# Poisoning Attacks against Communication and Computing Task Classification and Detection Techniques
## 7 Simulation Results
### Results  
In this section, we provide the results of the simulated 2C task classification, the impact of _attack 1_ and _attack 2_ on this classification, and the results of the application of _method 1_ and _method 2_ for attack detection (and mitigation). For reference and comparison with our methods, the 2C requests assigned to the servers (the serving 2C links) in a round robin fashion [47] were considered. Since the ML technique is a real-time classifier and has to be data-empty, there was no way to cluster or classify using the ML algorithm in the first epoch or round. For that, 1000 extra-classified requests were used as an offline initial state to the ML data. These examples were not considered in the results. The dataset was fed to the ML classification system. The dataset (The 1000 examples without consideration of the extra ones was preprocessed using the _PowerTransformer_ scaler with the _Box-Cox_ method followed by _MinMaxTransformer_ (with min = 0, max = 100) was from the _Scikit-learn_ library [44]. The _PowerTransformer_ transforms the data in each axis to be Gaussian-like distributed. In addition, the _MinMaxTransformer_ gives a range of data between 0 and 100, and this made the _k-means_ and _k\({}^{\prime}\)-NN_ metrics (3D Euclidean distance) fair between all the dataset samples. This choice also came from the intention to set the third axis (third dimension) of the Gaussian-like distributed dataset. Keep in mind that this axis was simulated using a uniform distribution. This ensured that all the 3D dimensions of the dataset followed the same distribution type and range (i.e., Gaussian shape with a range [0, 100]). After serving all the 2C requests, the ML classification converged to eight distinct clusters, as shown in Figure 5. Since the usage pattern of 5G services is stochastic, the number of needed servers to serve the 2C tasks could not be defined. As a result, the silhouette score test was used to determine the optimum number of clusters. This was accomplished by choosing the number of servers (clusters) that had the highest silhouette score among all the possible combinations. We limited our consideration to only eight servers, while for future work, more servers will be considered. The 2C link servers were chosen as shown in Table 2. We considered the CPU and GPU computing for the servers (computing part) and diverse radio and wired channels with different propagation conditions that defined the PER and latency introduced by each link (communication part). The servers were assumed to be buffer unlimited (i.e., no task was lost). The benefits of the ML classifiers compared with the simple round robin technique are shown in Figure 6.  
The processing time represents the time all servers took to process the 2C tasks. The server CPUs were assumed to behave as round robin schedulers with parallel GPU processing. The 2C request was said to not be accepted if it was allocated to a link server that did not fulfill the minimum evaluation constraints of this task's service, while the quantity of the used server was the total number of servers used by the allocation technique. The hybrid _k-means/k\({}^{\prime}\)-NN_ method showed rather high gains against the round robin method. Even though only five servers were enabled to serve the eight clusters, around 40 seconds (40 s) less time was observed compared with the round robin method with eight servers. In addition, only 14 tasks were not accepted in terms of the evaluation constraints guaranteed by the assigned link server compared with 693 for the round robin method. Figure 5: Attack 3D representations. Figure 6: Machine learning (ML) vs. round robin (RR) results. Because the main focus of this paper is the security aspects of ML, only the round robin method was compared to the proposed algorithm to show that AI is optimal for assisting edge intelligence. Based on the MITRE techniques and tactics discussed earlier, we launched the attacks on virtual machines that ran the sources and control system infrastructure.