In the evolving landscape of cyber threats,phishing attacks pose significant challenges,particularly through deceptive webpages designed to extract sensitive information under the guise of legitimacy.Conventional and ...In the evolving landscape of cyber threats,phishing attacks pose significant challenges,particularly through deceptive webpages designed to extract sensitive information under the guise of legitimacy.Conventional and machine learning(ML)-based detection systems struggle to detect phishing websites owing to their constantly changing tactics.Furthermore,newer phishing websites exhibit subtle and expertly concealed indicators that are not readily detectable.Hence,effective detection depends on identifying the most critical features.Traditional feature selection(FS)methods often struggle to enhance ML model performance and instead decrease it.To combat these issues,we propose an innovative method using explainable AI(XAI)to enhance FS in ML models and improve the identification of phishing websites.Specifically,we employ SHapley Additive exPlanations(SHAP)for global perspective and aggregated local interpretable model-agnostic explanations(LIME)to deter-mine specific localized patterns.The proposed SHAP and LIME-aggregated FS(SLA-FS)framework pinpoints the most informative features,enabling more precise,swift,and adaptable phishing detection.Applying this approach to an up-to-date web phishing dataset,we evaluate the performance of three ML models before and after FS to assess their effectiveness.Our findings reveal that random forest(RF),with an accuracy of 97.41%and XGBoost(XGB)at 97.21%significantly benefit from the SLA-FS framework,while k-nearest neighbors lags.Our framework increases the accuracy of RF and XGB by 0.65%and 0.41%,respectively,outperforming traditional filter or wrapper methods and any prior methods evaluated on this dataset,showcasing its potential.展开更多
In order to effectively detect malicious phishing behaviors, a phishing detection method based on the uniform resource locator （URL） features is proposed. First, the method compares the phishing URLs with legal ones...In order to effectively detect malicious phishing behaviors, a phishing detection method based on the uniform resource locator （URL） features is proposed. First, the method compares the phishing URLs with legal ones to extract the features of phishing URLs. Then a machine learning algorithm is applied to obtain the URL classification model from the sample data set training. In order to adapt to the change of a phishing URL, the classification model should be constantly updated according to the new samples. So, an incremental learning algorithm based on the feedback of the original sample data set is designed. The experiments verify that the combination of the URL features extracted in this paper and the support vector machine （SVM） classification algorithm can achieve a high phishing detection accuracy, and the incremental learning algorithm is also effective.展开更多
A phishing detection system, which comprises client-side filtering plug-in, analysis center and protected sites, is proposed. An image-based similarity detection algorithm is conceived to calculate the similarity of t...A phishing detection system, which comprises client-side filtering plug-in, analysis center and protected sites, is proposed. An image-based similarity detection algorithm is conceived to calculate the similarity of two web pages. The web pages are first converted into images, and then divided into sub-images with iterated dividing and shrinking. After that, the attributes of sub-images including color histograms, gray histograms and size parameters are computed to construct the attributed relational graph（ARG）of each page. In order to match two ARGs, the inner earth mover＇s distances（EMD）between every two nodes coming from each ARG respectively are first computed, and then the similarity of web pages by the outer EMD between two ARGs is worked out to detect phishing web pages. The experimental results show that the proposed architecture and algorithm has good robustness along with scalability, and can effectively detect phishing.展开更多
Cyber Attacks are critical and destructive to all industry sectors.They affect social engineering by allowing unapproved access to a Personal Computer(PC)that breaks the corrupted system and threatens humans.The defen...Cyber Attacks are critical and destructive to all industry sectors.They affect social engineering by allowing unapproved access to a Personal Computer(PC)that breaks the corrupted system and threatens humans.The defense of security requires understanding the nature of Cyber Attacks,so prevention becomes easy and accurate by acquiring sufficient knowledge about various features of Cyber Attacks.Cyber-Security proposes appropriate actions that can handle and block attacks.A phishing attack is one of the cybercrimes in which users follow a link to illegal websites that will persuade them to divulge their private information.One of the online security challenges is the enormous number of daily transactions done via phishing sites.As Cyber-Security have a priority for all organizations,Cyber-Security risks are considered part of an organization’s risk management process.This paper presents a survey of different modern machine-learning approaches that handle phishing problems and detect with high-quality accuracy different phishing attacks.A dataset consisting of more than 11000 websites from the Kaggle dataset was utilized and studying the effect of 30 website features and the resulting class label indicating whether or not it is a phishing website(1 or−1).Furthermore,we determined the confusion matrices of Machine Learning models:Neural Networks(NN),Na飗e Bayes,and Adaboost,and the results indicated that the accuracies achieved were 90.23%,92.97%,and 95.43%,respectively.展开更多
