Enhancing Cyber Forensics with AI and Machine Learning: A Study on Automated Threat Analysis and Classification
Bibliographic record
Abstract
The escalating frequency and complexity of cyber-attacks have necessitated the development of effective cyber forensic investigation techniques. This research investigates the utilization of machine learning and artificial intelligence (AI) in automated analysis and classification of cyber threats, aiming to enhance the understanding of their role in cyber forensics. Employing case studies, observations, and surveys, information was gathered from forensic investigators and cybersecurity experts. The case studies comprehensively examine organizations that have implemented AI and machine learning in cyber forensics. Observational methods involve attending conferences and closely observing investigators during forensic analysis. Survey data from forensic investigators and cybersecurity experts were collected to gain insights into the application of these novel investigation methods in cyber forensics. The findings demonstrate that AI and machine learning are emerging as powerful tools for augmenting cyber forensic investigations, particularly in the realms of threat detection and classification. The case studies reveal that businesses adopting these technologies have experienced notable improvements in the efficiency and precision of forensic investigations. This study underscores the potential advantages of integrating artificial intelligence and machine learning in advancing digital forensic investigations and provides valuable insights into their roles in cyber forensics. Accelerated analytical procedures and enhanced threat detection capabilities are evident outcomes of incorporating these technologies. By leveraging AI and machine learning, investigations can be expedited, enabling prompt responses to cyber threats and reducing overall risk exposure for businesses. As the cybersecurity landscape continues to evolve, the successful integration of AI and machine learning in the industry holds the promise of ushering in a new era of proactive threat detection, bolstering organizations' capacity to safeguard digital assets.
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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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".