The recent blaze in cyber espionage has posed unprecedented challenges to the cutting edge network intrusion detection systems in terms of accurate and precise classification of dynamically evolving threats. Along with the traditional signature based detection, the supervised and unsupervised machine learning algorithms are also being deployed to detect advance anomalies. However, due to the class overlap between the threat and legitimate data over feature space, satisfactory detection results cannot be obtained. This necessitates the introduction of cognition in the domain of cyber-security. In this paper, a wavelet based multiscale Hebbian learning approach in neural networks is introduced to address the challenge of class overlap. Contrary to inherently linear single scale Hebbian learning, the proposed methodology is able to distinguish non-linear and overlapping classification boundaries sufficiently well. A comparison of presented techniques with fundamental gradient descent based neural network shows promising results. Experimental results on simulated and real-world UNSW-NB15 dataset have been presented to support the claim.