DDoS Attack Detection in Cloud Computing Based on Ensemble Feature Selection and Deep Learning
Bibliographic record
Abstract
Intrusion Detection System (IDS) in the cloud Computing (CC) environment has received paramount interest over the last few years. Among the latest approaches, Deep Learning (DL)-based IDS methods allow the discovery of attacks with the highest performance. In the CC environment, Distributed Denial of Service (DDoS) attacks are widespread. The cloud services will be rendered unavailable to legitimate end-users as a consequence of the overwhelming network traffic, resulting in financial losses. Although various researchers have proposed many detection techniques, there are possible obstacles in terms of detection performance due to the use of insignificant traffic features. Therefore, in this paper, a hybrid deep learning mode based on hybridizing Convolutional Neural Network (CNN) with Long-Short-Term Memory (LSTM) is used due to its robustness and efficiency in detecting normal and attack traffic. Besides, the ensemble feature selection, mutualization aggregation between Particle Swarm Optimizer (PSO), Grey Wolf Optimizer (PSO), Krill Hird (KH), and Whale Optimization Algorithm (WOA), is used to select the most important features that would influence the detection performance in detecting DDoS attack in CC. A benchmark dataset proposed by the Canadian Institute of Cybersecurity (CIC), called CICIDS 2017 is used to evaluate the proposed IDS. The results revealed that the proposed IDS outperforms the state-of-the-art IDSs, as it achieved 97.9%, 98.3%, 97.9%, 98.1%, respectively. As a result, the proposed IDS achieves the requirements of getting high security, automatic, efficient, and self-decision detection of DDoS attacks.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".