Comparative Efficiency Evaluation of Hadoop and Spark Frameworks Using Random Forest Algorithm for Intrusion Detection
Bibliographic record
Abstract
This study uses the Random Forest algorithm to evaluate the efficiency of Hadoop, and Spark distributed computing systems for intrusion detection, highlighting the growing importance of efficient distributed systems in handling big data.This research aims to assess and compare the performance of Hadoop and Spark in the context of an intelligent intrusion detection system.We use the Random Forest machine learning algorithm to train and test the system.The methods developed an intrusion detection system using Hadoop and Spark frameworks, followed by a thorough performance assessment using a real-world dataset.The problem this study tackles is the ever-increasing demand for processing data swiftly and accurately in a distributed fashion.We aim to identify the strengths and weaknesses of Hadoop and Spark in the context of machine learning-based intrusion detection.The "intelligent network detection system for intrusions" in this study uses a sophisticated security system using machine learning algorithms to detect potential intrusions, assessing Hadoop and Spark's performance in realworld scenarios and handling large-scale data processing.The findings provide insightful information about the efficacy and efficiency of distributed systems in machine learning activities, which can help select big data application frameworks.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".