Nowadays, both the amount of cyberattacks and their sophistication have considerably increased, and their prevention is of concern of most of organizations. Cooperation by means of information sharing is a promising strategy to address this problem, but unfortunately it poses many challenges. Indeed, looking for a win-win environment is not straightforward and organizations are not properly motivated to share information. This work presents a model to analyse the benefits and drawbacks of information sharing among organizations that presents a certain level of dependency. The proposed model applies functional dependency network analysis to emulate attacks propagation and game theory for information sharing management. We present a simulation framework implementing the model that allows for testing different sharing strategies under several network and attack settings. Experiments using simulated environments show how the proposed model provides insights on which conditions and scenarios are beneficial for information sharing.
One of the main problems when analysing the costs and benefits of information sharing is the experimentation with real data. Whereas most of the proposed works @cite_21 @cite_11 @cite_16 perform evaluation using analytical methods, @cite_17 present a controlled data sharing approach and make empirical evaluation using a dataset of suspicious IP addresses. Authors in @cite_17 use different similarity metrics to analyse benefits of sharing and compare different sharing strategies: sharing everything or only information about attack entities. Authors in @cite_17 rely on a static scenario and provide useful metrics to mathematically predict benefits of info sharing. By contrast, using a simulated setting we empirically analyse how impacts are propagated through the network, at runtime, to afterwards analyse how information sharing is able to mitigate such impacts in the future.
@cite_21 @cite_17 @cite_16 @cite_11
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Sharing of security information among organizations has often been proposed as a method for improving the state of cyber-security for all. However, such disclosure entails costs for the reporting entity, including a drop in market value, loss of customer confidence, and bureaucratic burdens. By adopting a game-theoretic approach for understanding firms' incentives, we first show that in one shot interactions, disclosure costs act as disincentives for sharing, leading to no information sharing at equilibrium. We then consider a repeated game formulation that enables the use of inter-temporal incentives (i.e., the conditioning of future decisions on the history of past interactions) to support firms' cooperation on information sharing. We show that the nature of the monitoring available to firms greatly affects the possibility of sustaining nearly efficient outcomes through repeated interactions. Specifically, we first illustrate the limitations arising when firms' have independent and imperfect private monitoring technologies. We then consider the role of a public monitoring assessment system, and show that despite using the same imperfect technology as the individual firms, the monitor can facilitate cooperation among participants. Our results therefore illustrate the impact of a public rating reputation system on the viability of security information sharing agreements. Although sharing data across organizations is often advocated as a promising way to enhance cybersecurity, collaborative initiatives are rarely put into practice owing to confidentiality, trust, and liability challenges. In this paper, we investigate whether collaborative threat mitigation can be realized via a controlled data sharing approach, whereby organizations make informed decisions as to whether or not, and how much, to share. Using appropriate cryptographic tools, entities can estimate the benefits of collaboration and agree on what to share in a privacy-preserving way, without having to disclose their datasets. We focus on collaborative predictive blacklisting, i.e., forecasting attack sources based on one's logs and those contributed by other organizations. We study the impact of different sharing strategies by experimenting on a real-world dataset of two billion suspicious IP addresses collected from Dshield over two months. We find that controlled data sharing yields up to 105 accuracy improvement on average, while also reducing the false positive rate. New regulations mandating firms to share information on security breaches and security practices with authorities are high on the policy agenda around the globe. These initiatives are based on the hope that authorities can effectively advise and warn other firms, thereby strengthening overall defense and response to cyberthreats in an economy. If this mechanism works (as assumed in this paper with varying effectiveness), it has consequences on security investments of rational firms. We devise an economic model that distinguishes between investments in detective and preventive controls, and analyze its Nash equilibria. The model suggests that firms subject to mandatory security information sharing 1) over-invest in security breach detection as well as under-invest in breach prevention, and 2), depending on the enforcement practices, may shift investment priorities from detective to preventive controls. We also identify conditions where the regulation increases welfare. Technology Information technology; Terrorism and threats Cyberterrorism; Technology Modeling and simulation; Cyberspace and Cybersecurity