DDoS Attack Detection Using Smart IDS
Bibliographic record
Abstract
Among the major threats to network security are the Distributed Denial of Service (DDoS) attacks, leading to significant disruptions and financial losses across various industries. Traditional Intrusion Detection Systems (IDS) often fall short in effectively identifying and countering these attacks due to their increasing complexity and scale. To address this challenge, this paper presents a novel approach to DDoS detection by incorporating Suricata, an open-source IDS, with Machine Learning (ML) techniques, thereby creating a Smart IDS. This integration enhances Suricata's robust rule-based detection capabilities with the predictive power of ML models, resulting in more precise and timely identification of DDoS threats. The system's architecture was evaluated using the extensive CIC2023 dataset, supplied by researchers from the Canadian Institute for Cybersecurity (CIC), which contained both benign and malicious traffic. The analysis focused on features like packet rate, flow duration, and entropy. Various ML models, including Decision Trees, Random Forest (RF), and Deep Neural Networks (DNN), were evaluated to identify the best fit for integration with Suricata. The experimental results demonstrate that the Smart IDS significantly enhances DDoS attack detection compared to traditional approaches, especially in minimizing false positives. Suricata alone reached an accuracy of 87 %, but integrating it with the ML models raised detection accuracy to 99 %. This research contributes to the advancement of network security by providing a scalable and adaptable solution for real-time DDoS detection, with promising applications in both academic research and enterprise settings.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".