BoAu: Malicious traffic detection with noise labels based on boundary augmentation

Adversarial training (AT) has shown impressive advantages in maintaining accuracy and enhancing robustness against adversarial examples. However, most existing AT techniques jointly optimize clean-example accuracy and adversarial-example robustness as dual objectives. This setting introduces an often overlooked issue; when optimizing hard samples near the decision boundary, the model may bolster robustness at the expense of accuracy or preserve accuracy to the detriment of robustness. To alleviate the accuracy-robustness sacrifices induced by hard samples, we propose mitigating hard sample interference (MHSI) from a sample-intervention perspective. MHSI aims to reduce the instability caused by hard samples during AT. Specifically, we introduce a weighted adaptive (WA) mechanism that strengthens the model's learning of clean samples, thereby reducing the negative impact of hard samples on accuracy. In addition, guided by an analysis of the gradient norm and the Hessian matrix, we design a dynamic calibration (DC) strategy that dynamically calibrates the probability outputs of hard samples to mitigate their damage to robustness. With these two modules, our approach significantly improves robustness without sacrificing accuracy. Extensive experiments on CIFAR-10, CIFAR-100, Tiny ImageNet, and SVHN demonstrate that MHSI effectively improves both accuracy and robustness and outperforms state-of-the-art methods under glass-box attacks. Notably, under an $l_{\infty }$ attack, MHSI yields up to a 6.22% robustness gain over the AT baseline. Our code is available at https://github.com/hubin111/MHSI.

Collecting large-scale data with clean labels for supervised training of neural networks is practically challenging. Although noisy labels are usually cheap to acquire, existing methods suffer a lot from label noise. This paper targets at the challenge of robust training at high label noise regimes. The key insight to achieve this goal is to wisely leverage a small trusted set to estimate exemplar weights and pseudo labels for noisy data in order to reuse them for supervised training. We present a holistic framework to train deep neural networks in a way that is highly invulnerable to label noise. Our method sets the new state of the art on various types of label noise and achieves excellent performance on large-scale datasets with real-world label noise. For instance, on CIFAR100 with a $40\%$ uniform noise ratio and only 10 trusted labeled data per class, our method achieves $80.2{\pm}0.3\%$ classification accuracy, where the error rate is only $1.4\%$ higher than a neural network trained without label noise. Moreover, increasing the noise ratio to $80\%$, our method still maintains a high accuracy of $75.5{\pm}0.2\%$, compared to the previous best accuracy $48.2\%$. Source code available: https://github.com/google-research/google-research/tree/master/ieg

Federated learning (FL) is a privacy-preserving distributed learning paradigm that enables clients to jointly train a global model. In real-world FL implementations, client data could have label noise, and different clients could have vastly different label noise levels. Although there exist methods in centralized learning for tackling label noise, such methods do not perform well on heterogeneous label noise in FL settings, due to the typically smaller sizes of client datasets and data privacy requirements in FL. In this paper, we propose FedCorr, a general multi-stage framework to tackle heterogeneous label noise in FL, without making any assumptions on the noise models of local clients, while still maintaining client data privacy. In particular, (1) FedCorr dynamically identifies noisy clients by exploiting the dimensionalities of the model prediction subspaces independently measured on all clients, and then identifies incorrect labels on noisy clients based on persample losses. To deal with data heterogeneity and to increase training stability, we propose an adaptive local proximal regularization term that is based on estimated local noise levels. (2) We further finetune the global model on identified clean clients and correct the noisy labels for the remaining noisy clients after finetuning. (3) Finally, we apply the usual training on all clients to make full use of all local data. Experiments conducted on CIFAR-10/100 with federated synthetic label noise, and on a real-world noisy dataset, Clothing1M, demonstrate that FedCorr is robust to label noise and substantially outperforms the state-of-the-art methods at multiple noise levels.

