## 2 Related Work
Therefore, research in [6] introduced NSF-HC SQLIAS, a new NLP-based model designed to predict SQL injection attacks (SQLIA) in Secure Cloud Process Management (SCPM) systems. It starts with the extraction of low-dimensional semantic features from SQL queries through the use of three techniques: N-Skip Gram (SKG), TF-IDF, and Continuous Bag of Words (CBOW). Random sampling and up-sampling techniques address class imbalance in the dataset. Subsequently, these features undergo refinement via feature selection methods that involve significant predictor tests and variance threshold feature selection (VTFS). This process aims to improve time efficiency and reduce redundant calculations. After that, min-max normalization is utilized to avert convergence and overfitting problems. Nine machine learning algorithms, such as naive Bayes, neural networks, regression models, association rule mining, and ensemble techniques, are employed for binary classification (normal or SQLIA) of the processed features. These algorithms constitute a heterogeneous ensemble system, and final predictions are generated through a consensus model employing majority voting. The model's experimental results demonstrate its superiority over existing methods, as it achieves an accuracy of 98.6%, an F measure of 0.993, and an AUC of 0.999 with CBOW features. However, scalability problems emerge when implementing this method in big cloud infrastructure because of the challenging nature of hidden Markov models. The system faces reduced effectiveness because it depends on transductive learning when dealing with previously unknown attack patterns and data types. As mentioned in [7], IoT security requires an IDS to monitor sensor data and prevent zero-day threats. The information security system relies on FDLNN to overcome signature-based detection. However, MKM-based clustering for cluster development using MQTT and AMQP communication with FDLNN intrusion detection and LFEHO-based data distribution enables sensor network configuration before head selection. Standard IDSs manage different IoT data types and detect threats through known and unknown intrusion detection and by distinguishing harmful activity from benign activity, making them unsuitable for this architecture. FDLNN surpassed all deep learning and neural network models for intrusion detection on the TON_IoT dataset, with 96.12% accuracy and outstanding precision and recall. LFEHO outperformed IANFIS and ANFIS regarding data distribution qualities like turnaround, responsiveness, and throughput. A previous study combined powerful clustering and communication protocols with deep learning detection methods to provide a precise, lightweight IoT IDS. However, the proposed deep learning-based intrusion detection system encounters performance limitations associated with high processing demands, causing problems on resource-limited IoT devices. The model exhibits reduced performance capability when faced with novel or unobserved attack patterns during operations not appearing in previous training data. The study reported in [8] presents a deep learning framework driven by SDN to improve security and efficiency in Healthcare IoT (H-CIoT) networks. Because of the dynamic nature and complexity of H-IoT devices, traditional network models are inadequate for ensuring security and QoS. Utilizing Software-Defined Networking (SDN), the proposed framework separates control and data planes, enabling flexible management of distributed network components. A Bidirectional LSTM (BiLSTM) module is utilized to identify different types of cyber attacks in the network. In addition, the DL model is trained using the patient's medical history to make intelligent decisions about data transmission to the fog nodes. A CNN-based module is included to determine the fog node that offers the highest efficiency for data processing.