# Poisoning Attacks against Communication and Computing Task Classification and Detection Techniques
## 2 Literature Review and Paper Contributions  
### Classification  
Several approaches are designed to address task allocation in edge computing. Some use a centralized controller that controls the entire network. Others proposed decentralized techniques that allow each node in the network to allocate independently. Hybrid techniques use the advantages of both of the previous approaches [3; 4; 5; 6]. However, these techniques can be energy-exhaustive and suboptimal due to the lack of global information about the network. The idea behind addressing the communication part comes from the analysis of the energy consumption of such systems. As a result, researchers started to focus on the communication and computing parts jointly [7; 8]. However, given the limited knowledge of the network components at the edge, optimal communication and computing (2C) task allocation to the appropriate servers is a challenging problem.  
Clustering as unsupervised learning is utilized for discovering the patterns of the data [9; 10; 11]. This can be used to assist near-optimal 2C task classification. Clustering  
\begin{table}
\begin{tabular}{c c c} \hline
**Attack** & **Description** & **Example for 2C Task Classification** \\ \hline Trojan (Backdoor) Attacks & Combination of evasion and causative attacks, where the adversary injects triggers (backdoors) into training data and then activates them for some input samples in test time. & This can be used, for example, to target specific types of data such as mission-critical data, and these data will be wrongly classified as VMs of 2C link with lower QoS. \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._algorithms such as density-based spatial clustering of applications with noise (DBSCAN) were extensively used for general data clustering [12; 13; 14; 15; 16; 17; 18; 19; 20; 21]. However, these techniques are computationally expensive for large datasets. They may thus fail to satisfy the ultra-low-latency requirement from the communication perspective and the computational efficiency requirement from the computing perspective. A quick and efficient technique for huge datasets that addresses this issue is _k-means_[22]. This technique was used for task classification in edge computing [23]. But, implementing _k-means_ on 2C classification data with non-spherical clusters is impracticable. Furthermore, the technique requires an initial cluster number (k), and the choice can be far from the optimal k value. As a result, extra tools to define the optimal k value must be considered. In addition, the initial centroids are chosen randomly from the dataset, and with different combinations, the clustering results can be different. Moreover, because the clustering scheme will not change after a few new examples in the dataset, it is impractical to classify each new request using the same process.  
As a result, these new requests will be predicted using a less complex approach. Supervised learning techniques, as they are less complex techniques, are a good choice [24; 25]. However, the chosen approach must be adaptable to _k-means_ modifications after each individual epoch, and the lack of a training dataset restricts the considerations to non-training phase techniques. The choice in this case is k\({}^{\prime}\)-nearest neighbors (_k\({}^{\prime}\)-NN_) [26], where new requests are assigned to the nearest cluster based on a metric.  
The method allocates each example based on the neighbors' majority voting, although the technique is sensitive to the number of k\({}^{\prime}\) neighbors. If k\({}^{\prime}\) is reduced, then _k\({}^{\prime}\)-NN_ may overfit the _k-means_ data. Higher k\({}^{\prime}\) values, on the other hand, lead to greater computing complexity.