Enhancing Cyber Resilience: Artificial Intelligence based Novel Threat Detection and Incident Response Plan for PowerPlus
Bibliographic record
Abstract
Despite the several advantages brought about by the modernization of critical infrastructure, it has also given rise to cybersecurity risks as CI has shifted from an isolated environment to Internet-connected ecosystems, exposing critical infrastructure to cyber threats. Cybersecurity threats pose significant risks to critical infrastructure, including power systems. Therefore, it is imperative to tackle the evolving cyber threats targeting critical infrastructure and develop a strategic risk assessment and incident response framework to neutralize the emerging cyber threats. This paper presents a strategic approach to enhancing PowerPlus cyber resilience through a novel risk assessment and incident response framework. Unlike traditional models, the proposed method monitors the network traffic of the IT/OT environment. It transforms network traffic dumps into grayscale image formats, making it a research problem in computer vision. The convolutional neural network (CNN) model extracts features with minimal dependence on manual feature engineering. The extracted feature maps of different image dimensions are used to train a machine learning classifier. This study performs experiments using several pre-trained CNN models, including VGG16/19, ResNet101V2, and InceptionV3, as feature extractors, and SVM as a classifier. The results show that VGG19+SVM achieves the best performance, with 91.35% accuracy, 91% precision, and 9 recall. The proposed methodology outclassed similar kinds of research.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".