• No results found

Network Anomaly Detection Based on Wavelet Analysis

N/A
N/A
Protected

Academic year: 2020

Share "Network Anomaly Detection Based on Wavelet Analysis"

Copied!
16
0
0

Loading.... (view fulltext now)

Full text

Loading

Figure

Table 1: List of features.
Figure 5: Residuals for number of flows per minute; from left to right is Figures 5(a), 5(b), and 5(c) representing TCP, UDP, and ICMPflows, respectively.
Table 4: Number of attack instances detected for each attack typefor W5D1.
Table 7: Number of attack instances detected for each attack type by different basis functions.

References

Related documents

This Code of Practice is a key component of the information security management framework that replaces prior Information Management and Technology security guidance published by

We provide an empirical application of the model, in which we show by means of stochastic dominance tests that the returns from an optimal portfolio based on the model’s

12.1 The Organizing Committee shall establish simplified customs procedures through the customs consultative committee, and details of the applicable custom

Tandberg Data offers a powerful combination to meet the needs of SMBs looking for a tape solution to address the dual demons of rising energy costs and growing data

If you have memory problems or hallucinations and your doctor has pre- scribed you an ‘anti-cholinergic’ medication, there is a chance that these medications can make these

Therefore, a highly important requirement was to develop a performance appraisal instrument that would reflect the way experienced supervisors in the Navy concep- tualized

certified professional Engineers SHOULD all have their personal digital signature in order have their personal digital signature in order to conduct their own professional

Specifically, we find a prediction gain (defined as the ratio of the fraction of predicted events over the fraction of time in alarms) equal to 21 for a fraction of alarm of 1%,