The potential of text analytics is revealed by Machine Learning(ML)and Natural Language Processing(NLP)techniques.In this paper,we propose an NLP framework that is applied to multiple datasets to detect malicious Unif...The potential of text analytics is revealed by Machine Learning(ML)and Natural Language Processing(NLP)techniques.In this paper,we propose an NLP framework that is applied to multiple datasets to detect malicious Uniform Resource Locators(URLs).Three categories of features,both ML and Deep Learning(DL)algorithms and a ranking schema are included in the proposed framework.We apply frequency and prediction-based embeddings,such as hash vectorizer,Term Frequency-Inverse Dense Frequency(TF-IDF)and predictors,word to vector-word2vec(continuous bag of words,skip-gram)from Google,to extract features from text.Further,we apply more state-of-the-art methods to create vectorized features,such as GloVe.Additionally,feature engineering that is specific to URL structure is deployed to detect scams and other threats.For framework assessment,four ranking indicators are weighted:computational time and performance as accuracy,F1 score and type error II.For the computational time,we propose a new metric-Feature Building Time(FBT)as the cutting-edge feature builders(like doc2vec or GloVe)require more time.By applying the proposed assessment step,the skip-gram algorithm of word2vec surpasses other feature builders in performance.Additionally,eXtreme Gradient Boost(XGB)outperforms other classifiers.With this setup,we attain an accuracy of 99.5%and an F1 score of 0.99.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
Cybersecurity-related solutions have become familiar since it ensures security and privacy against cyberattacks in this digital era.Malicious Uniform Resource Locators(URLs)can be embedded in email or Twitter and used...Cybersecurity-related solutions have become familiar since it ensures security and privacy against cyberattacks in this digital era.Malicious Uniform Resource Locators(URLs)can be embedded in email or Twitter and used to lure vulnerable internet users to implement malicious data in their systems.This may result in compromised security of the systems,scams,and other such cyberattacks.These attacks hijack huge quantities of the available data,incurring heavy financial loss.At the same time,Machine Learning(ML)and Deep Learning(DL)models paved the way for designing models that can detect malicious URLs accurately and classify them.With this motivation,the current article develops an Artificial Fish Swarm Algorithm(AFSA)with Deep Learning Enabled Malicious URL Detection and Classification(AFSADL-MURLC)model.The presented AFSADL-MURLC model intends to differentiate the malicious URLs from genuine URLs.To attain this,AFSADL-MURLC model initially carries out data preprocessing and makes use of glove-based word embedding technique.In addition,the created vector model is then passed onto Gated Recurrent Unit(GRU)classification to recognize the malicious URLs.Finally,AFSA is applied to the proposed model to enhance the efficiency of GRU model.The proposed AFSADL-MURLC technique was experimentally validated using benchmark dataset sourced from Kaggle repository.The simulation results confirmed the supremacy of the proposed AFSADL-MURLC model over recent approaches under distinct measures.展开更多
The social engineering cyber-attack is where culprits mislead the users by getting the login details which provides the information to the evil server called phishing.The deep learning approaches and the machine learn...The social engineering cyber-attack is where culprits mislead the users by getting the login details which provides the information to the evil server called phishing.The deep learning approaches and the machine learning are compared in the proposed system for presenting the methodology that can detect phishing websites via Uniform Resource Locator(URLs)analysis.The legal class is composed of the home pages with no inclusion of login forms in most of the present modern solutions,which deals with the detection of phishing.Contrarily,the URLs in both classes from the login page due,considering the representation of a real case scenario and the demonstration for obtaining the rate of false-positive with the existing approaches during the legal login pages provides the test having URLs.In addition,some model reduces the accuracy rather than training the base model and testing the latest URLs.In addition,a feature analysis is performed on the present phishing domains to identify various approaches to using the phishers in the campaign.A new dataset called the MUPD dataset is used for evaluation.Lastly,a prediction model,the Dense forward-backwards Long Short Term Memory(LSTM)model(d−FBLSTM),is presented for combining the forward and backward propagation of LSMT to obtain the accuracy of 98.5%on the initiated login URL dataset.展开更多