The data in the cloud is protected by various mechanisms to ensure security aspects and user’s privacy.But,deceptive attacks like phishing might obtain the user’s data and use it for malicious purposes.In Spite of m...The data in the cloud is protected by various mechanisms to ensure security aspects and user’s privacy.But,deceptive attacks like phishing might obtain the user’s data and use it for malicious purposes.In Spite of much techno-logical advancement,phishing acts as thefirst step in a series of attacks.With technological advancements,availability and access to the phishing kits has improved drastically,thus making it an ideal tool for the hackers to execute the attacks.The phishing cases indicate use of foreign characters to disguise the ori-ginal Uniform Resource Locator(URL),typosquatting the popular domain names,using reserved characters for re directions and multi-chain phishing.Such phishing URLs can be stored as a part of the document and uploaded in the cloud,providing a nudge to hackers in cloud storage.The cloud servers are becoming the trusted tool for executing these attacks.The prevailing software for blacklisting phishing URLs lacks the security for multi-level phishing and expects security from the client’s end(browser).At the same time,the avalanche effect and immut-ability of block-chain proves to be a strong source of security.Considering these trends in technology,a block-chain basedfiltering implementation for preserving the integrity of user data stored in the cloud is proposed.The proposed Phish Block detects the homographic phishing URLs with accuracy of 91%which assures the security in cloud storage.展开更多
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 rang...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.展开更多
Phishing attacks pose a significant security threat by masquerading as trustworthy entities to steal sensitive information,a problem that persists despite user awareness.This study addresses the pressing issue of phis...Phishing attacks pose a significant security threat by masquerading as trustworthy entities to steal sensitive information,a problem that persists despite user awareness.This study addresses the pressing issue of phishing attacks on websites and assesses the performance of three prominent Machine Learning(ML)models—Artificial Neural Networks(ANN),Convolutional Neural Networks(CNN),and Long Short-Term Memory(LSTM)—utilizing authentic datasets sourced from Kaggle and Mendeley repositories.Extensive experimentation and analysis reveal that the CNN model achieves a better accuracy of 98%.On the other hand,LSTM shows the lowest accuracy of 96%.These findings underscore the potential of ML techniques in enhancing phishing detection systems and bolstering cybersecurity measures against evolving phishing tactics,offering a promising avenue for safeguarding sensitive information and online security.展开更多
In the digital age, phishing attacks have been a persistent security threat leveraged by traditional password management systems that are not able to verify the authenticity of websites. This paper presents an approac...In the digital age, phishing attacks have been a persistent security threat leveraged by traditional password management systems that are not able to verify the authenticity of websites. This paper presents an approach to embedding sophisticated phishing detection within a password manager’s framework, called PhishGuard. PhishGuard uses a Large Language Model (LLM), specifically a fine-tuned BERT algorithm that works in real time, where URLs fed by the user in the credentials are analyzed and authenticated. This approach enhances user security with its provision of real-time protection from phishing attempts. Through rigorous testing, this paper illustrates how PhishGuard has scored well in tests that measure accuracy, precision, recall, and false positive rates.展开更多
Onemust interact with a specific webpage or website in order to use the Internet for communication,teamwork,and other productive activities.However,because phishing websites look benign and not all website visitors ha...Onemust interact with a specific webpage or website in order to use the Internet for communication,teamwork,and other productive activities.However,because phishing websites look benign and not all website visitors have the same knowledge and skills to inspect the trustworthiness of visited websites,they are tricked into disclosing sensitive information and making them vulnerable to malicious software attacks like ransomware.It is impossible to stop attackers fromcreating phishingwebsites,which is one of the core challenges in combating them.However,this threat can be alleviated by detecting a specific website as phishing and alerting online users to take the necessary precautions before handing over sensitive information.In this study,five machine learning(ML)and DL algorithms—cat-boost(CATB),gradient boost(GB),random forest(RF),multilayer perceptron(MLP),and deep neural network(DNN)—were tested with three different reputable datasets and two useful feature selection techniques,to assess the scalability and consistency of each classifier’s performance on varied dataset sizes.The experimental findings reveal that the CATB classifier achieved the best accuracy across all datasets(DS-1,DS-2,and DS-3)with respective values of 97.9%,95.73%,and 98.83%.The GB classifier achieved the second-best accuracy across all datasets(DS-1,DS-2,and DS-3)with respective values of 97.16%,95.18%,and 98.58%.MLP achieved the best computational time across all datasets(DS-1,DS-2,and DS-3)with respective values of 2,7,and 3 seconds despite scoring the lowest accuracy across all datasets.展开更多
