Nowadays, a rapid demand of content access leads to requirement of shifting towards content-centric infrastructures from IP-based Internet framework. Due to its scalability and less latency of access data, it makes it more predominant to be used along with cloud computing, fog computing, and edge computing. The features like server hit reduction, load balancing, and low computation make Information-centric Networking (ICN) an attention seeker in the eyepoint of various service providers like AWS. For content access, ICN does not depend on tracking of IP addresses; instead, it can provide the content to users by utilizing the nearby Content Routers (CRs). This mechanism leads to various serious security threats like flooding attacks, denial of service attacks, and content poisoning attacks in the system, which lead to resource exhaustion and performance degradation due to malicious activity. Motivated by this, in this paper, an approach has been proposed for the detection of flooding attacks in ICN. For content access, the client initiates an interest packet in the network; when this packet is mistreated by malicious users, i.e., attackers, to launch a flooding attack, it is termed as Interest Flooding Attack (IFA). The proposed approach utilizes a distance metric-based scheme called Jensen-Shannon Divergence (JSD), which smoothly adapts the dynamicity of the IFA attack patterns in the network. In state-of-the-art, various strategies like Kullback-Leibler Divergence have been implemented and compared to the JSD approach. The resultant outcomes of plots illustrate that our anticipated approach performs preeminently than existing distance metric-based approaches in the detection and identification of IFA in ICN, in terms of lightweight implementation and variability of fluctuations in attack patterns.