URLs that have been classified as either phishing or legitimate.
2) It preprocesses the URLs using tokenization before training the logistic regression model. The trained model is able to classify new URLs with 96% accuracy as either phishing or legitimate based on the URL features.
3) The proposed approach provides an automated way to detect phishing URLs in real-time and help prevent phishing attacks. Future work could involve developing a browser extension using this approach and increasing the dataset size for higher accuracy.
IRJET- Detecting Phishing Websites using Machine LearningIRJET Journal
This document describes a research project that aims to implement machine learning techniques to detect phishing websites. The researchers plan to test algorithms like logistic regression, SVM, decision trees and neural networks on a dataset of phishing links. They will evaluate the performance of these algorithms and develop a browser plugin using the best model. This plugin will detect malicious URLs and protect users from phishing attacks. The document provides background on phishing and outlines the proposed approach, dataset, algorithms to be tested, planned Chrome extension implementation, and expected results sections of the project.
Phishing Website Detection Using Machine LearningIRJET Journal
This document describes a study that used machine learning to detect phishing websites. The researchers used the XGBoost algorithm and analyzed 48 features of websites to train a classifier. Their goal was to develop a browser extension that could automatically detect phishing sites and notify users. They achieved 99% accuracy in classifying websites as phishing or legitimate. The document provides background on phishing and reviews related literature using machine learning for detection. It then describes the researchers' proposed solution, methodology, and the scope for improving phishing detection given increased online activity during the COVID-19 pandemic.
IRJET - Phishing Attack Detection and Prevention using Linkguard AlgorithmIRJET Journal
This document discusses a machine learning approach to detecting phishing attacks based on URL analysis. It begins with an abstract that outlines using machine learning classifiers to analyze URL features to determine if a webpage is legitimate or a phishing attack. It then provides background on phishing attacks and discusses previous research on detection techniques, including whitelist, blacklist, content-based, and visual similarity approaches. The document focuses on using a URL-based detection technique with machine learning. It evaluates popular classifiers like SVM, Naive Bayes, and Decision Trees on their accuracy in detecting phishing URLs based on analyzed features like length, special characters, and IP addresses.
This document discusses techniques for detecting phishing websites. It proposes using web crawling and YARA rules to analyze website URLs and content to classify websites as phishing or non-phishing. Specifically, it involves capturing the URL, analyzing features like domain length and global rank to generate a score, then using a web crawler and YARA rules to analyze website text and detect if the content is irrelevant or malicious. The goal is to develop an extension that can act as middleware between users and malicious websites to reduce users' risk of exposure while allowing safe browsing.
IRJET- Preventing Phishing Attack using Evolutionary AlgorithmsIRJET Journal
This document proposes using evolutionary algorithms and support vector machine (SVM) classification to detect phishing attacks more effectively than existing techniques. It summarizes previous approaches like blacklisting, neuro-fuzzy systems, and discusses their limitations in terms of feature extraction time and training requirements. The proposed system extracts URL features, trains an SVM model on a dataset of phishing and legitimate sites, then classifies new URLs based on a threshold derived from their feature values. It is claimed this approach reduces time consumption, increases processing speed, and achieves over 99% accuracy in detecting phishing sites.
PHISHING URL DETECTION USING MACHINE LEARNINGIRJET Journal
This document proposes a machine learning model to detect phishing URLs. The model has three phases: 1) Parsing phase uses attribute selection algorithms to select the most informative features from a dataset of URLs. 2) Heuristic classification phase splits the data into training and test sets. 3) Performance analysis phase evaluates the model using metrics like ROC curve. A random forest algorithm is used for classification to improve over prior methods. The model aims to efficiently detect phishing URLs from legitimate ones.
IRJET- Detecting the Phishing Websites using Enhance Secure AlgorithmIRJET Journal
This document discusses detecting phishing websites using an enhanced secure algorithm. It begins by defining phishing attacks and how they are used to steal personal information from users. It then discusses how current techniques are not fully effective at stopping sophisticated phishing attacks. The proposed methodology checks for features of phishing websites, especially in URLs and domain names, to identify fake websites. Some features checked include IP addresses, long URLs, prefixes/suffixes, and symbols. Future work could involve updating datasets, detecting other attacks, and improving accuracy and efficiency. In conclusion, education is important to help users identify phishing attacks, as technical solutions are still limited.
