# A Complete EDA and DL Pipeline for Softwarized 5G Network Intrusion Detection
###### Abstract  
The rise of 5G networks is driven by increasing deployments of IoT devices and expanding mobile and fixed broadband subscriptions. Concurrently, the deployment of 5G networks has led to a surge in network-related attacks, due to expanded attack surfaces. Machine learning (ML), particularly deep learning (DL), has emerged as a promising tool for addressing these security challenges in 5G networks. To that end, this work proposed an exploratory data analysis (EDA) and DL-based framework designed for 5G network intrusion detection. The approach aimed to better understand dataset characteristics, implement a DL-based detection pipeline, and evaluate its performance against existing methodologies. Experimental results using the 5G-NIDD dataset showed that the proposed DL-based models had extremely high intrusion detection and attack identification capabilities (above 99.5% and outperforming other models from the literature), while having a reasonable prediction time. This highlights their effectiveness and efficiency for such tasks in softwarized 5G environments.  
5G networks; intrusion detection; exploratory data analysis; deep learning models +
Footnote †: journal: _Future Internet_  
0000-0001-122  
recorded in 2019, marking a threefold increase from 2018 [10]. Similarly, a report from Sonicwall indicated a 215.7% increase in IoT malware attacks between 2017 and 2018 [10]. While researchers have suggested various methods to safeguard these environments and networks, including software-defined perimeter (SDP) solutions and improved attack detection mechanisms [11; 12; 13; 14], there remains a need for more effective detection mechanisms to address security concerns in 5G-enabled networks.  
Machine learning (ML) in general, and deep learning (DL) specifically, has emerged as a promising solution for detecting security threats in 5G-enabled networks and environments, due to its ability to handle the vast amount of data generated. ML systems can \"learn\" without explicit programming, enhancing system flexibility and adaptability [15]. This approach has proven effective and computationally efficient in various applications [16; 17; 18], making it an ideal candidate for intrusion detection mechanisms in 5G networks.  
Accordingly, this work proposes a complete exploratory data analysis (EDA) and deep learning (DL) pipeline that comprehensively described the considered dataset and correspondingly motivated the choice ML algorithms. As such, a summary of this work's main contributions is as follows:  
* _Apply_ exploratory data analysis techniques to better understand the considered dataset's characteristics.
* _Propose_ a complete DL pipeline for softwarized 5G network intrusion detection.
* _Evaluate_ the performance of the different algorithms and compare them to other literature works.  
It is worth noting that, in this context, softwarized 5G network intrusion detection refers to the process of developing intrusion detection frameworks to be deployed as software-based network functions that can run on generic computing devices, rather than requiring dedicated proprietary computing hardware.