A Social Dimensional Cyber Threat Model with Formal Concept Analysis and Fact-Proposition Inference

Cyberspace has increasingly become a medium to express outrage, conduct protests, take revenge, spread opinions, and stir up issues. Many cyber attacks can be linked to current and historic events in the social, political, economic, and cultural (SPEC) dimensions of human conflicts in the physical world. These SPEC factors are often the root cause of many cyber attacks. Understanding the relationships between past and current SPEC events and cyber attacks can help understand and better prepare people for impending cyber attacks. The focus of this paper is to analyze these attacks in social dimensions and build a threat model based on past and current social events. A reasoning technique based on a novel combination of Formal Concept Analysis (FCA) and hierarchical fact-proposition space (FPS) inference is applied to build the model

BIM-enabled facilities management (FM): a scrutiny of risks resulting from cyber attacks

Purpose Building information modelling (BIM) creates a golden thread of information of the facility, which proves useful to those with the malicious intent of breaching the security of the facility. A cyber-attack incurs adverse implications for the facility and its managing organisation. Hence, this paper aims to unravel the impact of a cybersecurity breach, by developing a BIM-facilities management (FM) cybersecurity-risk-matrix to portray what a cybersecurity attack means for various working areas of FM. Design/methodology/approach This study commenced with exploring cybersecurity within various stages of a BIM project. This showcased a heightened risk of cybersecurity at the post-occupancy phase. Hence, thematic analysis of two main domains of BIM-FM and cybersecurity in the built environment led to the development of a matrix that illustrated the impact of a cybersecurity attack on a BIM-FM organisation. Findings Findings show that the existing approaches to the management of cybersecurity in BIM-FM are technology-dependent, resulting in an over-reliance on technology and a lack of cybersecurity awareness of aspects related to people and processes. This study sheds light on the criticality of cyber-risk at the post-occupancy phase, highlighting the FM areas which will be compromised as a result of a cyber-attack. Originality/value This study seeks to shift focus to the people and process aspects of cybersecurity in BIM-FM. Through discussing the interconnections between the physical and digital assets of a built facility, this study develops a cyber-risk matrix, which acts as a foundation for empirical investigations of the matter in future research

The entangled cyberspace: an integrated approach for predicting cyber-attacks

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonSignificant studies in cyber defence analysis have predominantly revolved around a single linear analysis of information from a single source of evidence (The Network). These studies were limited in their ability to understand the dynamics of entanglements related to cyber-incidents. This research integrates evidence beyond the network in an attempt to understand and predict phases of the kill-chain across the information space. This research provides a multi-dimensional phased analysis of the traditional kill-chain model using structural vector autoregressive models. In the ‘Entangled Cyberspace Framework’, each phase of the kill-chain corresponds to a single dimension of the information space based on time observations of certain events. Events are represented as time signals, where each phase is characterised by multiple time signals representing multiple events on that phase. Multiple time signals are analysed using structural models for multiple time series analysis (Vector Auto-Regressive models). At each phase of the kill-chain, we perform a lagged co-integration analysis of events across the information space. This nature of analysis detects hidden entanglements that characterise events in the kill-chain beyond the network. The measured prediction accuracy and error measured at each stage of the experiment represents the usefulness of selected events in characterising the defined stage of the kill-chain. The entangled cyberspace, in theory, is the fusion of three conceptual foundations: a) A multi-dimensional characterisation of cyberspace, b) A sequential phased model for perpetrating cyber-attacks and c) A structural model for integrating and simultaneously analysing multiple sources of evidence. It starts with the characterisation of the information space into different dimensions of interest. The framework goes further to identify evidence sources across these characterised dimensions and integrates them in the analytical context under consideration (e.g. Malware Injection). The concrete findings show that our approach and analytical methodology are capable of detecting entanglements when applied to a set of entangled activities across the information space. The findings also prove that activities beyond the network have significant effects on the nature of the unfolding cyber-attack vector. The predictive features of events across the kill-chain were also presented in this research as opinion and emotion drivers on the social dimension, packet data details and social and cultural events on the economic layer. Finally, co-integration detected between events across and within dimensions of the information space proves the existence of both inter-dimensional and intra-dimensional entanglements that affect the nature of events unfolding during the kill-chain (from the adversary’s point of view). The novelty of this research rests in the ability to hop across the information space for detecting evidential clues of activities that are related-to cyber-incidents. This research also expands the standard multi-dimensional information space to include SPEC factors as indicators of cyber-incidents. This research improves the current information security management model, specifically in the monitoring, analysis and detection phases. This research provides a methodology that accommodates a robust evidence base for understanding the attack surface. Practically, this research provides a basis for creating applications and tools for protecting critical national infrastructure by integrating data from social platforms, real-world political, cultural and economic events and the cyber-physical