Machine Learning Techniques for Detecting Phishing URL Attacks

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.

Adaptive Dynamic Dipper Throated Optimization for Feature Selection in Medical Data

The rapid population growth results in a crucial problem in the early detection of diseases inmedical research.Among all the cancers unveiled,breast cancer is considered the second most severe cancer.Consequently,an exponential rising in death cases incurred by breast cancer is expected due to the rapid population growth and the lack of resources required for performing medical diagnoses.Utilizing recent advances in machine learning could help medical staff in diagnosing diseases as they offer effective,reliable,and rapid responses,which could help in decreasing the death risk.In this paper,we propose a new algorithm for feature selection based on a hybrid between powerful and recently emerged optimizers,namely,guided whale and dipper throated optimizers.The proposed algorithm is evaluated using four publicly available breast cancer datasets.The evaluation results show the effectiveness of the proposed approach from the accuracy and speed perspectives.To prove the superiority of the proposed algorithm,a set of competing feature selection algorithms were incorporated into the conducted experiments.In addition,a group of statistical analysis experiments was conducted to emphasize the superiority and stability of the proposed algorithm.The best-achieved breast cancer prediction average accuracy based on the proposed algorithm is 99.453%.This result is achieved in an average time of 3.6725 s,the best result among all the competing approaches utilized in the experiments.

Eye Detection-Based Deep Belief Neural Networks and Speeded-Up Robust Feature Algorithm

The ability to detect and localize the human eye is critical for use in security applications and human identification and verification systems.This is because eye recognition algorithms have multiple challenges,such as multi-pose variations,ocular parts,and illumination.Moreover,the modern security applica-tions fail to detect facial expressions from eye images.In this paper,a Speeded-Up Roust Feature(SURF)Algorithm was utilized to localize the face images of the enrolled subjects.We highlighted on eye and pupil parts to be detected based on SURF,Hough Circle Transform(HCT),and Local Binary Pattern(LBP).Afterward,Deep Belief Neural Networks(DBNN)were used to classify the input features results from the SURF algorithm.We further determined the correctly and wrongly classified subjects using a confusion matrix with two class labels to classify people whose eye images are correctly detected.We apply Stochastic Gradient Descent(SGD)optimizer to address the overfitting problem,and the hyper-parameters arefine-tuned based on the applied DBNN.The accuracy of the proposed system is determined based on SURF,LBP,and DBNN classifier achieved 95.54%for the ORL dataset,94.07%for the BioID,and 96.20%for the CASIA-V5 dataset.The proposed approach is more reliable and more advanced when compared with state-of-the-art algorithms.

Weight Prediction Using the Hybrid Stacked-LSTM Food Selection Model

Food choice motives(i.e.,mood,health,natural content,convenience,sensory appeal,price,familiarities,ethical concerns,and weight control)have an important role in transforming the current food system to ensure the healthiness of people and the sustainability of the world.Researchers from several domains have presented several models addressing issues influencing food choice over the years.However,a multidisciplinary approach is required to better understand how various aspects interact with one another during the decision-making procedure.In this paper,four Deep Learning(DL)models and one Machine Learning(ML)model are utilized to predict the weight in pounds based on food choices.The Long Short-Term Memory(LSTM)model,stacked-LSTM model,Conventional Neural Network(CNN)model,and CNN-LSTM model are the used deep learning models.While the applied ML model is the K-Nearest Neighbor(KNN)regressor.The efficiency of the proposed model was determined based on the error rate obtained from the experimental results.The findings indicated that Mean Absolute Error(MAE)is 0.0087,the Mean Square Error(MSE)is 0.00011,the Median Absolute Error(MedAE)is 0.006,the Root Mean Square Error(RMSE)is 0.011,and the Mean Absolute Percentage Error(MAPE)is 21.Therefore,the results demonstrated that the stacked LSTM achieved improved results compared with the LSTM,CNN,CNN-LSTM,and KNN regressor.

Wind Power Prediction Based on Machine Learning and Deep Learning Models

Wind power is one of the sustainable ways to generate renewable energy.In recent years,some countries have set renewables to meet future energy needs,with the primary goal of reducing emissions and promoting sustainable growth,primarily the use of wind and solar power.To achieve the prediction of wind power generation,several deep and machine learning models are constructed in this article as base models.These regression models are Deep neural network(DNN),k-nearest neighbor(KNN)regressor,long short-term memory(LSTM),averaging model,random forest(RF)regressor,bagging regressor,and gradient boosting(GB)regressor.In addition,data cleaning and data preprocessing were performed to the data.The dataset used in this study includes 4 features and 50530 instances.To accurately predict the wind power values,we propose in this paper a new optimization technique based on stochastic fractal search and particle swarm optimization(SFSPSO)to optimize the parameters of LSTM network.Five evaluation criteria were utilized to estimate the efficiency of the regression models,namely,mean absolute error(MAE),Nash Sutcliffe Efficiency(NSE),mean square error(MSE),coefficient of determination(R2),root mean squared error(RMSE).The experimental results illustrated that the proposed optimization of LSTM using SFS-PSO model achieved the best results with R2 equals 99.99%in predicting the wind power values.

