The Evaluation on the Credit Risk of Enterprises with the CNN-LSTM-ATT Model

Zhang, Lei

doi:10.1155/2022/6826573

Cited by 10 publications

(2 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This combination of accuracy and efficiency is crucial for real-time applications where timely anomaly detection is paramount. The model's ability to process vast amounts of data efficiently makes it well-suited for deployment in big data environments where processing speed is essential [17].  Deep learning emerges as a promising approach for building intelligent, real-time network traffic analytics systems.…”

Section: ) Discussionmentioning

confidence: 99%

Real-Time Detection of Network Traffic Anomalies in Big Data Environments Using Deep Learning Models

Arjunan

2024

IJRASET

View full text Add to dashboard Cite

In light of the increasing sophistication of cyberattacks and the rapid growth in network traffic, it is essential to detect network traffic anomalies or intrusions as they occur. Manual inspection is inefficient due to the large volume, speed, and variety network traffic data. This paper suggests using deep learning techniques in order to build intelligent models which can detect network traffic anomalies automatically within big data environments. We present a framework for anomaly detection using long-short-term memory models (LSTM) and convolutional neural network (CNN). The models are based on data extracted from packet captures. The models are evaluated on benchmark intrusion datasets as well as a large scale real network traffic dataset. The results show that deep learning models are able to detect anomalies more effectively than traditional shallow learning methods. Models can handle high-volume streaming data with low latency and in real time. To improve detection efficiency, we also propose optimization methods such as model compression and transfer learning. This work shows the effectiveness of deep learning for real-time anomaly detection within big data environments

show abstract

Section: ) Discussionmentioning

confidence: 99%

Real-Time Detection of Network Traffic Anomalies in Big Data Environments Using Deep Learning Models

Arjunan

2024

IJRASET

View full text Add to dashboard Cite

show abstract

“…CNN mainly consists of convolutions, pooling and full connections (FC). Convolutions and pooling are responsible for feature extraction, while FNN is used for classification recognition [29]. The structure is shown in Figure 3.…”

Section: Cnn Modelmentioning

confidence: 99%

Credit Risk Prediction Model for Listed Companies Based on CNN-LSTM and Attention Mechanism

Bing

et al. 2023

Electronics

View full text Add to dashboard Cite

The financial market has been developing rapidly in recent years, and the issue of credit risk concerning listed companies has become increasingly prominent. Therefore, predicting the credit risk of listed companies is an urgent concern for banks, regulators and investors. The commonly used models are the Z-score, Logit (logistic regression model), the kernel-based virtual machine (KVM) and neural network models. However, the results achieved could be more satisfactory. This paper proposes a credit-risk-prediction model for listed companies based on a CNN-LSTM and an attention mechanism, Our approach is based on the benefits of the long short-term memory network (LSTM) model for long-term time-series prediction combined with a convolutional neural network (CNN) model. Furthermore, the advantages of being integrated into a CNN-LSTM model include reducing the complexity of the data, improving the calculation speed and training speed of the model and solving the possible lack of historical data in the long-term sequence prediction of the LSTM model, resulting in prediction accuracy. To reduce problems, we introduced an attention mechanism to assign weights independently and optimize the model. The results show that our model has distinct advantages compared with other CNNs, LSTMs, CNN-LSTMs and other models. The research on the credit-risk prediction of the listing formula has significant meaning.

show abstract

Systematic Literature Review and Bibliometric Analysis on Addressing the Vanishing Gradient Issue in Deep Neural Networks for Text Data

Haroon-Sulyman,

Taiye,

Kamaruddin

et al. 2024

Communications in Computer and Information Science

View full text Add to dashboard Cite

The Evaluation on the Credit Risk of Enterprises with the CNN-LSTM-ATT Model

Cited by 10 publications

References 35 publications

Real-Time Detection of Network Traffic Anomalies in Big Data Environments Using Deep Learning Models

Real-Time Detection of Network Traffic Anomalies in Big Data Environments Using Deep Learning Models

Credit Risk Prediction Model for Listed Companies Based on CNN-LSTM and Attention Mechanism

Systematic Literature Review and Bibliometric Analysis on Addressing the Vanishing Gradient Issue in Deep Neural Networks for Text Data

Contact Info

Product

Resources

About