Biblio

Big data holds the promise of enabling analysts to predict, mitigate and respond rapidly to natural disasters and human crises. Rapid response is critical for saving lives and mitigating damage.  Rapid response requires understanding the socio-cultural terrain; i.e., understanding who needs what or can provide what, where, how, why and when. Today, new technologies are forging a path making use of sensor and social media data to understand the socio-cultural terrain and provide faster response during crises.  A review of this area, the state of the art, and known challenges are discussed.  The basic argument is that this technology is still in its infancy.  Critical scientific challenges, social challenges, and legal challenges will need to be addressed before the promise of big data for Crisis Mapping is fulfilled.

Decision makers need capabilities to quickly model and effectively assess consequences of actions and reactions in crisis de-escalation environments. The creation and what-if exercising of such models has traditionally had onerous resource requirements. This research demonstrates fast and viable ways to build such models in operational environments. Through social network extraction from texts, network analytics to identify key actors, and then simulation to assess alternative interventions, advisors can support practicing and execution of crisis de-escalation activities. We describe how we used this approach as part of a scenario-driven modeling effort. We demonstrate the strength of moving from data to models and the advantages of data-driven simulation, which allow for iterative refinement. We conclude with a discussion of the limitations of this approach and anticipated future work.

Identifying factors behind countries’ weakness to cyber-attacks is an important step towards addressing these weaknesses at the root level.  For example, identifying factors why some countries become cyber- crime safe heavens can inform policy actions about how to reduce the attractiveness of these countries to cyber-criminals.  Currently, however, identifying these factors is mostly based on expert opinions and speculations.

In this work, we perform an empirical study to statistically test the validity of these opinions and specu- lations.  In our analysis, we use Symantec’s World Intelligence Network Environment (WINE) Intrusion Prevention System (IPS) telemetry data which contain attack reports from more than 10 million customer computers worldwide.  We use regression analysis to test for the relevance of multiple factors including monetary and computing resources, cyber-security research and institutions, and corruption.

Our analysis confirms some hypotheses and disproves others. We find that many countries in Eastern Europe extensively host attacking computers because of a combination of good computing infrastructure and high corruption rate.  We also find that web attacks and fake applications are most prevalent in rich countries because attacks on these countries are more lucrative. Finally, we find that computers in Africa launch the lowest rates of cyber-attacks. This is surprising given the bad cyber reputation of some African countries such as Nigeria. Our research has many policy implications.

The number of trojans, worms, and viruses that computers encounter varies greatly across countries. Empirically identifying factors behind such variation can provide a scientific empirical basis to policy actions to reduce malware encounters in the most affected countries. However, our understanding of these factors is currently mainly based on expert opinions, not empirical evidence.

In this paper, we empirically test alternative hypotheses about factors behind international variation in the number of trojan, worm, and virus encounters. We use the Symantec Anti-Virus (AV) telemetry data collected from more than 10 million Symantec customer computers worldwide that we accessed through the Symantec Worldwide Intelligence Environment (WINE) platform. We use regression analysis to test for the effect of computing and monetary resources, web browsing behavior, computer piracy, cyber security expertise, and international relations on international variation in malware encounters.

We find that trojans, worms, and viruses are most prevalent in Sub-Saharan African countries. Many Asian countries also encounter substantial quantities of malware. Our regression analysis reveals that the main factor that explains high malware exposure of these countries is a widespread computer piracy especially when combined with poverty. Our regression analysis also reveals that, surprisingly, web browsing behavior, cyber security expertise, and international relations have no significant effect.