Performance Analysis of a Bottleneck Layer Network in the Estimation of Cyber-Attacks

Cyber-attack is an attempt made from an individual or cybercriminals to hack a particular computer or network through internet. This leads to loss of information stored in the connected system and in certain cases it leads to denial of service. The traditional methods on addressing cyber-attacks are not efficient to the complex and high sophisticated attacks. Hence the deep learning based techniques are generated in recent years for estimating the attacks presence in a communication network. However, the deep learning networks are complex in nature as they are handled with a huge range of features during its operation. Therefore a bottleneck layer was developed to reduce the parameters count and feature formulations from a given data. The residual blocks are deeper than the traditional network architectures and it is achieved by enabling a 1x1 convolution block in the design flow. The performance of the bottleneck layer on the ANN and DNN algorithm is verified in the proposed work with an openly available CIDDS-001 dataset dataset. The CIDDS dataset is one of the recent dataset consists of server traffic data on OpenStack and external severs.