A threat detection scheme for financial big data in internet of things
Bibliographic record
Abstract
With the deep application of Internet of Things (IoT) technology in the financial field, the transmission, storage and processing of massive financial data face complex and diverse security threats. This paper proposes a threat detection scheme, CNN - BiLSTM - GAM, which is based on the vulnerabilities of IoT devices in financial big data scenarios and deep learning algorithms. By analyzing the traffic data and behavioral patterns generated by IoT devices during data collection and other processes, it extracts key features and identifies security threats such as malicious attacks. CNN-BiLSTM-GAM includes Convolutional Neural Network (CNN), Bidirectional long short-term memory (BiLSTM) and global attention module (GAM), which accurately extract spatial features of input financial data through one-dimensional convolutional neural network (1D-CNN). At the same time, BiLSTM layer captures the context dependency relationship in time series data through forward and backward networks. It optimizes the extraction of temporal features, finally assigns weights to input features through the global attention obtained by concatenating channel attention and spatial attention. The experimental results show that CNN-BiLSTM-GAM performs well with 96.81% of ACC and 96.79% of F1 on NSL-KDD, 96.98% of ACC and 96.46% of F1 on CICIDS2017, demonstrating better spatiotemporal feature extraction capabilities and providing technical support for ensuring the security of financial big data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".