Malevolent Activity Detection with Hypergraph-Based Models
Antonella Guzzo, Andrea Pugliese, Antonino Rullo, Domenico Saccà, Antonio Piccolo · IEEE Transactions on Knowledge and Data Engineering · 2017
We propose a hypergraph-based framework for modeling and detecting malevolent activities. The proposed model supports the specification of order-independent sets of action symbols along with temporal and cardinality constraints on the execution of actions. We study and characterize the problems of consistency checking, equivalence, and minimality of hypergraph-based models. In addition, we define and characterize the general activity detection problem, that amounts to finding all subsequences that represent a malevolent activity in a sequence of logged actions. Since the problem is intractable, we also develop an index data structure that allows the security expert to efficiently extract occurrences of activities of interest.