Adversarial Machine Learning on Social Network: A Survey

Abstract

In recent years, machine learning technology has made great improvements in social networks applications such as social network recommendation systems, sentiment analysis, and text generation. However, it cannot be ignored that machine learning algorithms are vulnerable to adversarial examples, that is, adding perturbations that are imperceptible to the human eye to the original data can cause machine learning algorithms to make wrong outputs with high probability. This also restricts the widespread use of machine learning algorithms in real life. In this paper, we focus on adversarial machine learning algorithms on social networks in recent years from three aspects: sentiment analysis, recommendation system, and spam detection, We review some typical applications of machine learning algorithms and adversarial example generation and defense algorithms for machine learning algorithms in the above three aspects in recent years. besides, we also analyze the current research progress and prospects for the directions of future research.

Design, Modeling and Implementation of Digital Twins

“…Hence, even when the model is accurate and correct, how to protect the system from attacks against the modeling techniques or the data used to generate them is also unknown. Some authors [ 133 , 134 ] have proposed countermeasures to face AI attacks. However, if these algorithms are incorporated in critical infrastructures, further analyses should be required to evaluate if they are efficient and useful for DTs.…”

Sentiment Analysis of Financial Textual data Using Machine Learning and Deep Learning Models

“…DL has become ubiquitous in our daily lives, offering solutions that were once considered the subject matter of science [3]. The widespread use of machine learning and deep learning has made it possible to apply them in various fields, such as computer vision, machine translation, recommendation systems, cybersecurity, and sentiment analysis [4]. Sentiment analysis (SA) is also called Opinion analysis or Opinion mining, it is a subfield of natural language processing (NLP) that evaluates the degree of polarity in the sentence to analyze and extracts feelings from text data.…”