Phishing websites present a severe cybersecurity risk since they can lead to financial losses,data breaches,and user privacy violations.This study uses machine learning approaches to solve the problem of phishing webs...Phishing websites present a severe cybersecurity risk since they can lead to financial losses,data breaches,and user privacy violations.This study uses machine learning approaches to solve the problem of phishing website detection.Using artificial intelligence,the project aims to provide efficient techniques for locating and thwarting these dangerous websites.The study goals were attained by performing a thorough literature analysis to investigate several models and methods often used in phishing website identification.Logistic Regression,K-Nearest Neighbors,Decision Trees,Random Forests,Support Vector Classifiers,Linear Support Vector Classifiers,and Naive Bayes were all used in the inquiry.This research covers the benefits and drawbacks of several Machine Learning approaches,illuminating how well-suited each is to overcome the difficulties in locating and countering phishing website predictions.The insights gained from this literature review guide the selection and implementation of appropriate models and methods in future research and real-world applications related to phishing detections.The study evaluates and compares accuracy,precision and recalls of several machine learning models in detecting phishing website URL’s detection.展开更多
基于晶闸管换流器的特高压直流输电系统(ultra-high voltage direct current based on line commutated converter,LCC-UHVDC)的故障定位算法对智能电网的安全稳定运行起着重要作用。针对长距离特高压直流输电系统故障测距方法精准度低...基于晶闸管换流器的特高压直流输电系统(ultra-high voltage direct current based on line commutated converter,LCC-UHVDC)的故障定位算法对智能电网的安全稳定运行起着重要作用。针对长距离特高压直流输电系统故障测距方法精准度低、快速性差的问题,提出了一种基于变分模态分解法(variational mode decomposition,VMD)和Teager能量算子(Teager energy operator,TEO)的双端行波故障测距方法。首先,研究了LCC-UHVDC线路故障电压行波的传播特性。利用零模电压随线路传播衰减明显的特征,通过VMD算法提取采样点处零模电压行波的时频特性。针对VMD参数选择不当导致的模态混叠问题,利用K-L散度(Kullback-Leibler divergence)对提取的模态指标进行优化。然后采用TEO对分解后信号进行瞬时能量谱提取,精确标定波头到达时间,最后采用双端迭代测距法迭代求解故障距离。在PSCAD/EMTDC搭建±800 kV LCC-UHVDC仿真模型进行验证。结果表明,所提方法在不同故障位置、过渡电阻和故障类型下具有较强的鲁棒性。展开更多
基金supported by a grant of the Ministry of Research,Innovation and Digitization,CNCS-UEFISCDI,Project Number PN-Ⅲ-P4-PCE-2021-0334,within PNCDI Ⅲ.
文摘The potential of text analytics is revealed by Machine Learning(ML)and Natural Language Processing(NLP)techniques.In this paper,we propose an NLP framework that is applied to multiple datasets to detect malicious Uniform Resource Locators(URLs).Three categories of features,both ML and Deep Learning(DL)algorithms and a ranking schema are included in the proposed framework.We apply frequency and prediction-based embeddings,such as hash vectorizer,Term Frequency-Inverse Dense Frequency(TF-IDF)and predictors,word to vector-word2vec(continuous bag of words,skip-gram)from Google,to extract features from text.Further,we apply more state-of-the-art methods to create vectorized features,such as GloVe.Additionally,feature engineering that is specific to URL structure is deployed to detect scams and other threats.For framework assessment,four ranking indicators are weighted:computational time and performance as accuracy,F1 score and type error II.For the computational time,we propose a new metric-Feature Building Time(FBT)as the cutting-edge feature builders(like doc2vec or GloVe)require more time.By applying the proposed assessment step,the skip-gram algorithm of word2vec surpasses other feature builders in performance.Additionally,eXtreme Gradient Boost(XGB)outperforms other classifiers.With this setup,we attain an accuracy of 99.5%and an F1 score of 0.99.
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.
基金the Deanship of Scientific Research at King Khalid University for funding this work through Large Groups Project under grant number(45/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R140)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:22UQU4310373DSR21.
