Federated Learning (FL) is a machine learning (ML) approach that enables multiple decentralized devices or edge servers to collaboratively train a shared model without exchanging raw data. During the training and sharing of model updates between clients and servers, data and models are susceptible to different data-poisoning attacks. In this study, our motivation is to explore the severity of data poisoning attacks in the computer network domain because they are easy to implement but difficult to detect. We considered two types of data-poisoning attacks, label flipping (LF) and feature poisoning (FP), and applied them with a novel approach. In LF, we randomly flipped the labels of benign data and trained the model on the manipulated data. For FP, we randomly manipulated the highly contributing features determined using the Random Forest algorithm. The datasets used in this experiment were CIC and UNSW related to computer networks. We generated adversarial samples using the two attacks mentioned above, which were applied to a small percentage of datasets. Subsequently, we trained and tested the accuracy of the model on adversarial datasets. We recorded the results for both benign and manipulated datasets and observed significant differences between the accuracy of the models on different datasets. From the experimental results, it is evident that the LF attack failed, whereas the FP attack showed effective results, which proved its significance in fooling a server. With a 1% LF attack on the CIC, the accuracy was approximately 0.0428 and the ASR was 0.9564; hence, the attack is easily detectable, while with a 1% FP attack, the accuracy and ASR were both approximately 0.9600, hence, FP attacks are difficult to detect. We repeated the experiment with different poisoning percentages.

翻译：联邦学习（FL）是一种机器学习（ML）方法，允许多个分散的设备或边缘服务器在不交换原始数据的情况下协同训练共享模型。在客户端与服务器之间进行模型更新的训练与共享过程中，数据与模型易遭受不同类型的数据投毒攻击。在本研究中，我们的动机在于探索计算机网络领域中数据投毒攻击的严重性，因为此类攻击易于实施却难以检测。我们考虑了两种数据投毒攻击——标签翻转（LF）与特征投毒（FP），并采用新颖方法加以应用。在LF攻击中，我们随机翻转良性数据的标签，并在被篡改的数据上训练模型；对于FP攻击，我们则利用随机森林算法确定的高贡献特征，对其进行随机篡改。实验所用数据集为与计算机网络相关的CIC和UNSW数据集。我们利用上述两种攻击生成了对抗样本，并将其应用于小比例的数据集。随后，我们在对抗数据集上训练并测试了模型的准确率。我们记录了良性数据集与篡改数据集上的结果，观察到模型在不同数据集上的准确率存在显著差异。从实验结果可知，LF攻击失效，而FP攻击展现出有效结果，这证明了其在欺骗服务器方面的重要性。对CIC数据集施加1%的LF攻击时，准确率约为0.0428，ASR为0.9564，因此该攻击易于检测；而施加1%的FP攻击时，准确率和ASR均约为0.9600，因此FP攻击难以检测。我们以不同的投毒比例重复了实验。