Hybrid Deep-Learning-Based Anomaly Detection Scheme for Suspicious Flow Detection in SDN: A Social Multimedia Perspective
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
The continuous development and usage of multi-media-based applications and services have contributed to the exponential growth of social multimedia traffic. In this context, secure transmission of data plays a critical role in realizing all of the key requirements of social multimedia networks such as reliability, scalability, quality of information, and quality of service (QoS). Thus, a trust-based paradigm for multimedia analytics is highly desired to meet the increasing user requirements and deliver more timely and actionable insights. In this regard, software-defined networks (SDNs) play a vital role; however, several factors such as as-runtime security, and energy-aware networking limit its capabilities to facilitate efficient network control and management. Thus, with the view to enhance the reliability of the SDN, a hybrid deep-learning-based anomaly detection scheme for suspicious flow detection in the context of social multimedia is proposed. It consists of the following two modules: (1) an anomaly detection module that leverages improved restricted Boltzmann machine and gradient descent-based support vector machine to detect the abnormal activities, and (2) an end-to-end data delivery module to satisfy strict QoS requirements of the SDN, that is, high bandwidth and low latency. Finally, the proposed scheme has been experimentally evaluated on both real-time and benchmark datasets to prove its effectiveness and efficiency in terms of anomaly detection and data delivery essential for social multimedia. Further, a large-scale analysis over a Carnegie Mellon University (CMU)-based insider threat dataset has been conducted to identify its performance in terms of detecting malicious events such as-Identity theft, profile cloning, confidential data collection, etc.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 it