Phishing Website Detection Using Machine LearningIRJET Journal
This document describes research into detecting phishing websites using machine learning. It discusses how phishing websites trick users into providing sensitive information by posing as legitimate websites. The researchers collected a dataset of real URLs labeled as legitimate or phishing and preprocessed the data. They then trained several machine learning models using URL-based features like length of hostname, use of URL shortening services, presence of @ symbols or IP addresses. The goal is to identify the most effective model for classifying URLs based on precision, false positive and false negative rates to help detect phishing websites in real-time.
IRJET- Advanced Phishing Identification Technique using Machine LearningIRJET Journal
1) The document describes a machine learning technique to identify phishing websites using a random forest algorithm.
2) It trains the random forest classifier on extracted URL features from a dataset of known phishing and legitimate websites.
3) The trained model is then used in a Chrome browser extension to analyze URLs and classify them as phishing or legitimate in real-time as users browse the web.
1) The document presents a system for detecting malicious links using machine learning techniques. It aims to improve the effectiveness of classifiers for identifying dangerous websites.
2) The system utilizes logistic regression as a supervised machine learning algorithm to categorize URLs based on their linguistic properties and behaviors. New attributes are extracted from URLs to train the classifier.
3) The proposed approach aims to identify malicious URLs based solely on their structure and properties, without relying on webpage content. This could lead to significant resource savings and a safer browsing experience for users.
In spite of the development of aversion strategies, phishing remains an essential risk even after the
primary countermeasures and in view of receptive URL blacklisting. This strategy is insufficient because of the
short lifetime of phishing websites. In order to overcome this problem, developing a real-time phishing website
detection method is an effective solution. This research introduces the PrePhish algorithm which is an automated
machine learning approach to analyze phishing and non-phishing URL to produce reliable result. It represents that
phishing URLs typically have couple of connections between the part of the registered domain level and the path
or query level URL. Using these connections URL is characterized by inter-relatedness and it estimates using
features mined from attributes. These features are then used in machine learning technique to detect phishing
URLs from a real dataset. The classification of phishing and non-phishing website has been implemented by
finding the range value and threshold value for each attribute using decision making classification. This method is
also evaluated in Matlab using three major classifiers SVM, Random Forest and Naive Bayes to find how it works
on the dataset assessed
IRJET- Noisy Content Detection on Web Data using Machine LearningIRJET Journal
The document presents a study on detecting noisy content on web data using machine learning techniques. It aims to identify and filter out advertisements, irrelevant data and other noisy content when users retrieve information from websites. The study uses algorithms like support vector machines, artificial neural networks, decision trees and k-nearest neighbors to classify web content as noisy or not. A proposed methodology involves training models using these machine learning algorithms and evaluating their performance for noisy content detection on web data.
Detecting Phishing Websites Using Machine LearningIRJET Journal
1. The document proposes a system to detect phishing websites in real-time using machine learning. It trains a random forest classifier on a dataset of URLs and associated features to classify websites as legitimate or phishing.
2. The system collects URL and page content features from a user's browser and sends them to a cloud-based random forest model. The model was trained on over 40,000 URLs and achieved 97.36% accuracy at detecting phishing sites.
3. The proposed system provides advantages like real-time detection, using a large dataset for training, detecting new phishing sites, and independence from third-party services. It aims to protect users by identifying phishing sites before sensitive information is entered.
Phishing Website Detection using Classification AlgorithmsIRJET Journal
This document discusses using machine learning algorithms to classify phishing websites. It begins with background on phishing and then discusses prior research applying algorithms like random forest, decision trees, SVM and KNN to detect phishing websites. The paper aims to address phishing website classification using various classifiers and ensemble learning approaches. It tests classifiers like random forest, decision tree, KNN, AdaBoost and GradientBoost on a phishing testing dataset and evaluates performance using metrics like accuracy, f1-score, precision and recall. The proposed approach achieves 97% accuracy in classifying phishing websites according to experimental results.
