Biblio

Recently, named data networking (NDN) has been introduced to connect the world of computing devices via naming data instead of their containers. Through this strategic change, NDN brings several new features to network communication, including in-network caching, multipath forwarding, built-in multicast, and data security. Despite these unique features of NDN networking, there exist plenty of opportunities for continuing developments, especially with packet forwarding and caching. In this context, we introduce liteNDN, a novel forwarding and caching strategy for NDN networks. liteNDN comprises a cooperative forwarding strategy through which NDN routers share their knowledge, i.e. data names and interfaces, to optimize their packet forwarding decisions. Subsequently, liteNDN leverages that knowledge to estimate the probability of each downstream path to swiftly retrieve the requested data. Additionally, liteNDN exploits heuristics, such as routing costs and data significance, to make proper decisions about caching normal as well as segmented packets. The proposed approach has been extensively evaluated in terms of the data retrieval latency, network utilization, and the cache hit rate. The results showed that liteNDN, compared to conventional NDN forwarding and caching strategies, achieves much less latency while reducing the unnecessary traffic and caching activities.

Authorities like the Federal Financial Institutions Examination Council in the US and the European Central Bank in Europe have stepped up their expected minimum security requirements for financial institutions, including the requirements for risk analysis. In a previous article, we introduced a visual tool and a systematic way to estimate the probability of a successful incident response process, which we called an incident response tree (IRT). In this article, we present several scenarios using the IRT which could be used in a risk analysis of online financial services concerning fraud prevention. By minimizing the problem of underreporting, we are able to calculate the conditional probabilities of prevention, detection, and response in the incident response process of a financial institution. We also introduce a quantitative model for estimating expected loss from fraud, and conditional fraud value at risk, which enables a direct comparison of risk among online banking channels in a multi-channel environment.