Deep Ensemble-based Efficient Framework for Network Attack Detection

Nowadays, networks play a critical role in business, education, and daily life, allowing people to communicate via different platforms across long distances. However, such communication contains many potential dangers and security vulnerabilities that can compromise the confidentiality, integrity, and privacy of data. Network attacks, malware, hacking, and phishing are increasing daily, resulting in colossal losses. Automated systems based on artificial intelligence can help to detect such attacks efficiently and protect sensitive information. This work proposes an ensemble deep voting classifier (EDVC) to detect network attacks with high accuracy. Long short-term memory (LSTM), recurrent neural network (RNN), and gated recurrent unit (GRU) are employed in the proposed approach using the majority voting criteria. Experimental results using the NSL-KDD dataset indicate the superior performance of EDVC with a 0.996 accuracy score which is superior to the existing state-of-the-art methods.