DDoS Attack Detection System: Utilizing Classification Algorithms with Apache Spark
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cloud computing is a model of configurable computing resources such as servers, networks, storages, applications, and services that are available from anywhere at any time. In addition, cloud computing is managed by experts from different computer science fields to provide high reliability, availability, mobility, security, and scalability. Of course, security against all form of attacks, including DDoS attack, must be provided. Numerous DDoS attacks have been launched against different organizations in the last decade and numerous approaches have been proposed and tried to detect and prevent DDoS attacks by utilizing classification algorithms. In this research, we propose a DDoS detection system that benefits from cloud computing resources. Our proposed system consists of three concepts: classification algorithms, parallelism computing, and a fuzzy logic system. Classification algorithms are used in our system to classify and predict DDoS attacks on traffic packets. The parallelism concept is used to efficiently accelerate the execution of the utilized classification algorithms. The fuzzy logic is used to choose which of the classification algorithms is to be used next. We evaluated the classification algorithm and the parallel processing of the DDoS detection by configuring a test-bed that consists of one master and three slaves. We validated the fuzzy logic system by using the MATLAB statistical tool.
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.
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 | 0.001 |
| 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 it