Automated Design of Tailored Link Prediction Heuristics for Applications in Enterprise Network Security


The link prediction problem, which involves determining the likelihood of a relationship between objects, has numerous applications in the areas of recommendation systems, social networking, anomaly detection, and others. A variety of link prediction techniques have been developed to improve predictive performance for different application domains. Selection of the appropriate link prediction heuristic is critical which demonstrates the need for tailored solutions. This work explores the use of hyper-heuristics to automate the selection and generation of customized link prediction algorithms. A genetic programming approach is used to evolve novel solutions from functionality present in existing techniques that exploit characteristics of a specific application to improve performance. Applications of this approach are tested using data from a real-world enterprise computer network to differentiate normal activity from randomly generated anomalous events. Results are presented that demonstrate the potential for the automated design of custom link prediction heuristics that improve upon the predictive capabilities of conventional methods.

Meeting Name

2019 Genetic and Evolutionary Computation Conference, GECCO 2019 (2019: Jul. 13-17, Prague, Czech Republic)


Computer Science


This work was supported by Los Alamos National Laboratory via the Cyber Security Sciences Institute under subcontract 259565 and the Laboratory Directed Research and Development program of Los Alamos National Laboratory under project numbers 20180607ECR and 20170683ER.

Keywords and Phrases

Anomaly detection; Forecasting; Genetic algorithms; Genetic programming; Heuristic methods, Anomalous events; Conventional methods; Enterprise networks; Hyper-heuristics; Improve performance; Predictive capabilities; Predictive performance; Tailored Solutions, Network security

International Standard Book Number (ISBN)


Document Type

Article - Conference proceedings

Document Version


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© 2019 Association for Computing Machinery (ACM), All rights reserved.

Publication Date

01 Jul 2019