<p>Cyber security must be implemented when using cloud computing to identify and protect malevolent intrusions and strengthen the organizations capacity against cyberattacks. Detecting network intrusions with zero false alarms is a challenge. A number of intrusion detection systems (IDS) for cloud computing (CC) environments have put forward recently. The existing IDS exhibit significant false positive rates, poor classification accuracy, and over-fitting. Therefore, a Double Fuzzy Clustering-Driven Context Neural Network for Intrusion Detection in Cloud Computing (DFCCNN-BWOA-IDC) is proposed in this paper. Initially, the input data is gleaned from DARPA dataset. The input data is pre-processed utilizing Sequential pre-processing through orthogonalization (SPORT) method to replace the missing values and remove the duplicate values. After that, the pre-processing data is fed to the recursive feature elimination (REF) approach for selecting optimal features. Then the selected features are supplied to the DFCCNN to categorize the data as Normal or Anomaly. Finally, the Beluga Whale Optimization algorithm (BWOA) is proposed to enhance the weight parameters of DFCCNN classifier, which precisely detects the attacks. The proposed DFCCNN-BWOA-IDC approach is activated in MATLAB. The DFCCNN-BWOA-IDC method reaches better accuracy of 98.89% which is 15.98%, 13.59% and 19.53% higher than the existing approaches, like intrusion detection in CC with the help of hybrid deep learning approach (DKNN-CRDO-IDC), intrusion detection scheme under hybrid teacher learning optimization facilitates deep RNN in web and cloud computing (TL-DRNN-IDC), Intrusion detection scheme utilizing deep learning and Capuchin Search Algorithm for cloud and IoT (CNN-CapSA-IDC) respectively.</p>