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.…”

Recent Machine Learning and Deep Learning Theories and Methods for COVID-19 Diagnosis

“…As shown in Figure 1, machine learning (ML) is a subset of artificial intelligence (AI) that involves the development of algorithms and models that enable computers to learn from and make predictions [9] or decisions based on data. Instead of being explicitly programmed to perform specific tasks, ML systems use data to identify patterns, make inferences, and improve their performance over time [10]. This technology is widely used in various applications, from natural language processing and image recognition to recommendation systems [11] and autonomous vehicles [12].…”

Recent Machine Learning and Deep Learning Theories and Methods for COVID-19 Diagnosis

“…Instead of being explicitly programmed to perform specific tasks, ML systems use data to identify patterns, make inferences, and improve their performance over time [10]. This technology is widely used in various applications, from natural language processing and image recognition to recommendation systems [11] and autonomous vehicles [12]. It plays a pivotal role in automating complex tasks, extracting insights from large datasets, and enhancing decision-making processes across many industries.…”

“…Additionally, data quality and consistency across healthcare institutions can vary, leading to potential biases in the models. Further, the raw data of the dataset provided by the healthcare organization can easily lead the machine learning algorithms to make wrong judgments with a high probability when perturbations imperceptible to the human eye are added [11].…”