Detection Syn Flood and UDP Lag Attacks Based on Machine Learning Using AdaBoost
Bibliographic record
Abstract
Syn flood is a commonly used Distributed Denial-of-Service (DDoS) attack that aims to overwhelm a server by sending a large number of Transmission Control Protocol (TCP) SYN requests without completing the handshake process and rejecting user packets. On the other hand, UDP flood attacks target the network infrastructure rather than the server, making it difficult to identify the source of the attack. In recent years, research related using machine learning to detect Distributed Denial-of-Service (DDoS) attacks with different methods. Steps taken to detect SYN Flood and UDP Lag attacks are system design, data collection, search, and data analysis. Test metrics such as precision, recall, accuracy, and F1-score are closely related to machine learning algorithms used for detecting Distributed Denial-of-Service (DDoS) attacks, including SYN Flood and UDP Lag attacks. Some literatures on SYN Flood attack detection have a low accuracy value with algorithm has been used. With the research conducted by author in detecting attacks by designing and building a system using machine learning algorithm which include ensemble Learning using AdaBoost and CICDDoS2019 dataset. For AdaBoost is an ensemble algorithm boosting type classifier that each individual model has its own way self to build sequentially by repeating the previous and CICDDoS2019 dataset was created by the Canadian Institute for Cybersecurity (CIC) at the University of New Brunswick.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 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".