Comparing FTP and SSH Password Brute Force Attack Detection using k-Nearest Neighbour (k-NN) and Decision Tree in Cloud Computing
Bibliographic record
Abstract
Cloud computing represents a new epoch in computing. From huge enterprises to individual use, cloud computing always provides an answer. Therefore, cloud computing must be readily accessible and scalable, and customers must pay only for the resources they consume rather than for the entire infrastructure. With such conveniences, come with their own threat especially brute force attacks since the resources are available publicly online for the whole world to see. In a brute force attack, the attacker attempts every possible combination of username and password to obtain access to the system. This study aims to examine the performance of the k-Nearest Neighbours (k-NN) and Decision Tree algorithms by contrasting their precision, recall, and F1 score. This research makes use of the CICIDS2017 dataset, which is a labelled dataset produced by the Canada Institute for Cybersecurity. A signature for the brute force attack is utilised with an Intrusion Detection System (IDS) to detect the attack. This strategy, however, is ineffective when a network is being attacked by a novel or unknown attack or signature. At the conclusion of the study, the performance of both algorithms is evaluated by comparing their precision, recall, and f1 score. The results show that Decision Tree performs slightly better than k-NN at classifying FTP and SSH attacks.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".