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Network intrusion detection systems (IDS) examine network packets and alert system administrators and investigators to low-level security violations. In large networks, these reports become unmanageable. To create flexible and effective intrusion detection systems for unpredictable attacks, there are several challenges to overcome. Much work has been done on the use of deep learning techniques in IDS; however, substantial computational resources and processing time are often required. In this paper, a 1D-Dilated Causal Neural Network (1D-DCNN) based IDS for binary classification is employed. The dilated convolution with a dilation rate of 2 is introduced to compensate the max pooling layer, preventing the information loss imposed by pooling and down-sampling. The dilated convolution can also expand its receptive field to gather additional contextual data. To assess the efficacy of the suggested solution …