Domain generation algorithms (DGAs) are used in Botnets as rendezvous points to their command and control (C&C) servers, and can continuously provide a large number of domains which can evade detection by traditional methods such as Blacklist. Internet security vendors often use blacklists to detect Botnets and malwares, but the DGA can continuously update the domain to evade blacklist detection. In this paper, first, using features engineering; the three types of structural, statistical and linguistic features are extracted for the detection of DGAs, and then a new dataset is produced by using a dataset with normal DGAs and two datasets with malicious DGAs. Using supervised machine Learning algorithms, the classification of DGAs has been performed and the results have been compared to determine a DGA detection model with a higher accuracy and a lower error rate. The results obtained in this paper show that the random forest algorithm offers accuracy rate, detection rate and receiver operating characteristic (ROC) equal to 89. 32%, 91. 67% and 0. 889, respectively. Also, compared to the results of the other investigated algorithms, the random forest algorithm presents a lower false positive rate (FPR) equal to 0. 373.

Automatic topic detection seems unavoidable in social media analysis due to big text data which their users generate. Clustering-based methods are one of the most important and up-to-date categories in topic detection. The goal of this research is to have a wide study on this category. Therefore, this paper aims to study the main components of clustering-based-topic-detection, which are embedding methods, distance metrics, and clustering algorithms. Transfer Learning and consequently pretrained language models and word embeddings have been considered in recent years. Regarding the importance of embedding methods, the efficiency of five new embedding methods, from earlier to recent ones, are compared in this paper. To conduct our study, two commonly used distance metrics, in addition to five important clustering algorithms in the field of topic detection, are implemented by the authors. As COVID-19 has turned into a hot trending topic on social networks in recent years, a dataset including one-month tweets collected with COVID-19-related hashtags is used for this study. More than 7500 experiments are performed to determine tunable parameters. Then all combinations of embedding methods, distance metrics and clustering algorithms (50 combinations) are evaluated using Silhouette metric. Results show that T5 strongly outperforms other embedding methods, cosine distance is weakly better than other distance metrics, and DBSCAN is superior to other clustering algorithms.

Breast cancer has been the riskiest malignancy among ladies around the world. Nearly 2 million new cases were diagnosed in 2018. The main problem in the detection of breast cancer is to find how tumors turn into malignant or benign and we can do this with the help of machine Learning techniques as they provide an appropriate result. According to research, an experienced physician can diagnose cancer with 79% accuracy while using machine Learning techniques provides an accuracy of 91%. In this work, machine Learning techniques have been applied which include K-Nearest Neighbors algorithm (KNN), Support Vector Machine (SVM), and Decision Tree Classifier (DT). To predict whether the cause is benign or malignant we have used the breast cancer dataset. The SVM classifier gives more accurate and precise results as compared to others, and this classifier is trained with the larger datasets.

Flight planning, as one of the challenging issues in the industrial world, is faced with many uncertain conditions. One such condition is delay occurrence, which stems from various factors and imposes considerable costs on airlines, operators, and travelers. With these considerations in mind, we implemented flight delay prediction through the proposed approaches that were based on machine Learning algorithms. The parameters that enabled effective estimation of delay were identified and then, Bayesian modeling, decision tree, cluster classification, random forest, and hybrid method were applied to estimate the occurrences and magnitude of delay in a network. These methods were tested on a US flight dataset and then, refined for a large Iranian airline network. Results showed that the parameters affecting delay in US networks were visibility, wind, and departure time, whereas those affecting delay in the Iranian airline flights were fleet age and aircraft type. The proposed approaches exhibited an accuracy of more than 70% in calculating delay occurrence and magnitude for both the US and Iranian networks. It is hoped that the techniques put forward in this work will enable airline companies to accurately predict delays, improve flight planning, and prevent delay propagation.

Modern studies in the field of environment science and engineering show that deterministic models struggle to capture the relationship between the concentration of atmospheric pollutants and their emission sources. The recent advances in statistical modeling based on machine Learning approaches have emerged as solution to tackle these issues. It is a fact that, input variable type largely affect the performance of an algorithm, however, it is yet to be known why an algorithm is preferred over the other for a certain task. The work aims at highlighting the underlying principles of machine Learning techniques and about their role in enhancing the prediction performance. The study adopts, 38 most relevant studies in the field of environmental science and engineering which have applied machine Learning techniques during last 6 years. The review conducted explores several aspects of the studies such as: 1) the role of input predictors to improve the prediction accuracy; 2) geographically where these studies were conducted; 3) the major techniques applied for pollutant concentration estimation or forecasting; and 4) whether these techniques were based on Linear Regression, Neural Network, Support Vector Machine or Ensemble Learning algorithms. The results obtained suggest that, machine Learning techniques are mainly conducted in continent Europe and America. Furthermore a factorial analysis named multicomponent analysis performed show that pollution estimation is generally performed by using ensemble Learning and linear regression based approaches, whereas, forecasting tasks tend to implement neural networks and support vector machines based algorithms.

The steadily growing dependency over network environment introduces risk over information flow. The continuous use of various applications makes it necessary to sustain a level of security to establish safe and secure communication amongst the organizations and other networks that is under the threat of intrusions. The detection of Intrusion is the major research problem faced in the area of information security, the objective is to scrutinize threats or intrusions to secure information in the network Intrusion detection system (IDS) is one of the key to conquer against unfamiliar intrusions where intruders continuously modify their pattern and methodologies. In this paper authors introduces Intrusion detection system (IDS) framework that is deployed over KDD Cup99 dataset by using machine Learning algorithms as Support Vector Machine (SVM), Naï ve Bayes and Random Forest for the purpose of improving the precision, accuracy and recall value to compute the best suited algorithm.

In most surveys, the occupation and job-industry related questions are asked through open-ended questions, and the coding of this information into thousands of categories is done manually. This is very time consuming and costly. Given the requirement of modernizing the statistical system of countries, it is necessary to use statistical Learning methods in official statistics for primary and secondary data analysis. Statistical Learning classification methods are also useful in the process of producing official statistics. The purpose of this article is to code some statistical processes using statistical Learning methods and familiarize executive managers about the possibility of using statistical Learning methods in the production of official statistics. Two applications of classification statistical Learning methods, including automatic coding of economic activities and open-ended coding of statistical centres questionnaires using four iterative methods, are investigated. The studied methods include duplication, support vector machine (SVM) with multi-level aggregation methods, a combination of the duplication method and SVM, and the nearest neighbour method.