In the context of complex networks research, underlying network structure is considered to play a crucial role in spreading processes such as virus propagation in social and computer networks. There are some assumptions that are often considered while utilizing a network structure in the immunization process, mainly the lack of global topological information and access to the partial information of underlying network structure. However, without global information about the whole network the immunization process becoms much more difficult. The main contribution of this paper is twofold. First, it develops an efficient approach called “One Node at One Step Discovery Process” to identify chains of high degree connections for immunization purpose in scale-free networks, and second, it investigates the performance of previous approaches such as acquaintance immunization strategy (AIS), common acquaintance immunization strategy (CAIS) and Intentional Random Walk with respect to their parameters. We performed the experiments on both synthetic and real data sets and conducted a multi-agent-based simulation to verify our proposed algorithm. Experimental results show the effectiveness of our proposed algorithm in targeting high degree nodes. The results also show that some existing algorithms found in prior literature are more robust than others with respect to parameter configuration and improper selection of the parameter can produce significantly different results.