A Hybrid Approach For Phishing Website Detection Using Machine Learning.vivatechijri
In this technical age there are many ways where an attacker can get access to people’s sensitive information illegitimately. One of the ways is Phishing, Phishing is an activity of misleading people into giving their sensitive information on fraud websites that lookalike to the real website. The phishers aim is to steal personal information, bank details etc. Day by day it’s getting more and more risky to enter your personal information on websites fearing that it might be a phishing attack and can steal your sensitive information. That’s why phishing website detection is necessary to alert the user and block the website. An automated detection of phishing attack is necessary one of which is machine learning. Machine Learning is one of the efficient techniques to detect phishing attack as it removes drawback of existing approaches. Efficient machine learning model with content based approach proves very effective to detect phishing websites.
Our proposed system uses Hybrid approach which combines machine learning based method and content based method. The URL based features will be extracted and passed to machine learning model and in content based approach, TF-IDF algorithm will detect a phishing website by using the top keywords of a web page. This hybrid approach is used to achieve highly efficient result. Finally, our system will notify and alert user if the website is Phishing or Legitimate.
IRJET- Machine Learning Techniques to Seek Out Malicious WebsitesIRJET Journal
This document presents a study on using an ensemble machine learning model to detect phishing websites. The study collected features from URLs to train random forest, logistic regression, and support vector machine classifiers. It then used an ensemble approach that combines the results of these classifiers to provide more accurate phishing detection than any single model. The ensemble model analyzed lexical and URL-based features. The study found that the ensemble approach achieved higher accuracy for phishing website detection compared to existing methods. It aims to develop an intelligent system for detecting phishing websites in real-time using machine learning algorithms.
This document discusses the development of a machine learning model to accurately detect phishing websites in real-time. It begins with an introduction to the problem of phishing and the need for reliable phishing detection systems. It then discusses using supervised machine learning, specifically logistic regression, to classify websites as legitimate or phishing based on discriminatory features. The goal is to determine an optimal feature combination to train a classifier with high performance at detecting phishing sites. Previous literature on phishing detection is reviewed, including techniques using fuzzy rough set theory and a three-tiered approach using web crawler traffic data, content analysis, and URL analysis.
Phishing is a method of trying to gather personal information using deceptive emails and website it is a classic example for cybercrime. For example we may receive an email from our bank or trusted company and its asks you for information which may look real but it’s designed to fool you into handing over crucial information this is a scam and we need to avoid it. There are many techniques to detect it but Machine learning is the most effective technique for detecting these types of attacks and it can detect the drawbacks of other phishing techniques. This paper focuses on discerning the many features that discriminate between authorized and phishing URLs. The main aim of this paper is to develop a model as a solution for detecting malicious websites. By detecting a large number of phishing hosts, this model can manage 80 95 percent accuracy while retaining a modest false positive rate. Implementation will be carried out on the datasets of 4,20,465 websites containing both phi shy sites and authorized sites. Ultimately, the findings will show us the higher precision detection rate algorithm, which will classify phishing or legitimate websites more correctly. Dirash A R | Mehtab Mehdi "Phishing URL Detection" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38109.pdf Paper URL : https://www.ijtsrd.com/computer-science/other/38109/phishing-url-detection/dirash-a-r
IRJET - Detection and Prevention of Phishing Websites using Machine Learning ...IRJET Journal
This document discusses approaches for detecting phishing websites using machine learning. It describes three main approaches: 1) analyzing features of the URL, 2) checking the legitimacy of the website by examining the hosting and management details, and 3) using visual appearance analysis to check the genuineness of the website. It then proposes a hybrid approach that uses blacklist/whitelist screening, heuristic analysis of website features, and visual similarity comparisons to flag potential phishing sites.
