Gender Forecast Based on the Information about People Who Violated Traffic Principle

Rui, Li; Sun, Guodao; Ying, Jiang; Sun, Rui; Li, Haixia; Peng, Guixiang; Jian-jun, Zhang

doi:10.32604/jiot.2020.09868

Cited by 5 publications

(5 citation statements)

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“…Although the classifier based on ensemble learning has relatively high accuracy, the classifier based on ensemble learning has a disadvantage compared with a single classifier, which is significantly longer training time. In the case of abundant hardware resources, CNN has obvious advantages regardless of whether the sparse matrix is compressed [23][24][25][26].…”

Section: Discussionmentioning

confidence: 99%

Malicious Encryption Traffic Detection Based on NLP

Yang

Liu

et al. 2021

Security and Communication Networks

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The development of Internet and network applications has brought the development of encrypted communication technology. But on this basis, malicious traffic also uses encryption to avoid traditional security protection and detection. Traditional security protection and detection methods cannot accurately detect encrypted malicious traffic. In recent years, the rise of artificial intelligence allows us to use machine learning and deep learning methods to detect encrypted malicious traffic without decryption, and the detection results are very accurate. At present, the research on malicious encrypted traffic detection mainly focuses on the characteristics’ analysis of encrypted traffic and the selection of machine learning algorithms. In this paper, a method combining natural language processing and machine learning is proposed; that is, a detection method based on TF-IDF is proposed to build a detection model. In the process of data preprocessing, this method introduces the natural language processing method, namely, the TF-IDF model, to extract data information, obtain the importance of keywords, and then reconstruct the characteristics of data. The detection method based on the TF-IDF model does not need to analyze each field of the data set. Compared with the general machine learning data preprocessing method, that is, data encoding processing, the experimental results show that using natural language processing technology to preprocess data can effectively improve the accuracy of detection. Gradient boosting classifier, random forest classifier, AdaBoost classifier, and the ensemble model based on these three classifiers are, respectively, used in the construction of the later models. At the same time, CNN neural network in deep learning is also used for training, and CNN can effectively extract data information. Under the condition that the input data of the classifier and neural network are consistent, through the comparison and analysis of various methods, the accuracy of the one-dimensional convolutional network based on CNN is slightly higher than that of the classifier based on machine learning.

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Section: Discussionmentioning

confidence: 99%

Malicious Encryption Traffic Detection Based on NLP

Yang

Liu

et al. 2021

Security and Communication Networks

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“…Subjective difference exploration: In previous studies, gender affected the subjective estimate of mental workload and other information processes [23]. It was found that female subjects rated effort and frustration significantly higher, and their performance was significantly lower than that of their male counterparts.…”

Section: Design Goal and Methodsmentioning

confidence: 96%

Measuring Mental Workload Using ERPs Based on FIR, ICA, and MARA

Sun¹,

Ding²,

Jiang³

et al. 2022

Computer Systems Science and Engineering

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Mental workload is considered to be strongly linked to human performance, and the ability to measure it accurately is key for balancing human health and work. In this study, brain signals were elicited by mental arithmetic tasks of varying difficulty to stimulate different levels of mental workload. In addition, a finite impulse response (FIR) filter, independent component analysis (ICA), and multiple artifact rejection algorithms (MARAs) were used to filter event-related potentials (ERPs). Then, the data consisting of ERPs, subjective ratings of mental workload, and task performance, were analyzed through the use of variance and Spearman's correlation during a simulated computer task. We found that participants responded faster and performed better in the easy task condition, followed by the medium and high-difficulty conditions, which verifies the validity of the ERP filtering. Moreover, larger P2 and P3 waveforms were evoked as the task difficulty increased, and a higher task difficulty elicited a more enhanced N300. Correlation analysis revealed a negative relationship between the amplitude of P3 and the subjective ratings, and a positive relationship between the P3 amplitude and accuracy. The results presented in this paper demonstrate that a combination of FIR, ICA, and MARA methods can filter ERPs in the non-invasive real-time measurement of workload. Additionally, frontocentral P2, N3, and parietal P3 components showed differences between genders. The proposed measurement of mental workload can be useful for real-time identification of mental states and can be applied to human-computer interaction in the future.

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“…1. (1) The SP first releases the initial model, master public key, and testing dataset. (2) Each user can generate its own encryption key based on master public key and its local information.…”

Section: System Architecturementioning

confidence: 99%

“…As a result of the rapid development of deep neural networks, data-driven artificial intelligence has been widely used in smart transportation [1,2], Internet of Things [3,4], smart grid [5,6] and financial applications [7,8]. Accuracy in data analytics depends not only on the volume of training data but also on the diversity of training data.…”

Section: Introductionmentioning

confidence: 99%

Fed-DFE: A Decentralized Function Encryption-Based Privacy-Preserving Scheme for Federated Learning

Habib¹,

Kiani²,

Butt³

et al. 2022

Computers, Materials &Amp; Continua

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Federated learning is a distributed learning framework which trains global models by passing model parameters instead of raw data. However, the training mechanism for passing model parameters is still threatened by gradient inversion, inference attacks, etc. With a lightweight encryption overhead, function encryption is a viable secure aggregation technique in federation learning, which is often used in combination with differential privacy. The function encryption in federal learning still has the following problems: a) Traditional function encryption usually requires a trust third party (TTP) to assign the keys. If a TTP colludes with a server, the security aggregation mechanism can be compromised. b) When using differential privacy in combination with function encryption, the evaluation metrics of incentive mechanisms in the traditional federal learning become invisible. In this paper, we propose a hybrid privacy-preserving scheme for federated learning, called Fed-DFE. Specifically, we present a decentralized multi-client function encryption algorithm. It replaces the TTP in traditional function encryption with an interactive key generation algorithm, avoiding the problem of collusion. Then, an embedded incentive mechanism is designed for function encryption. It models the real parameters in federated learning and finds a balance between privacy preservation and model accuracy. Subsequently, we implemented a prototype of Fed-DFE and evaluated the performance of decentralized function encryption algorithm. The experimental results demonstrate the effectiveness and efficiency of our scheme.

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Gender Forecast Based on the Information about People Who Violated Traffic Principle

Cited by 5 publications

References 0 publications

Malicious Encryption Traffic Detection Based on NLP

Malicious Encryption Traffic Detection Based on NLP

Measuring Mental Workload Using ERPs Based on FIR, ICA, and MARA

Fed-DFE: A Decentralized Function Encryption-Based Privacy-Preserving Scheme for Federated Learning

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