Network Traffic Behavioral Analytics for Detection of DDoS Attacks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As more organizations and businesses in different sectors are moving to a digital transformation, there is a steady increase in malware, facing data theft or service interruptions caused by cyberattacks on network or application that impact their customer experience. Bot and Distributed Denial of Service (DDoS) attacks consistently challenge every industry relying on the internet. In this paper, we focus on Machine Learning techniques to detect DDoS attack in network communication flows using continuous learning algorithm that learns the normal pattern of network traffic, behavior of the network protocols and identify a compromised network flow. Detection of DDoS attack will help the network administrators to take immediate action and mitigate the impact of such attacks. DDoS attacks are costing enterprises anywhere between $50,000 to $2.3 million per year. We performed experiments with Intrusion Detection Evaluation Dataset (CICIDS2017) available from Canadian Institute for Cybersecurity to detect anomalies in network traffic. We use flow based traffic characteristics to analyze the difference in pattern between normal vs anomaly packet.We evaluate several supervised classification algorithms using metrics like maximum detection accuracy, lowest false negatives prediction, time taken to train and run. We prove that decision tree based Random Forest is the most promising algorithm whereas Dense Neural network performs equally well on certain DDoS types but require more samples to improve the accuracy of low sampled attacks.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it