In 5G and 6G ear, sensing data from huge amount of heterogeneous sensors will generate big data at the edge of IoT. Fog/Edge computing technology is proposed to resolve the edge big data analysis and processing. However, the security and intelligent management for fog/edge computing resources are still open issues. First, because fog/edge computing is usually deployed in large-scale IoT, it faces various threats from untrusted distributed geographic multi-sources and differentiated layer of the networks. Second, content threats will be generated at the communication layer, because software-defined networking/information-centric networking (SDN/ICN) technologies has been introduced into networked fog/edge computing nodes. Third, at the edge of the networks, there is unbalance between the users and providers of fog/edge computing resources, which means on-demand resource scheduling and balance are the must for fog/edge computing. Based on aforementioned motivations, this chapter aims to study the lightweight security and intelligent scheduling approaches for fog/edge computing resources. Collaborative trust, intrusion detection and security isolation, storage resource intelligent orchestration and service popularity-based smart resources partitioning technologies are studied for edge/fog computing. The works are significant to improve the intelligence and security level for novel fog/edge computing systems.