Cyber-attacks attempt to hack computers or networks, causing information loss or denial of service. Traditional methods are inefficient against complex attacks. Deep learning techniques with bottleneck layers reduce parameters and features. Residual blocks deeper than traditional networks with 1x1 convolutions improve performance. The proposed approach is tested on the CIDDS-001 dataset, effectively detecting cyber-attacks. © 2022 Elsevier B.V., All rights reserved.