Malicious human behaviour in information system security: Contribution to a Threat Model for Event Detection Algorithms

Olivier De Casanove , Florence Sèdes (1, 2)

1 IRIT-SIG - Systèmes d’Informations Généralisées
2 UT3 - Université Toulouse III - Paul Sabatier

Among the issues the information system security community has to fix, the security of both data and algorithms is a concern. The security of algorithms is dependent on the reliability of the input data. This reliability is questioned, especially when the data is generated by humans (or bots operated by humans), such as in online social networks. Event detection algorithms are an example of technology using this type of data, but the question of the security is not systematically considered in this literature. We propose in this paper a first contribution to a threat model to overcome this problem. This threat model is composed of a description of the subject we are modelling, assumptions made, potential threats and defence strategies. This threat model includes an attack classification and defensive strategies which can be useful for anyone who wants to create a resilient event detection algorithm using online social networks.

Keywords: Threat model; Adversarial Learning; Online Social Network; Event Detection

Domains: Cryptography and Security [cs.CR]; Computers and Society [cs.CY]; Social and Information Networks [cs.SI]

Olivier De Casanove, Florence Sèdes. Malicious human behaviour in information system security: Contribution to a Threat Model for Event Detection Algorithms. 15th Symposium on Foundations and Practice of Security (FPS 2022), Dec 2022, Ottawa, Canada. pp.208-220, ⟨10.1007/978-3-031-30122-3_13⟩. ⟨hal-03815005v3⟩