Phishing is a type of cybercrime in which cyber-attackers pose themselves as authorized persons or entities and hack the victims’sensitive data.E-mails,instant messages and phone calls are some of the common modes us...Phishing is a type of cybercrime in which cyber-attackers pose themselves as authorized persons or entities and hack the victims’sensitive data.E-mails,instant messages and phone calls are some of the common modes used in cyberattacks.Though the security models are continuously upgraded to prevent cyberattacks,hackers find innovative ways to target the victims.In this background,there is a drastic increase observed in the number of phishing emails sent to potential targets.This scenario necessitates the importance of designing an effective classification model.Though numerous conventional models are available in the literature for proficient classification of phishing emails,the Machine Learning(ML)techniques and the Deep Learning(DL)models have been employed in the literature.The current study presents an Intelligent Cuckoo Search(CS)Optimization Algorithm with a Deep Learning-based Phishing Email Detection and Classification(ICSOA-DLPEC)model.The aim of the proposed ICSOA-DLPEC model is to effectually distinguish the emails as either legitimate or phishing ones.At the initial stage,the pre-processing is performed through three stages such as email cleaning,tokenization and stop-word elimination.Then,the N-gram approach is;moreover,the CS algorithm is applied to extract the useful feature vectors.Moreover,the CS algorithm is employed with the Gated Recurrent Unit(GRU)model to detect and classify phishing emails.Furthermore,the CS algorithm is used to fine-tune the parameters involved in the GRU model.The performance of the proposed ICSOA-DLPEC model was experimentally validated using a benchmark dataset,and the results were assessed under several dimensions.Extensive comparative studies were conducted,and the results confirmed the superior performance of the proposed ICSOA-DLPEC model over other existing approaches.The proposed model achieved a maximum accuracy of 99.72%.展开更多
This paper proposes a novel phishing web image segmentation algorithm which based on improving spectral clustering.Firstly,we construct a set of points which are composed of spatial location pixels and gray levels fro...This paper proposes a novel phishing web image segmentation algorithm which based on improving spectral clustering.Firstly,we construct a set of points which are composed of spatial location pixels and gray levels from a given image.Secondly,the data is clustered in spectral space of the similar matrix of the set points,in order to avoid the drawbacks of K-means algorithm in the conventional spectral clustering method that is sensitive to initial clustering centroids and convergence to local optimal solution,we introduce the clone operator,Cauthy mutation to enlarge the scale of clustering centers,quantum-inspired evolutionary algorithm to find the global optimal clustering centroids.Compared with phishing web image segmentation based on K-means,experimental results show that the segmentation performance of our method gains much improvement.Moreover,our method can convergence to global optimal solution and is better in accuracy of phishing web segmentation.展开更多
The continuous destruction and frauds prevailing due to phishing URLs make it an indispensable area for research.Various techniques are adopted in the detection process,including neural networks,machine learning,or hy...The continuous destruction and frauds prevailing due to phishing URLs make it an indispensable area for research.Various 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 URLs.Feature selection based on various techniques to identify the phishing candidates from the URL is conducted.In this approach,the features mined from the URL are extracted using data mining rules.The features are selected on the basis of URL structure.The classification of these features identified by the data mining rules is done using PSO techniques.The selection of features with PSO optimization makes it possible to identify phishing URLs.Using a large number of rule identifiers,the true positive rate for the identification of phishing URLs is maximized in this approach.The 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 itself.Moreover,it can minimize processing time for identifying the phishing website instead.So,the approach can be beneficial to identify suchURLs over the existing contemporary detecting models proposed before.展开更多
