Security threats are becoming very sophisticated and pervasive everywhere. Phishing threats in particular has a changeable nature and short life cycle that complicates the detection process. In this paper, we introduce a Multi-Agent System (MAS) as an adaptive intelligent technique that acts on top of distributed Case-Based Reasoning (CBR) Phishing Detection Systems (CBR-PDSs) as a Phishing Detection System Architecture (PDSA) that runs on large scale globally to constitute a robust worldwide Phishing Threat Intelligence (PTI) environment.

Named Data Networking architecture originally provided consumer mobility by design, however content or producer mobility was left unspecified.  Since then a number of producer mobility support schemes have been proposed. In this paper, we provide a survey on the most relevant proposed techniques to support mobility in NDN. We classify these mobility support techniques into categories based on their underlying mechanisms of explicit notification, routing, mapping, indirection, and proactive caching.

Named-Data Networking (NDN) is a promising candidate for the Internet of Things (IoT) which targets to improve
data dissemination eciency. This new paradigm brings considerable benefits such as minimizing the content producer

solicitation and rapid data transmission. However, the producer mobility issue in NDN is not suciently addressed.

Especially, in IoT scenario, in which devices are frequently mobile and it requires data to keep continuity. In this paper,
we present proposed producer mobility solutions in NDN in an IoT context.

Many classifications techniques have been used and devised to combat phishing threats, but none of them is able to efficiently identify web phishing attacks due to the continuous change and the short life cycle of phishing websites. In this paper, we introduce a Case-Based Reasoning (CBR) Phishing Detection System (CBR-PDS). It mainly depends on CBR methodology as a core part. The proposed system is highly adaptive and dynamic as it can easily adapt to detect new phishing attacks with a relatively small dataset in contrast to other classifiers that need to be heavily trained in advance.