Metaheuristic Optimization Through Deep Learning Classification of COVID-19 in Chest X-Ray Images

As corona virus disease(COVID-19)is still an ongoing global outbreak,countries around the world continue to take precautions and measures to control the spread of the pandemic.Because of the excessive number of infected patients and the resulting deficiency of testing kits in hospitals,a rapid,reliable,and automatic detection of COVID-19 is in extreme need to curb the number of infections.By analyzing the COVID-19 chest X-ray images,a novel metaheuristic approach is proposed based on hybrid dipper throated and particle swarm optimizers.The lung region was segmented from the original chest X-ray images and augmented using various transformation operations.Furthermore,the augmented images were fed into the VGG19 deep network for feature extraction.On the other hand,a feature selection method is proposed to select the most significant features that can boost the classification results.Finally,the selected features were input into an optimized neural network for detection.The neural network is optimized using the proposed hybrid optimizer.The experimental results showed that the proposed method achieved 99.88%accuracy,outperforming the existing COVID-19 detection models.In addition,a deep statistical analysis is performed to study the performance and stability of the proposed optimizer.The results confirm the effectiveness and superiority of the proposed approach.

Using Semantic Web Technologies to Improve the Extract Transform Load Model

Semantic Web(SW)provides new opportunities for the study and application of big data,massive ranges of data sets in varied formats from multiple sources.Related studies focus on potential SW technologies for resolving big data problems,such as structurally and semantically heterogeneous data that result from the variety of data formats(structured,semi-structured,numeric,unstructured text data,email,video,audio,stock ticker).SW offers information semantically both for people and machines to retain the vast volume of data and provide a meaningful output of unstructured data.In the current research,we implement a new semantic Extract Transform Load(ETL)model that uses SW technologies for aggregating,integrating,and representing data as linked data.First,geospatial data resources are aggregated from the internet,and then a semantic ETL model is used to store the aggregated data in a semantic model after converting it to Resource Description Framework(RDF)format for successful integration and representation.The principal contribution of this research is the synthesis,aggregation,and semantic representation of geospatial data to solve problems.A case study of city data is used to illustrate the semantic ETL model’s functionalities.The results show that the proposed model solves the structural and semantic heterogeneity problems in diverse data sources for successful data aggregation,integration,and representation.

A Novel Convolutional Neural Network Model for Malaria Cell Images Classification

Infectious diseases are an imminent danger that faces human beings around the world.Malaria is considered a highly contagious disease.The diagnosis of various diseases,including malaria,was performed manually,but it required a lot of time and had some human errors.Therefore,there is a need to investigate an efficient and fast automatic diagnosis system.Deploying deep learning algorithms can provide a solution in which they can learn complex image patterns and have a rapid improvement in medical image analysis.This study proposed a Convolutional Neural Network(CNN)model to detect malaria automatically.A Malaria Convolutional Neural Network(MCNN)model is proposed in this work to classify the infected cases.MCNN focuses on detecting infected cells,which aids in the computation of parasitemia,or infection measures.The proposed model achieved 0.9929,0.9848,0.9859,0.9924,0.0152,0.0141,0.0071,0.9890,0.9894,and 0.9780 in terms of specificity,sensitivity,precision,accuracy,F1-score,and Matthews Correlation Coefficient,respectively.A comparison was carried out between the proposed model and some recent works in the literature.This comparison demonstrates that the proposed model outperforms the compared works in terms of evaluation metrics.

A Quantum Algorithm for Evaluating the Hamming Distance

We present a novel quantum algorithm to evaluate the hamming distance between two unknown oracles via measuring the degree of entanglement between two ancillary qubits.In particular,we use the power of the entanglement degree based quantum computing model that preserves at most the locality of interactions within the quantum model structure.This model uses one of two techniques to retrieve the solution of a quantum computing problem at hand.In the first technique,the solution of the problem is obtained based on whether there is an entanglement between the two ancillary qubits or not.In the second,the solution of the quantum computing problem is obtained as a function in the concurrence value,and the number of states that can be generated from the Boolean variables.The proposed algorithm receives two oracles,each oracle represents an unknown Boolean function,then it measures the hamming distance between these two oracles.The hamming distance is evaluated based on the second technique.It is shown that the proposed algorithm provides exponential speedup compared with the classical counterpart for Boolean functions that have large numbers of Boolean variables.The proposed algorithm is explained via a case study.Finally,employing recently developed experimental techniques,the proposed algorithm has been verified using IBM’s quantum computer simulator.

Unlocking the power of blockchain in education:An overview of innovations and outcomes

Blockchain is a revolutionary technology that has the potential to revolutionize various industries,including finance,supply chain management,healthcare,and education.Its decentralized,secure,and transparent nature makes it ideal for use in industries where trust,security,and efficiency are of paramount importance.The integration of blockchain technology into the education system has the potential to greatly improve the efficiency,security,and credibility of the educational process.By creating secure and transparent platforms for tracking and verifying students’academic achievements,blockchain technology can help to create a more accessible and trustworthy education system,making it easier for students to showcase their skills and knowledge to potential employers.While the potential benefits of blockchain in education are significant,there are also several challenges that must be addressed in order to fully realize the potential of this technology in the educational sector.Some of the major challenges include adoption,technical knowledge,interoperability,regulation,cost,data privacy and security,scalability,and accessibility.The necessary equipment for the implementation of blockchain technology in education is diverse and critical to the success of this innovative technology.Organizations should carefully consider this equipment when planning their implementation of blockchain technology in education to ensure the efficient and secure transfer of educational data and transactions within the blockchain network.Blockchain technology has the potential to play a significant role in promoting sustainability education and advancing the sustainability goals of both individuals and organizations.Organizations should consider incorporating blockchain technology into their sustainability education programs,in order to enhance the transparency,verifiability,and efficiency of their sustainability-related activities.While the use of blockchain technology in education is still in its early stages,the available data suggest that it has significant potential to transform the education sector and improve the efficiency and transparency of educational systems.