This document summarizes a research paper that proposes a phishing detector plugin called PHISCAN that uses machine learning to detect phishing websites. The plugin is developed for the Chrome browser using JavaScript and HTML. It extracts features from URLs to train classifiers like random forest that can accurately classify URLs as phishing or benign in less than a second while maintaining user privacy. The paper conducts a literature review of existing phishing detection systems and techniques using blacklists, heuristics, or machine learning. It motivates the need for the proposed plugin by discussing the increasing prevalence and sophistication of phishing attacks.
This document proposes an Offtech Tool and End URL Finder to determine where links lead before clicking on them. It summarizes that hackers can steal data or damage websites through malicious links. The tool was created using the Python Flask framework to independently run on various operating systems. It follows the URL route of a link to display the full, redirected URL to avoid theft of personal information. Testing showed the tool successfully detected 98.5% of links intended to steal sensitive data by analyzing URL properties like length and IP addresses.
Click Fraud Detection Of Advertisements using Machine LearningIRJET Journal
This document presents a study on detecting click fraud in online advertisements using machine learning algorithms. The researchers collected data on ad clicks from a large Chinese data platform handling billions of daily clicks, of which 90% are estimated to be fraudulent. They propose using ensemble algorithms like XGBoost and AdaBoost with feature engineering to classify clicks as valid or fraudulent. Several related works applying machine learning for click fraud detection are reviewed. The proposed system architecture involves data preprocessing, model training and testing, and performance evaluation. XGBoost achieved the best performance with 96.2% accuracy for click fraud detection on this dataset.
Phishing Detection using Decision Tree ModelIRJET Journal
This document summarizes a research paper that proposes detecting phishing websites using a decision tree machine learning model. It begins by defining phishing attacks and their goal of stealing user data. It then describes extracting features from URLs to train a decision tree classifier, which achieved 95% accuracy in distinguishing real from phishing websites. The model was tested on a dataset of over 25,000 URLs. When users input a URL, the model classifies it as real or phishing to help protect users from fraudulent sites.
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
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3) The trained model is then used in a Chrome browser extension to analyze URLs and classify them as phishing or legitimate in real-time as users browse the web.
1) The document presents a system for detecting malicious links using machine learning techniques. It aims to improve the effectiveness of classifiers for identifying dangerous websites.
2) The system utilizes logistic regression as a supervised machine learning algorithm to categorize URLs based on their linguistic properties and behaviors. New attributes are extracted from URLs to train the classifier.
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In spite of the development of aversion strategies, phishing remains an essential risk even after the
primary countermeasures and in view of receptive URL blacklisting. This strategy is insufficient because of the
short lifetime of phishing websites. In order to overcome this problem, developing a real-time phishing website
detection method is an effective solution. This research introduces the PrePhish algorithm which is an automated
machine learning approach to analyze phishing and non-phishing URL to produce reliable result. It represents that
phishing URLs typically have couple of connections between the part of the registered domain level and the path
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features mined from attributes. These features are then used in machine learning technique to detect phishing
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In this technical age there are many ways where an attacker can get access to people’s sensitive information illegitimately. One of the ways is Phishing, Phishing is an activity of misleading people into giving their sensitive information on fraud websites that lookalike to the real website. The phishers aim is to steal personal information, bank details etc. Day by day it’s getting more and more risky to enter your personal information on websites fearing that it might be a phishing attack and can steal your sensitive information. That’s why phishing website detection is necessary to alert the user and block the website. An automated detection of phishing attack is necessary one of which is machine learning. Machine Learning is one of the efficient techniques to detect phishing attack as it removes drawback of existing approaches. Efficient machine learning model with content based approach proves very effective to detect phishing websites.
Our proposed system uses Hybrid approach which combines machine learning based method and content based method. The URL based features will be extracted and passed to machine learning model and in content based approach, TF-IDF algorithm will detect a phishing website by using the top keywords of a web page. This hybrid approach is used to achieve highly efficient result. Finally, our system will notify and alert user if the website is Phishing or Legitimate.
