Abstract
The capture and deployment of intelligence to build strong defense is more holistic and a more focused approach to security for private and government organizations, due to diversified activities of threat actors and malicious users. It is essential for the private sector and government organizations to use technologies such as Cloud, Fog and Edge Computing that provide storing, processing, and transferring vast amounts of public and private data that can be used to generate classified and unclassified threat intelligence. Fog Computing is a platform that uses computation, storage, and application services similar to the Cloud Computing paradigm but fundamentally different due to its decentralized architecture. It is an emerging technology that gives the Cloud a companion to handle the exabytes of data generated daily from the devices in the Internet of things (IoT ) environment. In this scenario, the context-aware intelligent systems of Fog Computing that analyze the IoT data become crucial weapons to detect, mitigate, and prevent any possible cyber-threats , much earlier. The IoT environments generate vast amount of data that requires different context-aware intelligent systems to analyze this volumetric and complex data for security proposes to provide cyber-threat intelligence (CTI). The CTI is a collection of intelligence combining open-source intelligence (OSINT ), social media intelligence (SOCMINT ), measurement and signature intelligence (MASINT ), human intelligence (HUMINT ), and technical intelligence (TECHINT ) from deep and dark Web. All these intelligence systems have functions that are based on specific context-aware intelligence mechanisms. In this chapter, sensitive data collection and analysis methods and techniques are elaborated through several context-aware intelligence systems to expose the use of Fog Computing for analysis of edge networks data for cyber-threat intelligence .
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Sari, A. (2018). Context-Aware Intelligent Systems for Fog Computing Environments for Cyber-Threat Intelligence. In: Mahmood, Z. (eds) Fog Computing. Springer, Cham. https://doi.org/10.1007/978-3-319-94890-4_10
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