Biblio

(No abstract.)

Bug-bounty programs have the potential to harvest the efforts and diverse knowledge of thousands of white hat hackers. As a consequence, they are becoming increasingly popular as a key part of the security culture of organizations. However, bug-bounty programs can be riddled with myriads of invalid vulnerability-report submissions, which are partially the result of misaligned incentives between white hats and organizations. To further improve the effectiveness of bug-bounty programs, we introduce a theoretical model for evaluating approaches for reducing the number of invalid reports. We develop an economic framework and investigate the strengths and weaknesses of existing canonical approaches for effectively incentivizing higher validation efforts by white hats. Finally, we introduce a novel approach, which may improve effi- ciency by enabling different white hats to exert validation effort at their individually optimal levels.

Increasing concern about insider threats, cyber-espionage, and other types of attacks which involve a high degree of stealthiness has renewed the desire to better understand the timing of actions to audit, clean, or otherwise mitigate such attacks. However, to the best of our knowledge, the modern literature on games shares a common limitation: the assumption that the cost and effectiveness of the players' actions are time-independent. In practice, however, the cost and success probability of attacks typically vary with time, and adversaries may only attack when an opportunity is present (e.g., when a vulnerability has been discovered). In this paper, we propose and study a model which captures dynamic environments. More specifically, we study the problem faced by a defender who has deployed a new service or resource, which must be protected against cyber-attacks. We assume that adversaries discover vulnerabilities according to a given vulnerability-discovery process which is modeled as an arbitrary function of time. Attackers and defenders know that each found vulnerability has a basic lifetime, i.e., the likelihood that a vulnerability is still exploitable at a later date is subject to the efforts by ethical hackers who may rediscover the vulnerability and render it useless for attackers. At the same time, the defender may invest in mitigation efforts to lower the impact of an exploited vulnerability. Attackers therefore face the dilemma to either exploit a vulnerability immediately, or wait for the defender to let its guard down. The latter choice leaves the risk to come away empty-handed. We develop two versions of our model, i.e., a continuous-time and a discrete-time model, and conduct an analytic and numeric analysis to take first steps towards actionable guidelines for sound security investments in dynamic contested environments.

Insurance is based on the diversifiability of individual risks: if an insurance provider maintains a large portfolio of customers, the probability of an event involving a large portion of the customers is negligible. However, in the case of cyber-insurance, not all risks are diversifiable due to software monocultures. If a vulnerability is discovered in a widely used software product, it can be used to compromise a multitude of targets until it is eventually patched, leading to a catastrophic event for the insurance provider. To lower their exposure to non-diversifiable risks, insurance providers may try to influence the security of widely used software products in their customer population, for example, through vulnerability reward programs. We explore the proposal that insurance providers should take a proactive role in improving software security, and provide evidence that this approach is viable for a monopolistic provider. We develop a model which captures the supply and demand sides of insurance, provide computational complexity results on the provider's investment decisions, and propose different heuristic investment strategies. We demonstrate that investments can reduce non-diversifiable risks and can lead to a more profitable cyber-insurance market. Finally, we detail the relative merits of the different heuristic strategies with numerical results.