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UBC Theses and Dissertations

On paradigm shifts : enabling proactive defenses by identifying the vulnerable population, and online bitemporal dynamic graph analytics Halawa, Hassan


Fueled by the massive amount of data and meta-data harvested by large-scale online service providers, two trends stand out: the broad adoption of machine learning particularly for cybersecurity defenses, and the growing pains of temporal graph analytics particularly for dynamically evolving systems. In this dissertation, my overarching goal is to explore novel ways: to effectively harness this harvested data to evidently improve the security of online platforms in general and their most vulnerable users in particular, and to explicitly model the temporal evolution of this data to efficiently enable business use cases that can not be served by existing graph analytics systems. To that end, I advocate for a paradigm shift across two high impact domains: cybersecurity and graph analytics. On the one hand, existing cybersecurity defenses are reactive, and victim-agnostic: they are predicated on identifying the attacks/attackers, and do not take user characteristics into account. In contrast, I propose a proactive approach based on identifying the vulnerable population, and leveraging this information to improve the security of the platform in general and the most vulnerable users in particular. To that end, I approach harnessing the vulnerable population under a victim-centric defense paradigm while contrasting against conventional defenses, and demonstrate its feasibility using four months of production data encompassing billions of events from hundreds of millions of users. To my knowledge, I am the first to propose and discuss such a defense paradigm. On the other hand, existing graph analytics systems are mostly static, and non-temporal: they are not fully able to support modeling systems that evolve dynamically over time while supporting the queries (including current state, historical, and audit queries) required by today's use cases. In contrast, I contend that future graph analytics systems should be: online, dynamic, and employ bitemporal modeling at their core. To that end, I examine the use cases that are an ideal match for an online bitemporal dynamic graph analytics system, explore the design trade-off space, and develop and characterize several designs targeting different points within that space. To my knowledge, I am the first to propose, develop, and characterize such a system end-to-end.

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