Phishing attacks are security attacks that do not affect only individuals’or organizations’websites but may affect Internet of Things(IoT)devices and net-works.IoT environment is an exposed environment for such atta...Phishing attacks are security attacks that do not affect only individuals’or organizations’websites but may affect Internet of Things(IoT)devices and net-works.IoT environment is an exposed environment for such attacks.Attackers may use thingbots software for the dispersal of hidden junk emails that are not noticed by users.Machine and deep learning and other methods were used to design detection methods for these attacks.However,there is still a need to enhance detection accuracy.Optimization of an ensemble classification method for phishing website(PW)detection is proposed in this study.A Genetic Algo-rithm(GA)was used for the proposed method optimization by tuning several ensemble Machine Learning(ML)methods parameters,including Random Forest(RF),AdaBoost(AB),XGBoost(XGB),Bagging(BA),GradientBoost(GB),and LightGBM(LGBM).These were accomplished by ranking the optimized classi-fiers to pick out the best classifiers as a base for the proposed method.A PW data-set that is made up of 4898 PWs and 6157 legitimate websites(LWs)was used for this study's experiments.As a result,detection accuracy was enhanced and reached 97.16 percent.展开更多
Phishing,an Internet fraudwhere individuals are deceived into revealing critical personal and account information,poses a significant risk to both consumers and web-based institutions.Data indicates a persistent rise ...Phishing,an Internet fraudwhere individuals are deceived into revealing critical personal and account information,poses a significant risk to both consumers and web-based institutions.Data indicates a persistent rise in phishing attacks.Moreover,these fraudulent schemes are progressively becoming more intricate,thereby rendering them more challenging to identify.Hence,it is imperative to utilize sophisticated algorithms to address this issue.Machine learning is a highly effective approach for identifying and uncovering these harmful behaviors.Machine learning(ML)approaches can identify common characteristics in most phishing assaults.In this paper,we propose an ensemble approach and compare it with six machine learning techniques to determine the type of website and whether it is normal or not based on two phishing datasets.After that,we used the normalization technique on the dataset to transform the range of all the features into the same range.The findings of this paper for all algorithms are as follows in the first dataset based on accuracy,precision,recall,and F1-score,respectively:Decision Tree(DT)(0.964,0.961,0.976,0.968),Random Forest(RF)(0.970,0.964,0.984,0.974),Gradient Boosting(GB)(0.960,0.959,0.971,0.965),XGBoost(XGB)(0.973,0.976,0.976,0.976),AdaBoost(0.934,0.934,0.950,0.942),Multi Layer Perceptron(MLP)(0.970,0.971,0.976,0.974)and Voting(0.978,0.975,0.987,0.981).So,the Voting classifier gave the best results.While in the second dataset,all the algorithms gave the same results in four evaluation metrics,which indicates that each of them can effectively accomplish the prediction process.Also,this approach outperformed the previous work in detecting phishing websites with high accuracy,a lower false negative rate,a shorter prediction time,and a lower false positive rate.展开更多
To secure web applications from Man-In-The-Middle(MITM)and phishing attacks is a challenging task nowadays.For this purpose,authen-tication protocol plays a vital role in web communication which securely transfers dat...To secure web applications from Man-In-The-Middle(MITM)and phishing attacks is a challenging task nowadays.For this purpose,authen-tication protocol plays a vital role in web communication which securely transfers data from one party to another.This authentication works via OpenID,Kerberos,password authentication protocols,etc.However,there are still some limitations present in the reported security protocols.In this paper,the presented anticipated strategy secures both Web-based attacks by leveraging encoded emails and a novel password form pattern method.The proposed OpenID-based encrypted Email’s Authentication,Authorization,and Accounting(EAAA)protocol ensure security by relying on the email authenticity and a Special Secret Encrypted Alphanumeric String(SSEAS).This string is deployed on both the relying party and the email server,which is unique and trustworthy.The first authentication,OpenID Uniform Resource Locator(URL)identity,is performed on the identity provider side.A second authentication is carried out by the hidden Email’s server side and receives a third authentication link.This Email’s third SSEAS authentication link manages on the relying party(RP).Compared to existing cryptographic single sign-on protocols,the EAAA protocol ensures that an OpenID URL’s identity is secured from MITM and phishing attacks.This study manages two attacks such as MITM and phishing attacks and gives 339 ms response time which is higher than the already reported methods,such as Single Sign-On(SSO)and OpenID.The experimental sites were examined by 72 information technology(IT)specialists,who found that 88.89%of respondents successfully validated the user authorization provided to them via Email.The proposed EAAA protocol minimizes the higher-level risk of MITM and phishing attacks in an OpenID-based atmosphere.展开更多
