Anomaly detection, aka outlier detection or novelty detection, has been a lasting yet active
research area in various research communities for several decades. There are still some …

This survey investigates current techniques for representing qualitative data for use as input
to neural networks. Techniques for using qualitative data in neural networks are well known …

Recently, blockchain technology has become a topic in the spotlight but also a hotbed of
various cybercrimes. Among them, phishing scams on blockchain have been found to make …

We propose two solutions to outlier detection in time series based on recurrent autoencoder
ensembles. The solutions exploit autoencoders built using sparsely-connected recurrent …

System auditing provides a low-level view into cyber threats by monitoring system entity
interactions. In response to advanced cyber-attacks, one prevalent solution is to apply data …

To subvert recent advances in perimeter and host security, the attacker community has
developed and employed various attack vectors to make a malware much stealthier than …

Large enterprises are increasingly relying on threat detection softwares (eg, Intrusion
Detection Systems) to allow them to spot suspicious activities. These softwares generate …

In this paper, we study the problem of author identification under double-blind review setting,
which is to identify potential authors given information of an anonymized paper. Different …

Recent advances in neural networks have inspired people to design hybrid
recommendation algorithms that can incorporate both (1) user-item interaction information …

Abstract Representation learning has offered a revolutionary learning paradigm for various
AI domains. In this survey, we examine and review the problem of representation learning …