Improving ransomware detection based on portable executable header using xception convolutional neural network

All malware are harmful to computer systems;however, crypto-ransomware specifically leads to irreparable data loss and causes substantial economic prejudice. Ransomware attacks increased significantly during the COVID-19 pandemic, and because of its high profitability, this growth will likely persist. To respond to these attacks, we apply static analysis to detect ransomware by converting Portable Executable (PE) header files into color images in a sequential vector pattern and classifying these via Xception Convolutional Neural Network (CNN) model without transfer learning, which we call Xception ColSeq. This approach simplifies feature extraction, reduces processing load, and is more resilient against evasion techniques and ransomware evolution. The proposed method was evaluated using two datasets. The first contains 1000 ransomware and 1000 benign applications, on which the model achieved an accuracy of 93.73%, precision of 92.95%, recall of 94.64%, and F-measure of 93.75%. The second dataset, which we created and have made available, contains 1023 ransomware, grouped in 25 still active and relevant families, and 1134 benign applications, on which the proposed method achieved an accuracy of 98.20%, precision of 97.50%, recall of 98.76%, and F-measure of 98.12%. Furthermore, we refined a testing methodology for a particular case of zero-day ransomware attacks detection—the detection of new ransomware families—by adding an adequate amount of randomly selected benign applications to the test set, providing representative evaluation performance metrics. These results represent an improvement over the performance of the current methods reported in the literature. Our advantageous approach can be applied as a technique for ransomware detection to protect computer systems from cyber threats. © 2023 Elsevier Ltd

Sentimental Analysis of COVID-19 Vaccine Tweets Using BERT+NBSVM

The development of the vaccine for the control of COVID-19 is the need of hour. The immunity against coronavirus highly depends upon the vaccine distribution. Unfortunately, vaccine hesitancy seems to be another big challenge worldwide. Therefore, it is necessary to analysis and figure out the public opinion about COVID-19 vaccines. In this era of social media, people use such platforms and post about their opinion, reviews etc. In this research, we proposed BERT+NBSVM model for the sentimental analysis of COVID-19 vaccines tweets. The polarity of the tweets was found using TextBlob(). The proposed BERT+NBSVM outperformed other models and achieved 73% accuracy, 71% precision, 88% recall and 73% F-measure for classification of positive sentiments while 73% accuracy, 71% precision, 74% recall and 73% F-measure for classification of negative sentiments respectively. Thus, these sentimental and spatial analysis helps in world-wide pandemics by identify the people's attitudes towards the vaccines. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Triggers and Tweets: Implicit Aspect-Based Sentiment and Emotion Analysis of Community Chatter Relevant to Education Post-COVID-19

This research proposes a well-being analytical framework using social media chatter data. The proposed framework infers analytics and provides insights into the public’s well-being relevant to education throughout and post the COVID-19 pandemic through a comprehensive Emotion and Aspect-based Sentiment Analysis (ABSA). Moreover, this research aims to examine the variability in emotions of students, parents, and faculty toward the e-learning process over time and across different locations. The proposed framework curates Twitter chatter data relevant to the education sector, identifies tweets with the sentiment, and then identifies the exact emotion and emotional triggers associated with those feelings through implicit ABSA. The produced analytics are then factored by location and time to provide more comprehensive insights that aim to assist the decision-makers and personnel in the educational sector enhance and adapt the educational process during and following the pandemic and looking toward the future. The experimental results for emotion classification show that the Linear Support Vector Classifier (SVC) outperformed other classifiers in terms of overall accuracy, precision, recall, and F-measure of 91%. Moreover, the Logistic Regression classifier outperformed all other classifiers in terms of overall accuracy, recall, an F-measure of 81%, and precision of 83% for aspect classification. In online experiments using UAE COVID-19 education-related data, the analytics show high relevance with the public concerns around the education process that were reported during the experiment’s timeframe. © 2022 by the authors.

Combining Linear Regressions to Determine the Future of the Covid in Mexico from the News

This paper presents an approach to determine the Semaphore Covid in Mexico from the news to participate in the Rest-Mex 2022 evaluation forum. The purpose of the task is to determine the covid semaphore color (red, orange, yellow, and green) in different time spaces. The proposed approach consists of two main steps. First, to generate a list of topics of the news, and second, to implement several linear regressions methods in order to these results serve to feed a deep neural network. For the first step, the LDA algorithm was implemented, and for the second, well-known methods such as Lasso, Ridge, Lars, among others, were utilized. With this approach, a weighted average of 0.48 was obtained, which is considerably higher than the baseline proposed by the organizers, which is 0.12. The best result to classify the semaphore was two weeks in the future with 0.56 of F-measure. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

Semantic-based Thai Recipe Recommendation

Nowadays, people are constantly affected by epidemics such as COVID-19. To reduce the risk of acquiring germs in the community, people's lifestyles have been changed, and they are more inclined to cook for themselves. Typically, people can usually quickly and easily find recipe information via websites and applications. The resulting recipes consist of ingredients as specified by the user. Unfortunately, users often have ingredients that disappear in available cooking recipes. This makes the system is unable to recommend all relevant recipes to users, although the users can use the existing ingredients instead of the ingredients specified in the recipes. Based on this limitation, this research proposes a semantic-based Thai cooking recipe recommendation system which can recommend recipes based on the ingredient substitutes. This research uses existing Thai food ontology to retrieve substitute ingredients based on three different ingredient properties, such as smell, taste, and texture. To recommend cooking recipes, the system expands the given user queries with substitute ingredients and then calculates similarities between all queries and each cooking recipe. Recipes with high similarities are presented and ranked to users. To evaluate the performances, precision, recall and f-measure are applied. The experiments demonstrate that the proposed method performs well with 0.96, 0.72, and 0.82 in precision, recall, and f-measure respectively. © 2022 IEEE.