Cybercrime is estimated to have cost the global economy just under USD 1 trillion in 2020,
indicating an increase of more than 50% since 2018. With the average cyber insurance …

As the number of Internet-connected systems rises, cyber analysts find it increasingly difficult
to effectively monitor the produced volume of data, its velocity and diversity. Signature-based …

Given high-dimensional time series data (eg, sensor data), how can we detect anomalous
events, such as system faults and attacks? More challengingly, how can we do this in a way …

The automatic supervision of IT systems is a current challenge at Orange. Given the size and
complexity reached by its IT operations, the number of sensors needed to obtain …

Multimodal time series (MTS) anomaly detection is crucial for maintaining the safety and
stability of working devices (eg, water treatment system and spacecraft), whose data are …

Unsupervised/self-supervised time series representation learning is a challenging problem
because of its complex dynamics and sparse annotations. Existing works mainly adopt the …

Engineers at eBay utilize robust methods in monitoring IT system signals for anomalies.
However, the growing scale of signals, both in volumes and dimensions, overpowers …

Many real-world cyber-physical systems (CPSs) are engineered for mission-critical tasks
and usually are prime targets for cyber-attacks. The rich sensor data in CPSs can be …

Several techniques for multivariate time series anomaly detection have been proposed
recently, but a systematic comparison on a common set of datasets and metrics is lacking …

Self-supervised learning (SSL) has recently achieved impressive performance on various
time series tasks. The most prominent advantage of SSL is that it reduces the dependence …