# Poisoning Attacks against Communication and Computing Task Classification and Detection Techniques
## 1 Introduction  
Radio communication technology has continuously been advancing for the past few decades. Recent developments such as fifth-generation (5G) mobile radio communication promise to take connectivity to new heights. In comparison with their predecessors, 5G and 6G wireless communications deliver faster speeds, lower latency, higher reliability, and increased capacity for anticipated services. The increased bandwidth and reduced latency of 5G enable a wide range of applications, including autonomous cars, robotics, virtual reality, and the Internet of Things (IoT). It promotes the establishment of these new values by enabling the development of new services in three major use case domains: enhanced mobile broadband (eMBB), ultra-reliable low-latency communication (URLLC), and massive machine-type communications (mMTC). While 5G is already pushing the boundaries of wireless communication, researchers, engineers, and industry practitioners have begun to look beyond it. Beyond 5G, wireless network development, often called sixth generation (6G) or beyond 5G, aims to break down barriers and explore new frontiers. One of the primary goals of wireless communication beyond 5G is to push the boundaries of latency, dependability, connection, and coverage. Although the specifics of the technology are now speculative, numerous critical areas have emerged as possible priority areas for future wireless communication system development. According to the recent Ericsson Mobility Report [1], 5 billion 5G mobile subscribers will be operating by 2028. Furthermore, wireless communication of 34.7 billion machines and devices is predicted to compose the IoT by that year. This expected massive human-machine communication means tremendous data flow across communication channels. Moreover, emerging applications such as intelligent healthcare, intelligent transportation, and interactive gaming impose more demanding quality of service (QoS) requirements. Edge computing is emerging as a unique computing paradigm that leverages cloud computing and pushes it closer to the edge to deal with these challenges. The development of edge-computing networks may lead to the reduction of communication latency since the networking distance between the end devices and processing (computing) nodes will be shortened. It will also enable local data processing, which is critical for applications requiring real-time decision making and instant reaction. Furthermore, rather than burdening the main network or cloud data centers with all computing requirements, the edge nodes can execute localized jobs and alleviate the central infrastructure, a practice known as task offloading. To fully benefit from edge computing, edge intelligence using artificial intelligence (AI) and machine learning (ML) is an essential paradigm. For example, optimal communication and computing (2C) task offloading to the appropriate servers is a challenging problem, and its optimization is not always possible, given the limited knowledge of the network components at the edge. Therefore, ML can be used to classify and predict the generated 2C requests. This classification is intended to support the near-optimal delegation of a request to an appropriate server at the edge of a network. To enable the execution of edge intelligence apps and services, the mobile edge host in the future 5G or 6G architecture operates a mobile edge platform. The 5G or 6G network design will be almost totally virtualized and dependent on the software features. As a result, it is open to being utilized, attacked, and interrupted by hackers. In the case of sensitive applications, such as mission-critical ones that need URLLC service, attacks against ML algorithms used for traffic steering may result in disastrous failures. In the edge-computing network under consideration, 2C tasks should be classified based on service performance requirements (for example, end-to-end latency, packet error rate (PER), or the computational complexity of a delegated task).