Detection and Mitigation of IP Spoofing and SYN Flooding DDoS Attacks in Cloud Computing
Bibliographic record
Abstract
Denial of Distributed Services (DDoS) is one of the significant threats to cloud computing. The attacker can affect the machine’s availability, and traditional defense strategies are difficult to apply to cloud computing due to their poor availability and high storage requirements. There are multiple different types of DDoS attacks such as IP Spoofing, SYN flooding, smurf, buffer overflow, ping of death, land and finally, teardrop attack. Among these, SYN Flooding and IP Spoofing are the most common and effective attacks these days. This thesis will focus on implementing a security algorithm to improve the two most common DDoS attacks in cloud computing. First, we will implement a simple detection mechanism using operating system fingerprinting for IP Spoofing and Confidence-Based Filtering pattern recognition with timestamp parameters for SYN Flooding attacks. After an attack is detected, a simple shared cloud-based database is updated for both legitimate connection and illegitimate connection for mitigation purposes. To mitigate an IP Spoof attack, source IP address filtering is used to allow only traffic with legitimate source IP addresses to access the network. For the purpose of mitigating SYN Flooding attacks, a prevention technique is used to classify the attack sources and discard traffic from such sources. In a secure cloud environment, we test our proposed algorithm and literature security methods for better comparison. The result shows that our proposed algorithm has counter literature methods drawbacks and allowed a more legitimate connection with less error.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".