Phishing attacks present a serious threat to enterprise systems,requiring advanced detection techniques to protect sensitive data.This study introduces a phishing email detection framework that combines Bidirectional ...Phishing attacks present a serious threat to enterprise systems,requiring advanced detection techniques to protect sensitive data.This study introduces a phishing email detection framework that combines Bidirectional Encoder Representations from Transformers(BERT)for feature extraction and CNN for classification,specifically designed for enterprise information systems.BERT’s linguistic capabilities are used to extract key features from email content,which are then processed by a convolutional neural network(CNN)model optimized for phishing detection.Achieving an accuracy of 97.5%,our proposed model demonstrates strong proficiency in identifying phishing emails.This approach represents a significant advancement in applying deep learning to cybersecurity,setting a new benchmark for email security by effectively addressing the increasing complexity of phishing attacks.展开更多
文摘In the evolving landscape of cyber threats,phishing attacks pose significant challenges,particularly through deceptive webpages designed to extract sensitive information under the guise of legitimacy.Conventional and machine learning(ML)-based detection systems struggle to detect phishing websites owing to their constantly changing tactics.Furthermore,newer phishing websites exhibit subtle and expertly concealed indicators that are not readily detectable.Hence,effective detection depends on identifying the most critical features.Traditional feature selection(FS)methods often struggle to enhance ML model performance and instead decrease it.To combat these issues,we propose an innovative method using explainable AI(XAI)to enhance FS in ML models and improve the identification of phishing websites.Specifically,we employ SHapley Additive exPlanations(SHAP)for global perspective and aggregated local interpretable model-agnostic explanations(LIME)to deter-mine specific localized patterns.The proposed SHAP and LIME-aggregated FS(SLA-FS)framework pinpoints the most informative features,enabling more precise,swift,and adaptable phishing detection.Applying this approach to an up-to-date web phishing dataset,we evaluate the performance of three ML models before and after FS to assess their effectiveness.Our findings reveal that random forest(RF),with an accuracy of 97.41%and XGBoost(XGB)at 97.21%significantly benefit from the SLA-FS framework,while k-nearest neighbors lags.Our framework increases the accuracy of RF and XGB by 0.65%and 0.41%,respectively,outperforming traditional filter or wrapper methods and any prior methods evaluated on this dataset,showcasing its potential.
基金The National Basic Research Program of China(973 Program)(No.2010CB328104,2009CB320501)the National Natural Science Foundation of China(No.61272531,61070158,61003257,61060161,61003311,41201486)+4 种基金the National Key Technology R&D Program during the11th Five-Year Plan Period(No.2010BAI88B03)Specialized Research Fund for the Doctoral Program of Higher Education(No.20110092130002)the National Science and Technology Major Project(No.2009ZX03004-004-04)the Foundation of the Key Laboratory of Netw ork and Information Security of Jiangsu Province(No.BM2003201)the Key Laboratory of Computer Netw ork and Information Integration of the Ministry of Education of China(No.93K-9)
文摘In order to effectively detect malicious phishing behaviors, a phishing detection method based on the uniform resource locator （URL） features is proposed. First, the method compares the phishing URLs with legal ones to extract the features of phishing URLs. Then a machine learning algorithm is applied to obtain the URL classification model from the sample data set training. In order to adapt to the change of a phishing URL, the classification model should be constantly updated according to the new samples. So, an incremental learning algorithm based on the feedback of the original sample data set is designed. The experiments verify that the combination of the URL features extracted in this paper and the support vector machine （SVM） classification algorithm can achieve a high phishing detection accuracy, and the incremental learning algorithm is also effective.
基金The National Basic Research Program of China (973Program)(2010CB328104,2009CB320501)the National Natural Science Foundation of China (No.60773103,90912002)+1 种基金Specialized Research Fund for the Doctoral Program of Higher Education(No.200802860031)Key Laboratory of Computer Network and Information Integration of Ministry of Education of China (No.93K-9)
文摘A phishing detection system, which comprises client-side filtering plug-in, analysis center and protected sites, is proposed. An image-based similarity detection algorithm is conceived to calculate the similarity of two web pages. The web pages are first converted into images, and then divided into sub-images with iterated dividing and shrinking. After that, the attributes of sub-images including color histograms, gray histograms and size parameters are computed to construct the attributed relational graph（ARG）of each page. In order to match two ARGs, the inner earth mover＇s distances（EMD）between every two nodes coming from each ARG respectively are first computed, and then the similarity of web pages by the outer EMD between two ARGs is worked out to detect phishing web pages. The experimental results show that the proposed architecture and algorithm has good robustness along with scalability, and can effectively detect phishing.
