## 3 The ML Strategy within Network Slicing
### Practical Applications  
Numerous instances in the literature showcase the application of AI to bolster security within network slicing. This section aims to elucidate several examples, providing an overview of their implementations and outcomes. Authors of [89] have proposed the _Secure5G_ framework aimed at fortifying the security of network slicing functionalities within 5G networks. This framework adopts a network slicing model driven by deep learning CNNs. It is strategically designed to pre-emptively identify and neutralise potential risks posed by incoming connections before they penetrate the core of the 5G network. The framework aims to achieve various objectives, including detecting and mitigating Distributed Denial of Service (DDoS) attacks, analysing traffic patterns, predicting future traffic trends, resource allocation to optimise slice performance, and detecting unauthorised operations through User Equipment. It also keeps a detailed database of devices and how users behave, learning from this information over time. This includes all past and present connection requests from any device. Introducing a new concept termed \"Quarantine Slice,\" the framework proposes a unique approach to mitigate attacks by deploying a slice with minimal Quality-of-Service (QoS) parameters and stringent requirements. Building upon the foundation laid by the _DeepSlice_[90] research, _Secure5G_ was evaluated with volume-based flooding and spoofing attack scenarios, achieving a detection accuracy rate exceeding 98%. Future endeavours are poised to refine the framework's capabilities further, focusing on real-time model training. The authors of [91] introduced the DeepSecure framework, leveraging Long Short-Term Memory (LSTM) deep learning techniques to develop models for predicting slices and detecting attacks within 5G network environments. The attack detection model, powered by LSTM, predicts DDoS attacks originating from User Equipment network traffic, while the slice prediction model anticipates appropriate slices for authorised User Equipment. DeepSecure shares similarities with the previously discussed _Secure5G_[89] framework. Evaluation of DeepSecure utilised the CICDDoS2019 [92] dataset, with training parameters for attack detection and slice prediction models including learning rate, activation function, optimizer, and epochs. The tests resulted in a 99.970% detection accuracy, surpassing the performance of the _Secure5G_[89] framework. The framework described by [93] introduces Intelligence Slicing, a unified AI framework tailored for the software-defined virtualized 5G infrastructure, offering a holistic approach to network management and security. The key features of this framework include the cooperation between SDN and NFV. A notable aspect of the framework is the introduction of \"intelligence slicing\", a concept allowing AI functional modules, or \"intelligence slices\", to be deployed on demand within the network. These slices are designed to execute specific intelligent tasks and can utilise the most suitable AI algorithms optimised for the task at hand. The framework also incorporates a specialised \"security intelligence slice\" to address security challenges within industrial networks connected to the 5G infrastructure. This slice employs Machine Learning-based anomaly detection algorithms such as Random Forest and Support Vector Machine (SVM). The efficacy of the anomaly detection algorithms utilised within the security intelligence slice is thoroughly evaluated using industrial network datasets; the DS1, DS2, and DS3 datasets were provided by [PERSON] and [PERSON] [94], and they are focused on detecting malware. The results show good performance metrics, including high detection accuracy and F1-score, such as an accuracy of 100% using the DS1 dataset and both algorithms. Authors from [95] proposed a framework for managing resource allocation within network slicing scenarios for cyber-physical systems under DDoS attacks.