A Survey on Threat Hunting in Enterprise Networks
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
With the rapidly evolving technological landscape, the huge development of the Internet of Things, and the embracing of digital transformation, the world is witnessing an explosion in data generation and a rapid evolution of new applications that lead to new, wider, and more sophisticated threats that are complex and hard to be detected. Advanced persistence threats use continuous, clandestine, and sophisticated techniques to gain access to a system and remain hidden for a prolonged period of time, with potentially destructive consequences. Those stealthy attacks are often not detectable by advanced intrusion detection systems (e.g., LightBasin attack was detected in 2022 and has been active since 2016). Indeed, threat actors are able to quickly and intelligently alter their tactics to avoid being detected by security defense lines (e.g., prevention and detection mechanisms). In response to these evolving threats, organizations need to adopt new proactive defense approaches. Threat hunting is a proactive security line exercised to uncover stealthy attacks, malicious activities, and suspicious entities that could circumvent standard detection mechanisms. Additionally, threat hunting is an iterative approach to generate and revise threat hypotheses endeavoring to provide early attack detection in a proactive way. The proactiveness consists of testing and validating the initial hypothesis using various manual and automated tools/techniques with the objective of confirming/refuting the existence of an attack. This survey studies the threat hunting concept and provides a comprehensive review of the existing solutions for Enterprise networks. In particular, we provide a threat hunting taxonomy based on the used technique and a sub-classification based on the detailed approach. Furthermore, we discuss the existing standardization efforts. Finally, we provide a qualitative discussion on current advances and identify various research gaps and challenges that may be considered by the research community to design concrete and efficient threat hunting solutions.
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 | 0.013 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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