文摘Cyber Attacks are critical and destructive to all industry sectors.They affect social engineering by allowing unapproved access to a Personal Computer(PC)that breaks the corrupted system and threatens humans.The defense of security requires understanding the nature of Cyber Attacks,so prevention becomes easy and accurate by acquiring sufficient knowledge about various features of Cyber Attacks.Cyber-Security proposes appropriate actions that can handle and block attacks.A phishing attack is one of the cybercrimes in which users follow a link to illegal websites that will persuade them to divulge their private information.One of the online security challenges is the enormous number of daily transactions done via phishing sites.As Cyber-Security have a priority for all organizations,Cyber-Security risks are considered part of an organization’s risk management process.This paper presents a survey of different modern machine-learning approaches that handle phishing problems and detect with high-quality accuracy different phishing attacks.A dataset consisting of more than 11000 websites from the Kaggle dataset was utilized and studying the effect of 30 website features and the resulting class label indicating whether or not it is a phishing website(1 or−1).Furthermore,we determined the confusion matrices of Machine Learning models:Neural Networks(NN),Na飗e Bayes,and Adaboost,and the results indicated that the accuracies achieved were 90.23%,92.97%,and 95.43%,respectively.
文摘The data in the cloud is protected by various mechanisms to ensure security aspects and user’s privacy.But,deceptive attacks like phishing might obtain the user’s data and use it for malicious purposes.In Spite of much techno-logical advancement,phishing acts as thefirst step in a series of attacks.With technological advancements,availability and access to the phishing kits has improved drastically,thus making it an ideal tool for the hackers to execute the attacks.The phishing cases indicate use of foreign characters to disguise the ori-ginal Uniform Resource Locator(URL),typosquatting the popular domain names,using reserved characters for re directions and multi-chain phishing.Such phishing URLs can be stored as a part of the document and uploaded in the cloud,providing a nudge to hackers in cloud storage.The cloud servers are becoming the trusted tool for executing these attacks.The prevailing software for blacklisting phishing URLs lacks the security for multi-level phishing and expects security from the client’s end(browser).At the same time,the avalanche effect and immut-ability of block-chain proves to be a strong source of security.Considering these trends in technology,a block-chain basedfiltering implementation for preserving the integrity of user data stored in the cloud is proposed.The proposed Phish Block detects the homographic phishing URLs with accuracy of 91%which assures the security in cloud storage.
文摘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.
文摘Phishing attacks pose a significant security threat by masquerading as trustworthy entities to steal sensitive information,a problem that persists despite user awareness.This study addresses the pressing issue of phishing attacks on websites and assesses the performance of three prominent Machine Learning(ML)models—Artificial Neural Networks(ANN),Convolutional Neural Networks(CNN),and Long Short-Term Memory(LSTM)—utilizing authentic datasets sourced from Kaggle and Mendeley repositories.Extensive experimentation and analysis reveal that the CNN model achieves a better accuracy of 98%.On the other hand,LSTM shows the lowest accuracy of 96%.These findings underscore the potential of ML techniques in enhancing phishing detection systems and bolstering cybersecurity measures against evolving phishing tactics,offering a promising avenue for safeguarding sensitive information and online security.
文摘In the digital age, phishing attacks have been a persistent security threat leveraged by traditional password management systems that are not able to verify the authenticity of websites. This paper presents an approach to embedding sophisticated phishing detection within a password manager’s framework, called PhishGuard. PhishGuard uses a Large Language Model (LLM), specifically a fine-tuned BERT algorithm that works in real time, where URLs fed by the user in the credentials are analyzed and authenticated. This approach enhances user security with its provision of real-time protection from phishing attempts. Through rigorous testing, this paper illustrates how PhishGuard has scored well in tests that measure accuracy, precision, recall, and false positive rates.
文摘Onemust interact with a specific webpage or website in order to use the Internet for communication,teamwork,and other productive activities.However,because phishing websites look benign and not all website visitors have the same knowledge and skills to inspect the trustworthiness of visited websites,they are tricked into disclosing sensitive information and making them vulnerable to malicious software attacks like ransomware.It is impossible to stop attackers fromcreating phishingwebsites,which is one of the core challenges in combating them.However,this threat can be alleviated by detecting a specific website as phishing and alerting online users to take the necessary precautions before handing over sensitive information.In this study,five machine learning(ML)and DL algorithms—cat-boost(CATB),gradient boost(GB),random forest(RF),multilayer perceptron(MLP),and deep neural network(DNN)—were tested with three different reputable datasets and two useful feature selection techniques,to assess the scalability and consistency of each classifier’s performance on varied dataset sizes.The experimental findings reveal that the CATB classifier achieved the best accuracy across all datasets(DS-1,DS-2,and DS-3)with respective values of 97.9%,95.73%,and 98.83%.The GB classifier achieved the second-best accuracy across all datasets(DS-1,DS-2,and DS-3)with respective values of 97.16%,95.18%,and 98.58%.MLP achieved the best computational time across all datasets(DS-1,DS-2,and DS-3)with respective values of 2,7,and 3 seconds despite scoring the lowest accuracy across all datasets.
