Text Classification problem has been thoroughly studied in information retrieval problems and data mining tasks. It is beneficial in multiple tasks including medical diagnose health and care department, targeted marketing, entertainment industry, and group filtering processes. A recent innovation in both data mining and natural language processing gained the attention of researchers from all over the world to develop automated systems for text classification. NLP allows categorizing documents containing different texts. A huge amount of data is generated on social media sites through social media users. Three datasets have been used for experimental purposes including the COVID-19 fake news dataset, COVID-19 English tweet dataset, and extremist-non-extremist dataset which contain news blogs, posts, and tweets related to coronavirus and hate speech. Transfer learning approaches do not experiment on COVID-19 fake news and extremist-non-extremist datasets. Therefore, the proposed work applied transfer learning classification models on both these datasets to check the performance of transfer learning models. Models are trained and evaluated on the accuracy, precision, recall, and F1-score. Heat maps are also generated for every model. In the end, future directions are proposed.

/pdf/a-fine-tuned-bert-based-transfer-learning-approach-for-text-3tj7rl4p.pdf

A Fine-Tuned BERT-Based Transfer Learning Approach for Text Classification

Machine learning has become widely adopted as a strategy for dealing with a variety of cybersecurity issues, ranging from insider threat detection to intrusion and malware detection. However, by their very nature, machine learning systems can introduce vulnerabilities to a security defence whereby a learnt model is unaware of so-called adversarial examples that may intentionally result in mis-classification and therefore bypass a system. Adversarial machine learning has been a research topic for over a decade and is now an accepted but open problem. Much of the early research on adversarial examples has addressed issues related to computer vision, yet as machine learning continues to be adopted in other domains, then likewise it is important to assess the potential vulnerabilities that may occur. A key part of transferring to new domains relates to functionality-preservation, such that any crafted attack can still execute the original intended functionality when inspected by a human and/or a machine. In this literature survey, our main objective is to address the domain of adversarial machine learning attacks and examine the robustness of machine learning models in the cybersecurity and intrusion detection domains. We identify the key trends in current work observed in the literature, and explore how these relate to the research challenges that remain open for future works. Inclusion criteria were: articles related to functionality-preservation in adversarial machine learning for cybersecurity or intrusion detection with insight into robust classification. Generally, we excluded works that are not yet peer-reviewed; however, we included some significant papers that make a clear contribution to the domain. There is a risk of subjective bias in the selection of non-peer reviewed articles; however, this was mitigated by co-author review. We selected the following databases with a sizeable computer science element to search and retrieve literature: IEEE Xplore, ACM Digital Library, ScienceDirect, Scopus, SpringerLink, and Google Scholar. The literature search was conducted up to January 2022. We have striven to ensure a comprehensive coverage of the domain to the best of our knowledge. We have performed systematic searches of the literature, noting our search terms and results, and following up on all materials that appear relevant and fit within the topic domains of this review. This research was funded by the Partnership PhD scheme at the University of the West of England in collaboration with Techmodal Ltd.

https://www.mdpi.com/2624-800X/2/1/10/pdf?version=1647919586

Functionality-Preserving Adversarial Machine Learning for Robust Classification in Cybersecurity and Intrusion Detection Domains: A Survey

Nowadays, social media is one widely used platform to access information. Fake news on social media and various other media is widely spreading. It is a matter of serious concern due to its ability to cause a lot of social and national damage with destructive impacts. Therefore, detecting misleading news is critical to detect automatically. Fake news detection software has been used in a variety of fields, such as social media, health, political news, etc. This paper presents the Instituto Politécnico Nacional (Mexico) at CheckThat! 2022. In this paper, we discuss using different algorithms for the multiclass and cross-lingual fake news detection task. We achieved a macro F1-score of 28.60% for a mono-lingual task in English (task 3a) using RoBERTa pre-trained model and 17.21% for a cross-lingual task for English and German (task 3b) using Bi-LSTM deep learning algorithm.

CIC at CheckThat!-2022: Multi-class and Cross-lingual Fake News Detection

Content-Based Fake News Detection With Machine and Deep Learning: a Systematic Review

Machine learning models are usually based on a single dataset and a single architecture approach. But this approach falls behind in many practical scenarios. Many techniques such as boosting and bagging have been developed in the past that enables better predictions by ensembling multiple models. In this paper, we propose an extension to the existing ensembling techniques by developing a community-driven system of distributed API that unifies several models to produce an ensembling effect along with several other benefits such as wider availability, greater usability, lesser resource constraints and an infrastructure for future developments in the field.

A distributed and unified API service for machine learning models

The fake news on social media and various other media is wide spreading and is a matter of serious concern due to its ability to cause a lot of social and national damage with destructive impacts. A lot of research is already focused on detecting it. This paper makes an analysis of the research related to fake news detection and explores the traditional machine learning models to choose the best, in order to create a model of a product with supervised machine learning algorithm, that can classify fake news as true or false, by using tools like python scikit-learn, NLP for textual analysis. This process will result in feature extraction and vectorization; we propose using Python scikit-learn library to perform tokenization and feature extraction of text data, because this library contains useful tools like Count Vectorizer and Tiff Vectorizer. Then, we will perform feature selection methods, to experiment and choose the best fit features to obtain the highest precision, according to confusion matrix results.

H Sirafi

Papers

Fake News Detection Using Machine Learning Approaches