## 2 Related Work and Paper Contributions  
### Related Work  
Several studies have been conducted on SIP analysis. In [20], the authors present an analysis for SIP-based CDRs using a CDR Assembler, showing the most critical messagesand interfaces in SIP CDRs to monitor and to counterfeit challenges for CDR generation. In [21], the authors investigate the SIP signaling performance for LTE-based Mission Critical Systems (MCS) using a discrete event simulator. Ref. [22] evaluates the performance of the SIP protocols in an IMS architecture with the Multi-Protocol Label Switching (MPLS) protocol. The authors demonstrate the impact of the number of connections on the duration of SIP session establishment and the impact of the number of users on the performances of Voice Over IP (VOIP) and video conference services. In [23], the authors evaluate the performance of the VOIP performance over the SIP architecture homogeneous 802.11e network in the context of a horizontal handover, taking into account the MIPv6 technology. In [24], a Deep Packet Inspection (DPI) method classification technique was used to classify VoLTE traffic using the SIP User-Agent field. A SIP analysis on Voice Over IP (VoIP) is widely discussed in the literature. Ref [25] studies the performance impact of using key agreements, elliptic curve Diffie-Hellman and elliptic curve Menezes-Qu-Vanstone, for making a SIP-based VoIP call. Ref. [26] investigates the performance of VoIP technology over cloud computing, when protected against an attack. The authors investigate the SIP VoIP protocol Registration Request Delay (RRD) in cloud computing with security layer protection. One of the critical components of 5G networks will be the use of artificial intelligence and machine learning [27]. Machine learning applications in 5G networks have been investigated widely in the literature [18; 28; 29]. Machine learning has been used heavily in the literature to detect network and call failures and detect anomalies and degradation in the Quality of Service (QoS). The authors in [30] proposed a framework based on N-gram analysis to identify abnormal behavior in sequences of network events, then, different anomaly detection approaches are applied and compared. Based on Linear Support Vector Machines (L-SVMs) and Bagged Decision Trees (BDT) machine learning algorithms, authors in [31] proposed a failure detection model for eNodeB in an LTE network that carries Machine-to-Machine (M2M) smart city traffic. The results demonstrated that failure detection accuracy reaches 97.5%. Ref. [32] uses a Recurrent Neural Network (RNN) to detect the outages of cell radio and degradations of performance. In [33], the authors proposed a machine learning algorithm to monitor a Social Networking Service (SNS) (Twitter in particular) and detect the mobile network failure. In [34], the authors discussed the application of machine learning in wireless networks to solve performance prediction problems; they proposed a categorization of main problem types using an extensive real-world drive test data set. They showed that Gaussian process regression, exponential smoothing of time series, and random forests could yield good prediction results. In [35], the authors use k-means clustering to detect the anomalies in the network. After that, they use the autoregressive integrated moving average (ARIMA) model to predict future traffic for a user. Meanwhile, authors in [36] used the semi-supervised algorithm to detect anomalies in the network. To this end, using machine learning to detect the VoLTE call failures occurring in the core network (IMS) is not widely discussed. Few works investigate the failures in the IMS network. Ref. [37] describes the method for the detection of failures on emergency service centers in the IMS network. The failure detection system is based on charging events; the authors provide the analysis of alarms generated by this system. Ref. [38] investigates backup and restore schemes for SIP/IMS servers.