Mohamad Imad Mahaini

Modelling and analysing trust and influence in social networks
Research work: 

Modelling and Analysing Trust and Influence in Social Networks.

The main topic of research of ESR-15 is about the "influence" and "trust" in Online Social Networks (OSN). Its objectives are:

  1. Study the socio-technical aspects that have a significant relevance to cyber-security analysis and operations.
  2. Develop socio-technical systems that enable modelling and analysing trust and influence in social networks (public and private) and in adversary groups and communities.

The research will focus on how trust, advice and influence impact on:

  1. Social networks that include cyber-security professionals or other personnel involved in cyber-security operations.
  2. Social networks used by adversaries for coordinating or enacting cyber-attacks.

We aim to build a hybrid approach that combine methods from Computer Science and Psychology in order to better understand what makes the OSN users “influencers” and how “Trust” occurred between users. Our focus is the Cybersecurity context where we consider only the topics, events and discussions that are related to Cybersecurity domains.

This project will also consider regulatory and legal aspects (including privacy) that underpin the ethical and lawful conduct of such analyses.

ESRs Publications


Taxonomies and ontologies are handy tools in many application domains such as knowledge systematization and automatic reasoning. In the cyber security field, many researchers have proposed such taxonomies and ontologies, most of which were built based on manual work. Some researchers proposed the use of computing tools to automate the building process, but mainly on very narrow sub-areas of cyber security. Thus, there is a lack of general cyber security taxonomies and ontologies, possibly due to the difficulties of manually curating keywords and concepts for such a diverse, inter-disciplinary and dynamically evolving field.

This paper presents a new human-machine teaming based process to build taxonomies, which allows human experts to work with automated natural language processing (NLP) and information retrieval (IR) tools to co-develop a taxonomy from a set of relevant textual documents. The proposed process could be generalized to support non-textual documents and to build (more complicated) ontologies as well. Using the cyber security as an example, we demonstrate how the proposed taxonomy building process has allowed us to build a general cyber security taxonomy covering a wide range of data-driven keywords (topics) with a reasonable amount of human effort.