Research Article
Big Data-Driven Hierarchical Local Area Network Security Risk Event Prediction Algorithm
Table 4
Description of network node communication transmission volume.
| Steps | Algorithm consumption | Description code |
| 1 | For the filtered security | Import numpy as np | 2 | The event merging function | For (int i = 101; i < 200; i+ = 2) | 3 | A large number of security events | Def nms (dets, thresh): | 4 | Accuracy of security events | Public static void main (String[] args) { | 5 | The accuracy of fusion is lower | Int f1 = 1, f2 = 1, f; | 6 | Compared with centralized fusion | Int M = 30; | 7 | Communication and energy consumption | System.out.println (f1); | 8 | Data-driven hierarchical local area | X1 = dets[:, 0] | 9 | The merging rules are under | Y1 = dets[:, 1] | 10 | Connect the database through vulnerability | X2 = dets[:, 2] | 11 | Environmental condition error | Y2 = dets[:, 3] | 12 | It reduces the amount of | Scores = dets[:,4] | 13 | A group of variables representing | For (int i = 3; i < M; i++) { | 14 | Many duplicate or similar incidents | F = f2; | 15 | The false alarm rate and false alarm rate | F2 = f1 + f2; | 16 | Other methods have fewer feature | Order = scores.argsort()[-1] | 17 | Vulnerability category labels | Keep = [] | 18 | System command injection | While order.size > 0: | 19 | Access verification error | I = order[0] | 20 | It divides vulnerabilities into | Keep.append(i) |
|
|