Bibliographic record
Abstract
The edge-based network attacks are increasing largely in size, scale and frequency with the booming internet. The Distributed Denial of Service (DDoS) attacks is one of the most diffused types of attacks in the cyberworld which is a great concern for all organizations today. There were 5.2 billion Google searches in the year 2017 alone. Research shows that there cannot be a better example to show how prevalent internet use is nowadays. What started off as a point to point conversation in 1970s as a mode of communication has expanded to millions and millions of devices communicating with each other via some or the other form of the web. And therefore, this research focuses on the strategic approach that has been taken to defend from the growing cyber threat. In this dissertation, the focus is on defending against these edge-based network attacks and collaborating with other neighboring networks in order to communicate with them to transfer information about potentially malicious hosts. This research has focused on exploring ways of integrating the Software Defined Networking with the pub-sub messaging system in order to show a collaborative approach of defense to these attacks. The attacks from unknown multiple sources have also been analyzed in order to cope with them through this robust solution that has been proposed and implemented in this dissertation.
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.
How this classification was reachedexpand
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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 teacher head, 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".