Embedded Signal Artificial Neural Network Based Intelligent Non-Dependent Feature Selection for Cyber Attack Classification in Signal-Based Networks
Bibliographic record
Abstract
Signal-based cyber attacks pose a significant threat to the integrity, confidentiality, and availability of information systems.Intrusion Detection Systems (IDS) monitor network and system activities for malicious activity or policy breaches, which are then reported to a management station.Due to the high volume of network traffic in cyber networks, real-time threat detection is often computationally infeasible.In this study, we explore the use of an Artificial Neural Network (ANN) for cyber network threat identification, specifically focusing on its application in nonlinear characteristics and network security domains.Data reduction is crucial for achieving real-time detection in a Signal-based Cyber Attack Detection Model (SCADM).However, traditional CADMs analyze all data features to detect patterns of intrusion or misuse, leading to redundancy in detection features.The primary objective of this research is to identify computationally efficient and effective input features for SCADM.We propose an embedded Signal with ANN-based Intelligent Non-Dependent Feature Selection Model (ANN-INDFSM) that effectively extracts signal-based cyber attack features and performs feature reduction for accurate detection of signal-based cyber attacks while maintaining security.The ANN-based feature selection method was employed for eliminating non-salient features and determining dimensionality levels.Given the diverse characteristics and pattern types of emerging cyber attacks, tracking them has become increasingly challenging.Various methods have been used for feature extraction and selection, with the ultimate goal of detecting anomalies in large cyber security datasets.Although this process is both time-consuming and computationally demanding, the efficiency of machine learning algorithms can be improved by removing unnecessary and redundant features.Feature selection (FS) serves as one such method.By utilizing datasets containing only a sufficient subset of features instead of the full dataset, the computational time required for attack detection algorithms can be reduced.When compared to existing models, the proposed ANN-INDFSM demonstrates optimized performance levels, providing a streamlined and effective solution for the detection of cyber attacks in signalbased networks.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".