Detecting Network Attacks Exhibiting Irregular Periodic Behavior
Bibliographic record
Abstract
There are several Internet applications that exhibit periodic behavior. Some of these applications are benign such as E-mail and software updates, while others are used to release malicious activities such as botnet command and control (C&C) traffic. Therefore, the detection of periodic behavior can lead to the detection of malicious activities that threatens the security of computer network. In this paper, we study the detection of periodic behavior of network traffic. We analyze the packet sequences extracted from network traffic. We estimate the power spectral density (PSD) of the packet count sequences using periodograms. We examine a statistical model that uniformly randomizes the value of the period within a certain range. We validate the detection approach using various types of datasets; Generated data, HULK DoS traffic obtained from the Canadian’s CICIDS2017 dataset, and Botnet IRC C&C traffic obtained from King Saud University’s network. Our results demonstrate the detection approach can detect periodic behavior whether the period is regular or irregular within a certain range.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 source (direct Gemma or distilled Codex), 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".