As we increasingly rely on wireless infrastructure for enabling critical applications, e.g., from Industrial Internet of Things (IIoT) or healthcare, we must ensure resilience, which includes robustness against both accidental and intentional jamming attacks. Hence, awareness of the potential jamming attack information is crucial to the resilience of a fully automated system. However, channel estimation within modern communications remains highly vulnerable to such attacks. The jamming signal is considered to be projected onto the captured channels, which enables the jamming detection through channel analysis. In this work, we consider the detection of a jamming source within an indoor environment. Narrowband white Gaussian noise (WGN) is employed to jam the communications. The impact of the jamming signal on the channel measurements is analyzed by examining the correlation between the jammed and unjammed channels. Based on the correlation pattern, the presence of interference is detected through a comparative suite of statistical thresholding and machine learning algorithms. An overall detection accuracy of 99.31% is achieved, demonstrating the feasibility of jamming detection through the proposed framework.