## 2 Related Works  
A DNN with game theory for cloud security (GT-CSDNN) was modeled in [16]. The proposed method includes defender and attacker techniques when utilizing the game theory method. Moreover, the deep neural network (DNN) network leverages the devised game theory method to categorize regular data and attacks. The GT-performance CSDNNs are evaluated by using the CICIDS--2017 data. The collected data can be normalized, and the improved whale algorithm (IWA) was employed to evaluate the attack's optimal points and normal data. [PERSON] and [PERSON] [17] suggested an innovative IDS recognized on an integration of a leader-oriented k-means clustering (LKM), an optimal fuzzy logic (FL) system. Initially, input data were assembled into clusters by using the LKM. Afterwards,cluster datasets were afforded to the FL system (FLS). In this study, abnormal and normal data were examined by the FLS, and FLS training can be performed through grey wolf optimizer (GWO) by maximizing the rules. [PERSON] et al. [18] devised a VM Introspection (VMI) related security structure model for fine granular monitoring of VMs for identifying renowned assaults and their variants. VMGuard enforces the software breakpoint injection approach, which is agnostic and is employed for trapping the accomplishment of programs. Inspired by text mining methods, VMGuard provides a 'Bag of n-grams (BonG)' method compiled with the term frequency-inverse document frequency (TF-IDF) approach for selecting and extracting attack features and normal traces. After implementing the random forest (RF) method for generating generic conduct for distinct groups of intrusion of the monitored VM, [PERSON] et al. [19] introduced HMM_TDL, a DL method for preventing and detecting assault in the cloud environment. The devised approach can be performed in three states, incorporating a hidden Markov model (HMM) for attack detection. The HMM method sends hyper-alerts to the database for assault prevention. In Ref. [20], an anomaly-related network IDS (NIDS) was modeled, analyzing and monitoring the network traffic flow that targeted a cloud platform. A network administrator must be informed regarding the nature of such traffic to block and drop any intrusive network connection. The binary-related particle swarm optimization (BPSO) was implemented to select the most related network features, whereas the standard-based PSO (SPSO) was enforced for tuning support vector machine (SVM) control parameters. [PERSON] and [PERSON] [21] evaluated and presented a radial basis function neural network (RBF-NN) detector for detecting DDoS assaults. The resultant network can be frequently insufficient or needlessly intricate, and a suitable network structure is configured merely by the trial-and-error technique. This study devises the bat algorithm (BA) for configuring RBF-NN mechanically. [PERSON] and [PERSON] [22] presented an effectual dragonfly-improved invasive weed optimizer-based Shepard CNN (DIIWO-based ShCNN) for detecting the intruder and for mitigating the attack from the cloud paradigm and it can further detect the intruder with ShCNN. In [23], an effectual IDS was suggested by the presented sail fish dolphin optimizer-based Deep RNN (SFDO-based Deep RNN) that was employed for identifying an anomaly from the cloud framework. The established SFDO was formed by combining sail fish optimizer (SFO) and the dolphin echolocation (DE) technique. Virtual machine (VM) migration and cloud data management can be obtained utilizing ChicWhale technique. [PERSON] and [PERSON] [24] examined a Fisher kernel-based PCA dimensional reduction technique and GWO-based weight dropped Bi-LSTM technique (FKPCA-GWO WDBiLSTM) for IDS. Primarily, combined with the data record for PCA, the Fisher kernel with Fisher score was offered as input for achieving linearly separable dimensionality reduction. Secondarily, the WDBiLSTM network was utilized for retaining the long-term dependency but removing the feature from forward and backward directions.