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.
This paper surveys ML applications in SG for cyberattacks.
Model for optimal cybersecurity investment considering clustered cyberattacks.
The paper shows supply chain features improve cyber risk prediction.
A federated graph learning approach improves EV charging demand forecasting while protecting against cyberattacks.
Industrial control systems are critical to the operation of industrial facilities, especially for critical infrastructures, such as refineries, power grids, and transportation systems. Similar to other information systems, a significant threat to industrial control systems is the attack from cyberspace---the offensive …
Modern smart grids rely on advanced metering infrastructure (AMI) networks for monitoring and billing purposes. However, such an approach suffers from electricity theft cyberattacks. Different from the existing research that utilizes shallow, static, and customer-specific-based electricity theft detectors, this paper p…
The rapid digital transformation without security considerations has resulted in the rise of global-scale cyberattacks. The first line of defense against these attacks are Network Intrusion Detection Systems (NIDS). Once deployed, however, these systems work as blackboxes with a high rate of false positives with no mea…
This paper shows diffusion models improve intrusion detection by purifying adversarial examples.
Paper models cloud outages for cyber insurance stress-testing.
To be prepared against cyberattacks, most organizations resort to security information and event management systems to monitor their infrastructures. These systems depend on the timeliness and relevance of the latest updates, patches and threats provided by cyberthreat intelligence feeds. Open source intelligence platf…
The paper values reinsurance contracts for dynamic catastrophe claims without arbitrage.
This paper considers the real-time detection of anomalies in high-dimensional systems. The goal is to detect anomalies quickly and accurately so that the appropriate countermeasures could be taken in time, before the system possibly gets harmed. We propose a sequential and multivariate anomaly detection method that sca…
The increasing scale and sophistication of cyberattacks has led to the adoption of machine learning based classification techniques, at the core of cybersecurity systems. These techniques promise scale and accuracy, which traditional rule or signature based methods cannot. However, classifiers operating in adversarial …
Paper develops adaptive models for robust energy forecasting with missing data.
Machine learning has begun to play a central role in many applications. A multitude of these applications typically also involve datasets that are distributed across multiple computing devices/machines due to either design constraints (e.g., multiagent systems) or computational/privacy reasons (e.g., learning on smartp…
Study shows data breaches cause significant financial losses for firms, especially in health sector.
SIGMA improves IDS robustness against new attacks using GAN and metaheuristics.