基金This research was supported in part by Basic Science Research Program through the National Research Foundation of Korea(NRF),funded by the Ministry of Education(NRF-2021R1A6A1A03039493)in part by the NRF grant funded by the Korea government(MSIT)(NRF-2022R1A2C1004401).
文摘Phishing is a type of cybercrime in which cyber-attackers pose themselves as authorized persons or entities and hack the victims’sensitive data.E-mails,instant messages and phone calls are some of the common modes used in cyberattacks.Though the security models are continuously upgraded to prevent cyberattacks,hackers find innovative ways to target the victims.In this background,there is a drastic increase observed in the number of phishing emails sent to potential targets.This scenario necessitates the importance of designing an effective classification model.Though numerous conventional models are available in the literature for proficient classification of phishing emails,the Machine Learning(ML)techniques and the Deep Learning(DL)models have been employed in the literature.The current study presents an Intelligent Cuckoo Search(CS)Optimization Algorithm with a Deep Learning-based Phishing Email Detection and Classification(ICSOA-DLPEC)model.The aim of the proposed ICSOA-DLPEC model is to effectually distinguish the emails as either legitimate or phishing ones.At the initial stage,the pre-processing is performed through three stages such as email cleaning,tokenization and stop-word elimination.Then,the N-gram approach is;moreover,the CS algorithm is applied to extract the useful feature vectors.Moreover,the CS algorithm is employed with the Gated Recurrent Unit(GRU)model to detect and classify phishing emails.Furthermore,the CS algorithm is used to fine-tune the parameters involved in the GRU model.The performance of the proposed ICSOA-DLPEC model was experimentally validated using a benchmark dataset,and the results were assessed under several dimensions.Extensive comparative studies were conducted,and the results confirmed the superior performance of the proposed ICSOA-DLPEC model over other existing approaches.The proposed model achieved a maximum accuracy of 99.72%.
基金Supported by the Fundamental Research Funds for the Central Universities in North China Electric Power University(11MG13)the Natural Science Foundation of Hebei Province(F2011502038)
文摘This paper proposes a novel phishing web image segmentation algorithm which based on improving spectral clustering.Firstly,we construct a set of points which are composed of spatial location pixels and gray levels from a given image.Secondly,the data is clustered in spectral space of the similar matrix of the set points,in order to avoid the drawbacks of K-means algorithm in the conventional spectral clustering method that is sensitive to initial clustering centroids and convergence to local optimal solution,we introduce the clone operator,Cauthy mutation to enlarge the scale of clustering centers,quantum-inspired evolutionary algorithm to find the global optimal clustering centroids.Compared with phishing web image segmentation based on K-means,experimental results show that the segmentation performance of our method gains much improvement.Moreover,our method can convergence to global optimal solution and is better in accuracy of phishing web segmentation.
基金The authors would like to thank the Deanship of Scientific Research at Shaqra University for supporting this work.
文摘The continuous destruction and frauds prevailing due to phishing URLs make it an indispensable area for research.Various 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 URLs.Feature selection based on various techniques to identify the phishing candidates from the URL is conducted.In this approach,the features mined from the URL are extracted using data mining rules.The features are selected on the basis of URL structure.The classification of these features identified by the data mining rules is done using PSO techniques.The selection of features with PSO optimization makes it possible to identify phishing URLs.Using a large number of rule identifiers,the true positive rate for the identification of phishing URLs is maximized in this approach.The 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 itself.Moreover,it can minimize processing time for identifying the phishing website instead.So,the approach can be beneficial to identify suchURLs over the existing contemporary detecting models proposed before.
基金This research has been funded by the Scientific Research Deanship at University of Ha'il-Saudi Arabia through Project Number RG-20023.
文摘Phishing attacks are security attacks that do not affect only individuals’or organizations’websites but may affect Internet of Things(IoT)devices and net-works.IoT environment is an exposed environment for such attacks.Attackers may use thingbots software for the dispersal of hidden junk emails that are not noticed by users.Machine and deep learning and other methods were used to design detection methods for these attacks.However,there is still a need to enhance detection accuracy.Optimization of an ensemble classification method for phishing website(PW)detection is proposed in this study.A Genetic Algo-rithm(GA)was used for the proposed method optimization by tuning several ensemble Machine Learning(ML)methods parameters,including Random Forest(RF),AdaBoost(AB),XGBoost(XGB),Bagging(BA),GradientBoost(GB),and LightGBM(LGBM).These were accomplished by ranking the optimized classi-fiers to pick out the best classifiers as a base for the proposed method.A PW data-set that is made up of 4898 PWs and 6157 legitimate websites(LWs)was used for this study's experiments.As a result,detection accuracy was enhanced and reached 97.16 percent.
