Advanced Persistent Threats (APTs) are difficult to detect due to their" low-and-slow" attack
patterns and frequent use of zero-day exploits. We present UNICORN, an anomaly-based …

As computational biologists continue to be inundated by ever increasing amounts of
metagenomic data, the need for data analysis approaches that keep up with the pace of …

Auditing, a central pillar of operating system security, has only recently come into its own as
an active area of public research. This resurgent interest is due in large part to the notion of …

Considerable advances in genomics over the past decade have resulted in vast amounts of
data being generated and deposited in global archives. The growth of these archives …

In recent years, the Advanced Persistent Threat (APT), which involves complex and
malicious actions over a long period, has become one of the biggest threats against the …

Reliable methods for host-layer intrusion detection remained an open problem within
computer security. Recent research has recast intrusion detection as a provenance graph …

Embeddings have become a key paradigm to learn graph representations and facilitate
downstream graph analysis tasks. Existing graph embedding techniques either sample a …

Twitter spam classification is a tough challenge for social media platforms and cyber security
companies. Twitter spam with illegal links may evolve over time in order to deceive filtering …

Advanced Persistent Threats (APT) attacks have plagued modern enterprises, causing
significant financial losses. To counter these attacks, researchers propose techniques that …

Estimating set similarity and detecting highly similar sets are fundamental problems in areas
such as databases, machine learning, and information retrieval. MinHash is a well-known …