Detecting Cyber Threat Event from Twitter Using IDCNN and BiLSTM

Fang, Yong; Gao, Jinwei; Liu, Zhonglin; Huang, Cheng

doi:10.3390/app10175922

Cited by 20 publications

(4 citation statements)

References 27 publications

(28 reference statements)

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“…This paper stacks four convolutional blocks of the same size, with a convolutional kernel count of 128. In order to better filter useful text, a layer of gating units is added at the end of each convolutional block, allowing the model to assign larger weights to more useful features [12] .…”

Section: Iterated Dilated Convolutional Neural Network (Idcnn)mentioning

confidence: 99%

Research on extractive text abstract generation method for graph model based on TextRank

Dong

2024

Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023)

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The traditional TextRank algorithm is based on a graph model, and the extracted abstract generated by it does not deviate from the source text, but there may be issues such as polysemy. The BERT model can capture contextual semantics well. It can directly convert sentences in text into vector form, which can better solve the problem of polysemy. BiLSTM can obtain further information, IDCNN can better handle overfitting problems compared to traditional convolutional neural networks, and CRF can better solve the problem of sentence coherence. The model proposed in this article has been compared with other extraction based text summarization methods on the TTNews dataset, and experiments have shown that the method proposed in this article can obtain meaningful text summaries with significant optimization.

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Section: Iterated Dilated Convolutional Neural Network (Idcnn)mentioning

confidence: 99%

Research on extractive text abstract generation method for graph model based on TextRank

Dong

2024

Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023)

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“…Although the most widely used convolution neural network (CNN) (Aslan et al, 2021) has obvious computational advantages in the named entity recognition of MNP documents, the traditional CNN can only obtain a small part of the input text information after convolution. In order to obtain contextual information, more convolutional layers need to be added, resulting in deeper networks, more parameters, and being prone to overfitting (Fang et al, 2020). Inflated convolutional neural network (IDCNN) (Li et al, 2022b) adds convolution holes to CNN, which enables IDCNN to control its sliding window to omit inputs of a specific length range, which can reduce the number of convolutional layers to better capture sentence context information and greatly improve the efficiency of parallel computing (Wang and Xu, 2017).…”

Section: Related Workmentioning

confidence: 99%

Research on named entity recognition method of marine natural products based on attention mechanism

Yu²,

Chen³

et al. 2023

Front. Chem.

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Marine natural product (MNP) entity property information is the basis of marine drug development, and this entity property information can be obtained from the original literature. However, the traditional methods require several manual annotations, the accuracy of the model is low and slow, and the problem of inconsistent lexical contexts cannot be solved well. In order to solve the aforementioned problems, this study proposes a named entity recognition method based on the attention mechanism, inflated convolutional neural network (IDCNN), and conditional random field (CRF), combining the attention mechanism that can use the lexicality of words to make attention-weighted mentions of the extracted features, the ability of the inflated convolutional neural network to parallelize operations and long- and short-term memory, and the excellent learning ability. A named entity recognition algorithm model is developed for the automatic recognition of entity information in the MNP domain literature. Experiments demonstrate that the proposed model can properly identify entity information from the unstructured chapter-level literature and outperform the control model in several metrics. In addition, we construct an unstructured text dataset related to MNPs from an open-source dataset, which can be used for the research and development of resource scarcity scenarios.

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“…Social network analysis is performed to explore such intriguing knowledge. In the physical world, social network analysis is utilized in job searching [4], studying urban life psychology, investigation of guilt association [12], finding communities [40], spreading of news [41], and influential networks [18,42]. In the recent era of information and technologies, massive logs are generating for each person, e.g., call records, bank transactions, online purchase records, daily emails, CCTV cameras, and much more mediums [7,43,44].…”

Section: Scientific Programmingmentioning

confidence: 99%

Inferring Ties in Social IoT Using Location-Based Networks and Identification of Hidden Suspicious Ties

Khan

Zhang

Zhou

et al. 2020

Scientific Programming

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Stochastic Internet of Things (IoT)-based communication behavior of the progressing world is tremendously impacting social networks. The growth of social networks helps to quantify the effect on the Social Internet of Things (SIoT). Multiple existences of two persons at several geographical locations in different time frames hint to predict the social connection. We investigate the extent to which social ties between people can be inferred by critically reviewing the social networks. Our study used Chinese telecommunication-based anonymized caller data records (CDRs) and two openly available location-based social network data sets, Brightkite and Gowalla. Our research identified social ties based on mobile communication data and further exploits communication reasons based on geographical location. This paper presents an inference framework that predicts the missing ties as suspicious social connections using pipe and filter architecture-based inference framework. It highlights the secret relationship of users, which does not exist in real data. The proposed framework consists of two major parts. Firstly, users’ cooccurrence based on the mutual location in a specific time frame is computed and inferred as social ties. Results are investigated based upon the cooccurrence count, the gap time threshold values, and mutual friend count values. Secondly, the detail about direct connections is collected and cross-related to the inferred results using Precision and Recall evaluation measures. In the later part of the research, we examine the false-positive results methodically by studying the human cooccurrence patterns to identify hidden relationships using a social activity. The outcomes indicate that the proposed approach achieves comprehensive results that further support the theory of suspicious ties.

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Detecting Cyber Threat Event from Twitter Using IDCNN and BiLSTM

Cited by 20 publications

References 27 publications

Research on extractive text abstract generation method for graph model based on TextRank

Research on extractive text abstract generation method for graph model based on TextRank

Research on named entity recognition method of marine natural products based on attention mechanism

Inferring Ties in Social IoT Using Location-Based Networks and Identification of Hidden Suspicious Ties

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