Most network intruders launch their attacks through stepping-stones to reduce the risks of being discovered. To uncover such intrusions, one prevalent, challenging, and critical way is to compare an incoming connection with outgoing connections to determine if a computer is used as a stepping-stone. In this paper, we present a way by using signal processing technology-correlation coefficient, such as Spearman Rank, Kendall Tau Rank, and Pearson Product-Moment, to correlate two sessions to identify stepping-stone intrusions. The contribution of this paper is that we are the first one to apply correlation coefficient to stepping-stone intrusion detection, and more importantly, it is not necessary to monitor a session for a long time to conclude a stepping-stone intrusion. The experiment results showed that a step-ping-stone intrusion can be detected while an intruder input the username and password. Further work needs to be done to test if this approach could resist intruders' evasion.