Geliştirilmiş dikkat ve genişletilmişkonvolüsyon tabanlı topluluk model tabanlıağ saldırı tespit sistemi, geliştirilmiş cheetah optimizörü kullanarak düşmanca kaçınma saldırılarına karşı

A reliable defense system against network threats over a long time is called the Intrusion Detection System (IDS). Defenders are notified by the IDS when suspicious or malevolent activities are identified on the network. In the last ten years, machine learning has helped the IDS to become more accurate, more capable of analysis, and more adept at finding new or modified forms of known intrusions. Deep learning, an advanced version of the machine learning technique, is essential to the field of network security. Additionally, a deep learningbased Network Intrusion Detection System (NIDS) performs better than conventional IDS techniques. Recent studies, however, demonstrate that when faced with attackers in realtime, the deep learning-based IDS becomes somewhat inaccurate. There is no analysis done on how attack models will affect NIDS as well. In order to defend against adversarial evasion attacks, an enhanced deep learning-based NIDS model is designed here. The required data is first collected from commonly available websites. The best feature extraction is carried out on the collected data. Here, the Improved Cheetah Optimizer (ICO). Then, an Attention and Dilated-based Ensemble Network (ADEN) is implemented to detect the intrusions from the optimally extracted features. The Deep Temporal Convolutional Neural Network (DTCN), Long Short-term Memory (LSTM), and Gated Recurrent Unit (GRU) models are assembled together to deploy the suggested ADCEN. In the end, the ADEN detects the viii intrusions and generates the respective outputs using the fuzzy ranking approach. To demonstrate well the recommended deep learning-based NIDS defends against adversarial evasion assaults, experiments are conducted against conventional models.