基金funding from Deanship of Scientific Research in King Faisal University with Grant Number KFU 241085.
文摘Phishing,an Internet fraudwhere individuals are deceived into revealing critical personal and account information,poses a significant risk to both consumers and web-based institutions.Data indicates a persistent rise in phishing attacks.Moreover,these fraudulent schemes are progressively becoming more intricate,thereby rendering them more challenging to identify.Hence,it is imperative to utilize sophisticated algorithms to address this issue.Machine learning is a highly effective approach for identifying and uncovering these harmful behaviors.Machine learning(ML)approaches can identify common characteristics in most phishing assaults.In this paper,we propose an ensemble approach and compare it with six machine learning techniques to determine the type of website and whether it is normal or not based on two phishing datasets.After that,we used the normalization technique on the dataset to transform the range of all the features into the same range.The findings of this paper for all algorithms are as follows in the first dataset based on accuracy,precision,recall,and F1-score,respectively:Decision Tree(DT)(0.964,0.961,0.976,0.968),Random Forest(RF)(0.970,0.964,0.984,0.974),Gradient Boosting(GB)(0.960,0.959,0.971,0.965),XGBoost(XGB)(0.973,0.976,0.976,0.976),AdaBoost(0.934,0.934,0.950,0.942),Multi Layer Perceptron(MLP)(0.970,0.971,0.976,0.974)and Voting(0.978,0.975,0.987,0.981).So,the Voting classifier gave the best results.While in the second dataset,all the algorithms gave the same results in four evaluation metrics,which indicates that each of them can effectively accomplish the prediction process.Also,this approach outperformed the previous work in detecting phishing websites with high accuracy,a lower false negative rate,a shorter prediction time,and a lower false positive rate.
文摘To secure web applications from Man-In-The-Middle(MITM)and phishing attacks is a challenging task nowadays.For this purpose,authen-tication protocol plays a vital role in web communication which securely transfers data from one party to another.This authentication works via OpenID,Kerberos,password authentication protocols,etc.However,there are still some limitations present in the reported security protocols.In this paper,the presented anticipated strategy secures both Web-based attacks by leveraging encoded emails and a novel password form pattern method.The proposed OpenID-based encrypted Email’s Authentication,Authorization,and Accounting(EAAA)protocol ensure security by relying on the email authenticity and a Special Secret Encrypted Alphanumeric String(SSEAS).This string is deployed on both the relying party and the email server,which is unique and trustworthy.The first authentication,OpenID Uniform Resource Locator(URL)identity,is performed on the identity provider side.A second authentication is carried out by the hidden Email’s server side and receives a third authentication link.This Email’s third SSEAS authentication link manages on the relying party(RP).Compared to existing cryptographic single sign-on protocols,the EAAA protocol ensures that an OpenID URL’s identity is secured from MITM and phishing attacks.This study manages two attacks such as MITM and phishing attacks and gives 339 ms response time which is higher than the already reported methods,such as Single Sign-On(SSO)and OpenID.The experimental sites were examined by 72 information technology(IT)specialists,who found that 88.89%of respondents successfully validated the user authorization provided to them via Email.The proposed EAAA protocol minimizes the higher-level risk of MITM and phishing attacks in an OpenID-based atmosphere.
基金supported by a grant from Hong Kong Metropolitan University (RD/2023/2.3)supported Princess Nourah bint Abdulrah-man University Researchers Supporting Project number (PNURSP2024R 343)+1 种基金Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabiathe Deanship of Scientific Research at Northern Border University,Arar,Kingdom of Saudi Arabia for funding this research work through the project number“NBU-FFR-2024-1092-09”.
文摘Phishing attacks present a serious threat to enterprise systems,requiring advanced detection techniques to protect sensitive data.This study introduces a phishing email detection framework that combines Bidirectional Encoder Representations from Transformers(BERT)for feature extraction and CNN for classification,specifically designed for enterprise information systems.BERT’s linguistic capabilities are used to extract key features from email content,which are then processed by a convolutional neural network(CNN)model optimized for phishing detection.Achieving an accuracy of 97.5%,our proposed model demonstrates strong proficiency in identifying phishing emails.This approach represents a significant advancement in applying deep learning to cybersecurity,setting a new benchmark for email security by effectively addressing the increasing complexity of phishing attacks.
