Epidemic Risk Assessment by a Novel Communication Station Based Method

Gu, Zhaoquan; Wang, Le; Chen, Xiaolong; Tang, Yunyi; Wang, Xingang; Du, Xiaojiang; Guizani, Mohsen; Tian, Zhihong

doi:10.1109/tnse.2021.3058762

Cited by 38 publications

(22 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the continuous advancement of artificial intelligence technology, more and more deep learning models are deployed on agents to solve problems in different fields, such as visual recognition (Ruiz-del-Solar et al, 2018;Gu et al, 2021), behavior supervision (Quan et al, 2018;Ganesan et al, 2021;Jia et al, 2021), and artificial assistance (Xiao et al, 2020), etc. Deep learning is defined as a scientific field involving complex functions (for example, non-linear dynamics) to train a multilayer neural network, embedding the data from the original, high-dimensional, multimodal state to the understandable state of the agent system (Goodfellow et al, 2016).…”

Section: Related Workmentioning

confidence: 99%

Public Opinion Early Warning Agent Model: A Deep Learning Cascade Virality Prediction Model Based on Multi-Feature Fusion

et al. 2021

View full text Add to dashboard Cite

With the rapid popularity of agent technology, a public opinion early warning agent has attracted wide attention. Furthermore, a deep learning model can make the agent more automatic and efficient. Therefore, for the agency of a public opinion early warning task, the deep learning model is very suitable for completing tasks such as popularity prediction or emergency outbreak. In this context, improving the ability to automatically analyze and predict the virality of information cascades is one of the tasks that deep learning model approaches address. However, most of the existing studies sought to address this task by analyzing cascade underlying network structure. Recent studies proposed cascade virality prediction for agnostic-networks (without network structure), but did not consider the fusion of more effective features. In this paper, we propose an innovative cascade virus prediction model named CasWarn. It can be quickly deployed in intelligent agents to effectively predict the virality of public opinion information for different industries. Inspired by the agnostic-network model, this model extracts the key features (independent of the underlying network structure) of an information cascade, including dissemination scale, emotional polarity ratio, and semantic evolution. We use two improved neural network frameworks to embed these features, and then apply the classification task to predict the cascade virality. We conduct comprehensive experiments on two large social network datasets. Furthermore, the experimental results prove that CasWarn can make timely and effective cascade virality predictions and verify that each feature model of CasWarn is beneficial to improve performance.

show abstract

Section: Related Workmentioning

confidence: 99%

Public Opinion Early Warning Agent Model: A Deep Learning Cascade Virality Prediction Model Based on Multi-Feature Fusion

et al. 2021

View full text Add to dashboard Cite

show abstract

“…In black-box attacks, an attacker knows nothing about the internal structure of the attacked model, training 2 Wireless Communications and Mobile Computing parameters, defense methods (if any defense methods are applied), or other information about the attacked model. The attackers can only interact with the model through input and output.…”

Section: Black-box Attacksmentioning

confidence: 99%

“…With the fast development of artificial intelligent technologies, deep learning models have been widely adopted in more and more areas [1][2][3]. In particular, they have been adopted not only in target detection, image classification, and other applications in the field of CV (Computer Vision) [4,5] but also in more and more NLP (Nature Language Processing) applications, such as sentiment classification, spam classification, and machine translation [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Exploring Security Vulnerabilities of Deep Learning Models by Adversarial Attacks

Han

et al. 2021

Wireless Communications and Mobile Computing

Self Cite

View full text Add to dashboard Cite

Nowadays, deep learning models play an important role in a variety of scenarios, such as image classification, natural language processing, and speech recognition. However, deep learning models are shown to be vulnerable; a small change to the original data may affect the output of the model, which may incur severe consequences such as misrecognition and privacy leakage. The intentionally modified data is referred to as adversarial examples. In this paper, we explore the security vulnerabilities of deep learning models designed for textual analysis. Specifically, we propose a visual similar word replacement (VSWR) algorithm to generate adversarial examples against textual analysis models. By using adversarial examples as the input of deep learning models, we verified that deep learning models are vulnerable to such adversarial attacks. We have conducted experiments on several sentiment analysis deep learning models to evaluate the performance. The results also confirmed that the generated adversarial examples could successfully attack deep learning models. As the number of modified words increases, the model prediction accuracy becomes lower. This kind of adversarial attack implies security vulnerabilities of deep learning models.

show abstract

“…On the other hand, disclosure of private keys increases the security risks of data such as PHRs (Personal Health Record) or even the information of the COVID-19 pandemic [21]. Liu et al [6] established a patient-centric framework in the multiauthority model and used signcryption to guarantee data security.…”

Section: Related Workmentioning

confidence: 99%

A Collusion-Resistant Blockchain-Enabled Data Sharing Scheme with Decryption Outsourcing under Time Restriction

Shen

Huang

Shen

et al. 2021

Security and Communication Networks

View full text Add to dashboard Cite

With the ever-increasing demands on decentralization and transparency of cloud storage, CP-ABE (Ciphertext Policy-Attribute-Based Encryption) has become a promising technology for blockchain-enabled data sharing methods due to its flexibility. However, real-world blockchain applications usually have some special requirements like time restrictions or power limitations. Thus, decryption outsourcing is widely used in data sharing scenarios and also causes concerns about data security. In this paper, we proposed a secure access control scheme based on CP-ABE, which could share contents during a particular time slot in blockchain-enabled data sharing systems. Specifically, we bind the time period with both ciphertexts and the keys to archive the goal of only users who have the required attributes in a particular time slot can decrypt the content. Besides, we use time slots as a token to protect the data and access control scheme when users want to outsource the decryption phase. The security analysis shows that our scheme can provide collusion resistance ability under a time restriction, and performance evaluations indicate that our scheme uses less time in decryption compared to other schemes while ensuring security.

show abstract

Epidemic Risk Assessment by a Novel Communication Station Based Method

Cited by 38 publications

References 47 publications

Public Opinion Early Warning Agent Model: A Deep Learning Cascade Virality Prediction Model Based on Multi-Feature Fusion

Public Opinion Early Warning Agent Model: A Deep Learning Cascade Virality Prediction Model Based on Multi-Feature Fusion

Exploring Security Vulnerabilities of Deep Learning Models by Adversarial Attacks

A Collusion-Resistant Blockchain-Enabled Data Sharing Scheme with Decryption Outsourcing under Time Restriction

Contact Info

Product

Resources

About