Research
On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,051 papers · 148 categories

Trend · papers per month

0.3%0.5%0.8%0.1% · Aug 202319922001200920182026
8 results for ransomware

Paper builds ML classifier to detect crypto-ransomware.

problem Detecting crypto-ransomware with high accuracy and low false positives.
method Behavior-based detection using input/output activities and file-content entropy. Deep-learning classifier with adversarial research and Integrated Gradient method for explanation.
result Deep-learning classifier achieves high accuracy and low false positive rate in detecting crypto-ransomware.

This work develops agents to learn generalizable policies for dynamic network environments.

problem Real-world network topologies change due to attackers, defenders, or system failures, leading to failures in adaptive ACD systems.
method Developing agents to learn generalizable policies across dynamic network environments.
result Agents can learn robust policies for dynamic network topologies and diverse attackers.

Cyber attacks are growing in frequency and severity. Over the past year alone we have witnessed massive data breaches that stole personal information of millions of people and wide-scale ransomware attacks that paralyzed critical infrastructure of several countries. Combating the rising cyber threat calls for a multi-p…

2018-06-08abs ↗pdf ↗

The paper introduces a US crime index to assess financial losses from property and cyber crimes.

problem Lack of indices evaluating crime's financial impact on investments.
method Developed an index-based insurance portfolio using FBI financial losses data.
result Real estate, ransomware, and government impersonation are major risk contributors.

Improved malware detection by adding auxiliary loss terms to a neural network.

problem Malware detection accuracy with a single label.
method Fit deep neural networks to multiple auxiliary prediction targets derived from metadata.
result Significant improvement in detection performance, reducing false negatives by 42.6% at a low false positive rate.

Model quantifies cyber-attacks' impact on firms and insurers.

problem Impact of cyber-attacks on firms' revenues and insurers' portfolios.
method Stochastic SIR model coupled with granular firm growth model.
result Predicts insurer needs to compensate up to two days of revenue in a 100-day incident.