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Purpose This paper aims to describe the use of a meta-learning framework for recommending cost-sensitive classification methods with the aim of answering an important question that arises in machine learning, namely, “Among all the available classification algorithms, and in considering a specific type of data and cost, which is the best algorithm for my problem?” Design/methodology/approach This paper describes the use of a meta-learning framework for recommending cost-sensitive classification methods for the aim of answering an important question that arises in machine learning, namely, “Among all the available classification algorithms, and in considering a specific type of data and cost, which is the best algorithm for my problem?” The framework is based on the idea of applying machine learning techniques to discover knowledge about the performance of different machine learning algorithms. It includes components that repeatedly apply different classification methods on data sets and measures their performance. The characteristics of the data sets, combined with the algorithms and the performance provide the training examples. A decision tree algorithm is applied to the training examples to induce the knowledge, which can then be used to recommend algorithms for new data sets. The paper makes a contribution to both meta-learning and cost-sensitive machine learning approaches. Those both fields are not new, however, building a recommender that recommends the optimal case-sensitive approach for a given data problem is the contribution. The proposed solution is implemented in WEKA and evaluated by applying it on different data sets and comparing the results with existing studies available in the literature. The results show that a developed meta-learning solution produces better results than METAL, a well-known meta-learning system. The developed solution takes the misclassification cost into consideration during the learning process, which is not available in the compared project. Findings The proposed solution is implemented in WEKA and evaluated by applying it to different data sets and comparing the results with existing studies available in the literature. The results show that a developed meta-learning solution produces better results than METAL, a well-known meta-learning system. Originality/value The paper presents a major piece of new information in writing for the first time. Meta-learning work has been done before but this paper presents a new meta-learning framework that is costs sensitive.

Phishing is the most prevalent method of cybercrime that convinces people to provide sensitive information; for instance, account IDs, passwords, and bank details. Emails, instant messages, and phone calls are widely used to launch such cyber-attacks. Despite constant updating of the methods of avoiding such cyber-attacks, the ultimate outcome is currently inadequate. On the other hand, phishing emails have increased exponentially in recent years, which suggests a need for more effective and advanced methods to counter them. Numerous methods have been established to filter phishing emails, but the problem still needs a complete solution. To the best of our knowledge, this is the first survey that focuses on using Natural Language Processing (NLP) and Machine Learning (ML) techniques to detect phishing emails. This study provides an analysis of the numerous state-of-the-art NLP strategies currently in use to identify phishing emails at various stages of the attack, with an emphasis on ML strategies. These approaches are subjected to a comparative assessment and analysis. This gives a sense of the problem, its immediate solution space, and the expected future research directions.

Phishing is the most prevalent method of cybercrime that convinces people to provide sensitive information; for instance, account IDs, passwords, and bank details. Emails, instant messages, and phone calls are widely used to launch such cyber-attacks. Despite constant updating of the methods of avoiding such cyber-attacks, the ultimate outcome is currently inadequate. On the other hand, phishing emails have increased exponentially in recent years, which suggests a need for more effective and advanced methods to counter them. Numerous methods have been established to filter phishing emails, but the problem still needs a complete solution. To the best of our knowledge, this is the first survey that focuses on using Natural Language Processing (NLP) and Machine Learning (ML) techniques to detect phishing emails. This study provides an analysis of the numerous state-of-the-art NLP strategies currently in use to identify phishing emails at various stages of the attack, with an emphasis on ML strategies. These approaches are subjected to a comparative assessment and analysis. This gives a sense of the problem, its immediate solution space, and the expected future research directions.

The successful application of deep learning has led to increasing expectations\ud of their use in embedded systems. This in turn, has created the need to find\ud ways of reducing the size of neural networks. Decreasing the size of a neural\ud network requires deciding which weights should be removed without compromising\ud accuracy, which is analogous to the kind of problems addressed by multi-arm\ud bandits. Hence, this paper explores the use of multi-armed bandits for reducing\ud the number of parameters of a neural network. Different multi-armed bandit\ud algorithms, namely e-greedy, win-stay, lose-shift, UCB1, KL-UCB, BayesUCB,\ud UGapEb, Successive Rejects and Thompson sampling are evaluated and their\ud performance compared to existing approaches. The results show that multi-\ud armed bandit pruning methods, especially those based on UCB, outperform other\ud pruning methods.

Phishing is a serious form of online fraud made up of spoofed websites that attempt to gain users’ sensitive information by tricking them into believing that they are visiting a legitimate site. Phishing attacks can be detected many ways, including a user's awareness of fraud protection, blacklisting websites, analyzing the suspected characteristics, or comparing them to recent attempts that followed similar patterns. The purpose of this paper is to create classification models using features extracted from websites to study and classify phishing websites. In order to train the system, we use two datasets consisting of 58,645 and 88,647 URLs labeled as “Phishing” or “Legitimate”. A diverse range of machine learning models such as “XGBOOST, Support Vector Machine (SVM), Random Forest (RF), k-nearest neighbor (KNN), Artificial neural network (ANN), Logistic Regression (LR), Decision tree (DT), and Gaussian naive Bayes (NB)” classifiers are evaluated. ANN provided the best performance with 97.63% accuracy for detecting phishing URLs in experiments. Such a study would be valuable to the scientific community, especially to researchers who work on phishing attack detection and prevention.