Enhancing Spear Phishing Defense with AI: A Comprehensive Review and Future Directions
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
This paper presents a critical analysis of the role of Artificial Intelligence (AI) in defending against spear phishing attacks, which continue to be a significant cybersecurity threat. By examining 30 seminal studies, we provide an in-depth evaluation of current AI techniques, such as machine learning, natural language processing, and behavioural analytics, which are utilized to detect and mitigate sophisticated email threats. Our review uncovers that AI not only significantly enhances the detection capabilities against these tar-geted attacks but also faces challenges like adaptability and false positives. These findings highlight the continuous evolution of AI strategies in spear phishing defense and the need for ongoing innovation to keep pace with ad-vanced threat tactics. This paper aims to guide future research by proposing integrated AI solutions that enhance both detection capabilities and respon-siveness to new threats, thereby strengthening cybersecurity defenses in an increasingly digital world.
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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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