文摘Cybersecurity-related solutions have become familiar since it ensures security and privacy against cyberattacks in this digital era.Malicious Uniform Resource Locators(URLs)can be embedded in email or Twitter and used to lure vulnerable internet users to implement malicious data in their systems.This may result in compromised security of the systems,scams,and other such cyberattacks.These attacks hijack huge quantities of the available data,incurring heavy financial loss.At the same time,Machine Learning(ML)and Deep Learning(DL)models paved the way for designing models that can detect malicious URLs accurately and classify them.With this motivation,the current article develops an Artificial Fish Swarm Algorithm(AFSA)with Deep Learning Enabled Malicious URL Detection and Classification(AFSADL-MURLC)model.The presented AFSADL-MURLC model intends to differentiate the malicious URLs from genuine URLs.To attain this,AFSADL-MURLC model initially carries out data preprocessing and makes use of glove-based word embedding technique.In addition,the created vector model is then passed onto Gated Recurrent Unit(GRU)classification to recognize the malicious URLs.Finally,AFSA is applied to the proposed model to enhance the efficiency of GRU model.The proposed AFSADL-MURLC technique was experimentally validated using benchmark dataset sourced from Kaggle repository.The simulation results confirmed the supremacy of the proposed AFSADL-MURLC model over recent approaches under distinct measures.
文摘The social engineering cyber-attack is where culprits mislead the users by getting the login details which provides the information to the evil server called phishing.The deep learning approaches and the machine learning are compared in the proposed system for presenting the methodology that can detect phishing websites via Uniform Resource Locator(URLs)analysis.The legal class is composed of the home pages with no inclusion of login forms in most of the present modern solutions,which deals with the detection of phishing.Contrarily,the URLs in both classes from the login page due,considering the representation of a real case scenario and the demonstration for obtaining the rate of false-positive with the existing approaches during the legal login pages provides the test having URLs.In addition,some model reduces the accuracy rather than training the base model and testing the latest URLs.In addition,a feature analysis is performed on the present phishing domains to identify various approaches to using the phishers in the campaign.A new dataset called the MUPD dataset is used for evaluation.Lastly,a prediction model,the Dense forward-backwards Long Short Term Memory(LSTM)model(d−FBLSTM),is presented for combining the forward and backward propagation of LSMT to obtain the accuracy of 98.5%on the initiated login URL dataset.
文摘Phishing websites present a severe cybersecurity risk since they can lead to financial losses,data breaches,and user privacy violations.This study uses machine learning approaches to solve the problem of phishing website detection.Using artificial intelligence,the project aims to provide efficient techniques for locating and thwarting these dangerous websites.The study goals were attained by performing a thorough literature analysis to investigate several models and methods often used in phishing website identification.Logistic Regression,K-Nearest Neighbors,Decision Trees,Random Forests,Support Vector Classifiers,Linear Support Vector Classifiers,and Naive Bayes were all used in the inquiry.This research covers the benefits and drawbacks of several Machine Learning approaches,illuminating how well-suited each is to overcome the difficulties in locating and countering phishing website predictions.The insights gained from this literature review guide the selection and implementation of appropriate models and methods in future research and real-world applications related to phishing detections.The study evaluates and compares accuracy,precision and recalls of several machine learning models in detecting phishing website URL’s detection.
文摘基于晶闸管换流器的特高压直流输电系统(ultra-high voltage direct current based on line commutated converter,LCC-UHVDC)的故障定位算法对智能电网的安全稳定运行起着重要作用。针对长距离特高压直流输电系统故障测距方法精准度低、快速性差的问题,提出了一种基于变分模态分解法(variational mode decomposition,VMD)和Teager能量算子(Teager energy operator,TEO)的双端行波故障测距方法。首先,研究了LCC-UHVDC线路故障电压行波的传播特性。利用零模电压随线路传播衰减明显的特征,通过VMD算法提取采样点处零模电压行波的时频特性。针对VMD参数选择不当导致的模态混叠问题,利用K-L散度(Kullback-Leibler divergence)对提取的模态指标进行优化。然后采用TEO对分解后信号进行瞬时能量谱提取,精确标定波头到达时间,最后采用双端迭代测距法迭代求解故障距离。在PSCAD/EMTDC搭建±800 kV LCC-UHVDC仿真模型进行验证。结果表明,所提方法在不同故障位置、过渡电阻和故障类型下具有较强的鲁棒性。
