## 6 Data Analysis
### Techniques Based on Authenticity Requirements:
In addition, security isolation methods allow users to separate personal data from corporate space. In these various ways, cryptographic and isolation techniques constitute a requirement for confidentiality. In a BYOD scenario, personal and company data are stored on the same device. There are numerous benefits to integrating BYOD devices into the company infrastructure. However, this causes safety issues such as space isolation, data confidentiality, policy compliance and vulnerabilities since small and medium-sized enterprises cannot afford a suitable product solution that allows personal and professional data to coexist securely on employees' devices. Previous research has suggested isolation-based methods. [PERSON] et al. [62] proposed a remote mobile screen (RMS) based on the virtualization method to solve these problems. RMS allows for data confidentiality and space isolation. However, the complexity of managing physical roles is challenging. In addition, an essential real-world scenario in BYOD requires individuals to be isolated from the enterprise's privileged information access, according to [PERSON] et al. [61]. They proposed an architecture called vNative that builds one foreground virtual machine (FVM), providing data confidentiality and isolation. However, the proposed solution is restricted to a few mobile intelligent devices. The proposed virtual network function (VNF) scheme is described in [63]. The suggested approach enables users to take advantage of the NFV server's more powerful corporate resources and increases service quality regarding security, mobility, and response time. It features a prototype technique [64] that takes advantage of virtualization characteristics common in today's mobile processors to fulfill this isolation demand. In addition, this solution offers typical Android users security and privacy features. Table 8 summarises the isolation strategies discussed in previous studies. **D. Technique-Based Attack Detection Requirement:**  
According to previous research, there are numerous approaches for identifying attacks and preventing and monitoring risks. [PERSON] and [PERSON] [70] suggested a network-based risk identification tool based on the ML algorithm. The goal was to detect and evaluate malware on infected mobile devices under BYOD. However, this strategy cannot be employed for applications using log files. [PERSON] et al. [76] improved the method based on a machine learning algorithm to detect denial of service assaults, probing attacks, and torrent traffic in BYOD. The proposed approach is adaptable and flexible, lowering costs. [PERSON] et al. [80] proposed a supervised classification-based malware detection technique for Android BYOD devices. It provides a self-adaptive network capable of protecting critical services and defending against internal attacks and should be upgraded and expanded to detect advanced APTs. Ref. [72] proposed method based on clustering algorithms that may improve malware detection in BYOD was used with a simulation data set and a public dataset and achieved high precision. Ref. [69] employed a risk access scheme based on NGFW (next-generation firewall) technology, and a deep packet inspection firewall is used to protect a campus network. This method tracks and categorises risk packages. The experiment was analysed using a case study (Tokyo University). It reduced costs while increasing security. However, it is weaker because package content needs to be verified for network traffic filtering. Furthermore, [PERSON] et al. [71] proposed a risk-based access control model based on a dynamic risk estimation method, using features to analyse each request's security risk. The algorithm evaluates access based on user context, resource sensitivity, action severity, and risk history to represent an end-to-end ecosystem. [PERSON] et al. [84] presented a framework for detecting anomalous behaviour and unauthorised access to BYOD devices using artificial neural networks (ANN and data mining.