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This document discusses the development of a machine learning model to accurately detect phishing websites in real-time. It begins with an introduction to the problem of phishing and the need for reliable phishing detection systems. It then discusses using supervised machine learning, specifically logistic regression, to classify websites as legitimate or phishing based on discriminatory features. The goal is to determine an optimal feature combination to train a classifier with high performance at detecting phishing sites. Previous literature on phishing detection is reviewed, including techniques using fuzzy rough set theory and a three-tiered approach using web crawler traffic data, content analysis, and URL analysis.
Phishing is a method of trying to gather personal information using deceptive emails and website it is a classic example for cybercrime. For example we may receive an email from our bank or trusted company and its asks you for information which may look real but it’s designed to fool you into handing over crucial information this is a scam and we need to avoid it. There are many techniques to detect it but Machine learning is the most effective technique for detecting these types of attacks and it can detect the drawbacks of other phishing techniques. This paper focuses on discerning the many features that discriminate between authorized and phishing URLs. The main aim of this paper is to develop a model as a solution for detecting malicious websites. By detecting a large number of phishing hosts, this model can manage 80 95 percent accuracy while retaining a modest false positive rate. Implementation will be carried out on the datasets of 4,20,465 websites containing both phi shy sites and authorized sites. Ultimately, the findings will show us the higher precision detection rate algorithm, which will classify phishing or legitimate websites more correctly. Dirash A R | Mehtab Mehdi "Phishing URL Detection" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38109.pdf Paper URL : https://www.ijtsrd.com/computer-science/other/38109/phishing-url-detection/dirash-a-r
IRJET - Detection and Prevention of Phishing Websites using Machine Learning ...IRJET Journal
This document discusses approaches for detecting phishing websites using machine learning. It describes three main approaches: 1) analyzing features of the URL, 2) checking the legitimacy of the website by examining the hosting and management details, and 3) using visual appearance analysis to check the genuineness of the website. It then proposes a hybrid approach that uses blacklist/whitelist screening, heuristic analysis of website features, and visual similarity comparisons to flag potential phishing sites.
This document summarizes a research paper that proposes a phishing detector plugin called PHISCAN that uses machine learning to detect phishing websites. The plugin is developed for the Chrome browser using JavaScript and HTML. It extracts features from URLs to train classifiers like random forest that can accurately classify URLs as phishing or benign in less than a second while maintaining user privacy. The paper conducts a literature review of existing phishing detection systems and techniques using blacklists, heuristics, or machine learning. It motivates the need for the proposed plugin by discussing the increasing prevalence and sophistication of phishing attacks.
This document proposes an Offtech Tool and End URL Finder to determine where links lead before clicking on them. It summarizes that hackers can steal data or damage websites through malicious links. The tool was created using the Python Flask framework to independently run on various operating systems. It follows the URL route of a link to display the full, redirected URL to avoid theft of personal information. Testing showed the tool successfully detected 98.5% of links intended to steal sensitive data by analyzing URL properties like length and IP addresses.
Click Fraud Detection Of Advertisements using Machine LearningIRJET Journal
This document presents a study on detecting click fraud in online advertisements using machine learning algorithms. The researchers collected data on ad clicks from a large Chinese data platform handling billions of daily clicks, of which 90% are estimated to be fraudulent. They propose using ensemble algorithms like XGBoost and AdaBoost with feature engineering to classify clicks as valid or fraudulent. Several related works applying machine learning for click fraud detection are reviewed. The proposed system architecture involves data preprocessing, model training and testing, and performance evaluation. XGBoost achieved the best performance with 96.2% accuracy for click fraud detection on this dataset.
Phishing Detection using Decision Tree ModelIRJET Journal
This document summarizes a research paper that proposes detecting phishing websites using a decision tree machine learning model. It begins by defining phishing attacks and their goal of stealing user data. It then describes extracting features from URLs to train a decision tree classifier, which achieved 95% accuracy in distinguishing real from phishing websites. The model was tested on a dataset of over 25,000 URLs. When users input a URL, the model classifies it as real or phishing to help protect users from fraudulent sites.
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