# Graph-Based Multi-Label Classification for WiFi Network Traffic Analysis
## 2 Related Works  
Recent developments in communication networks allowed us to build faster and more reliable communication services which can be accessed more easily and safely thanks to several security protocols available. The complexity of this new kind of technology requires new approaches to perform monitoring, anomaly detection and intrusion detection both to evaluate Quality of Service Key Performance Indicators [4; 5] and to check activity from potential attackers [6; 7]. Furthermore, since this aspect concerns both wired and wireless communications technologies, new paradigms are studied in both ways, such as Software Defined Networking (SDN). In fact, [PERSON] et al. in [6] provide a study concerning the usage of the SDN paradigm to perform both centralized and distributed intrusion detection in resource-constrained wireless networks. High data rate, low latency, and high volumes of data require a set of processing techniques that should be easily scalable both in terms of capacity and functionality. For this purpose, adaptive signal processing and machine learning provide exactly these kinds of capabilities, thanks to the possibility to build models from sets of examples and multiple ways to scale to previously unseen situations. [PERSON] et al., for example, studied machine learning-based performance prediction algorithms for SDN environments [8], providing case studies both on real and synthetic networks. Results taken from a centralized and a distributed architecture show the feasibility to perform network status prediction based on the considered features. In this context, wireless communications are a flexible technology able to connect different kinds of devices, from enterprise environments to industrial Internet of Things. Both 5G and IEEE 802.11 WiFi standards offer key capabilities for deployment in such scenarios, so they are subject to constant security reviews. In fact, in [9], in order to perform a security analysis of the 5G Core Network components, the authors employ a machine learning approach to assist in the definition and processing of attack graphs. In this way, they have been able to find multiple vulnerabilities and novel attacks on the Authentication and Key Agreement protocol. Concerning IEEE 802.11 WiFi networks, attack analysis and intrusion detection have been particularly studied. Starting from the publication of the Aegean WiFi Intrusion Detection dataset in [10], multiple works have tackled the problem. Notably, in [11], [PERSON] et al. proposed a new approach based on Particle Swarm algorithms able to achieve an interesting level of accuracy and, at the same time, generate a set of _IF-THEN_ human-readable rules. Despite multiple works having achieved interesting results by processing individual network packets and frames (e.g., by means of several techniques not only related to Deep Learning [12; 13]), processing network flows is still required to achieve state-of-the-art performance able to handle deployments in real scenarios [14]. Moreover, it is important to notice the changes, which are constantly evolving, that network technology and services are subject to, due to evolving quality of service and security requirements. Hence, computational intelligence models further require increasingly-deeper knowledge discovery capabilities, simplifying human interpretability and support in decision-making. As an attempt to overcome this limitation, in this work, we further study these kinds of problems, redefining the original supervised problem in the graph domain. This allows us to model the relationships linking raw WiFi frames, with a Granular Computing-based approach able to describe network status on multiple semantic levels. Graph signal processing and machine learning are a florid research field when it comes to dealing with structured data such as graphs. These kinds of approaches have been proven successful in multiple scenarios, from bioinformatics to social networks [15].