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 …

Given a stream of heterogeneous graphs containing different types of nodes and edges,
how can we spot anomalous ones in real-time while consuming bounded memory? This …

In this work, we propose a new approach to detect anomalous graphs in a stream of directed
and labeled heterogeneous edges. The stream consists of a sequence of edges derived …

In this paper, a new binary linear programming formulation for computing the exact Graph
Edit Distance (GED) between two graphs is proposed. A fundamental strength of the …

Deep learning methods are being increasingly widely used in static malware detection field
because they can summarize the feature of malware and its variants that have never …

Approximation of graph edit distance in polynomial time enables us to compare large,
arbitrarily labeled graphs for structural pattern recognition. In a recent approximation …

Due to the ability of graphs to represent properties of entities and binary relations at the
same time, a growing interest in this representation formalism can be observed in various …

Recent advances in data-acquisition technologies have equipped team coaches and sports
analysts with the capability of collecting and analyzing detailed data of team activity in the …

This paper presents a binary linear program which computes the exact graph edit distance
between two richly attributed graphs (ie with attributes on both vertices and edges). Without …

With the rapid advances of anti-virus and anti-tracking technologies, three aspects in
malware clustering need to be improved for effective clustering, ie, the robustness of …