Breast ultrasound image classification with hard sample generation and semi-supervised learning

We investigate the geometric complexity of decision boundaries for robust training compared to standard training. By considering the local geometry of nearest neighbour sets, we study them in a model-agnostic way and theoretically derive a lower-bound \(R^*\in \mathbb {R}\) on the perturbation magnitude \(\delta \in \mathbb {R}\) for which robust training provably requires a geometrically more complex decision boundary than accurate training. We show that state-of-the-art robust models learn more complex decision boundaries than their non-robust counterparts, confirming previous hypotheses. Then, we compute \(R^*\) for common image benchmarks and find that it also empirically serves as an upper bound over which label noise is introduced. We demonstrate for deep neural network classifiers that perturbation magnitudes \(\delta \ge R^*\) lead to reduced robustness and generalization performance. Therefore, \(R^*\) bounds the maximum feasible perturbation magnitude for norm-bounded robust training and data augmentation. Finally, we show that \(R^*< 0.5R\) for common benchmarks, where R is a distribution’s minimum nearest neighbour distance. Thus, we improve previous work on determining a distribution’s maximum robust radius.

Label noise in medical image classification datasets significantly hampers the training of supervised deep learning methods, undermining their generalizability. The test performance of a model tends to decrease as the label noise rate increases. Over recent years, several methods have been proposed to mitigate the impact of label noise in medical image classification and enhance the robustness of the model. Predominantly, these works have employed CNN-based architectures as the backbone of their classifiers for feature extraction. However, in recent years, Vision Transformer (ViT)-based backbones have replaced CNNs, demonstrating improved performance and a greater ability to learn more generalizable features, especially when the dataset is large. Nevertheless, no prior work has rigorously investigated how transformer-based backbones handle the impact of label noise in medical image classification. In this paper, we investigate the architectural robustness of ViT against label noise and compare it to that of CNNs. We use two medical image classification datasets-COVID-DU-Ex, and NCT-CRC-HE-100K-both corrupted by injecting label noise at various rates. Additionally, we show that pretraining is crucial for ensuring ViT's improved robustness against label noise in supervised training.

Siamese network-based visual tracking algorithms have achieved excellent performance in recent years, but challenges such as fast target motion, shape and scale variations have made the tracking extremely difficult. The regression of anchor-free tracking has low computational complexity, strong real-time performance, and is suitable for visual tracking. Based on the anchor-free siamese tracking framework, this paper firstly introduces balance factors and modulation coefficients into the cross-entropy loss function to solve the classification inaccuracy caused by the imbalance between positive and negative samples as well as the imbalance between hard and easy samples during the training process, so that the model focuses more on the positive samples and the hard samples that make the major contribution to the training. Secondly, the intersection over union (IoU) loss function of the regression branch is improved, not only focusing on the IoU between the predicted box and the ground truth box, but also considering the aspect ratios of the two boxes and the minimum bounding box area that accommodate the two, which guides the generation of more accurate regression offsets. The overall loss of classification and regression is iteratively minimized and improves the accuracy and robustness of visual tracking. Experiments on four public datasets, OTB2015, VOT2016, UAV123 and GOT-10k, show that the proposed algorithm achieves the state-of-the-art performance.

Learning with noisy labels aims to train neural networks with noisy labels. Current models handle instance-independent label noise (IIN) well; however, they fall short with real-world noise. In medical image classification, atypical samples frequently receive incorrect labels, rendering instance-dependent label noise (IDN) an accurate representation of real-world scenarios. However, the current IDN approaches fail to consider the typicality of samples, which hampers their ability to address real-world label noise effectively. To alleviate the issues, we introduce typicality- and instance-dependent label noise (TIDN) to simulate real-world noise and establish a TIDN-combating framework to combat label noise. Specifically, we use the sample’s distance to decision boundaries in the feature space to represent typicality. The TIDN is then generated according to typicality. We establish a TIDN-attention module to combat label noise and learn the transition matrix from latent ground truth to the observed noisy labels. A recursive algorithm that enables the network to make correct predictions with corrections from the learned transition matrix is proposed. Our experiments demonstrate that the TIDN simulates real-world noise more closely than the existing IIN and IDN. Furthermore, the TIDN-combating framework demonstrates superior classification performance when training with simulated TIDN and actual real-world noise.

A generalised label noise model for classification in the presence of annotation errors

Existing multi-label learning algorithms mostly assume that all class labels in an obtained data set are completely accurate. This assumption is rarely valid. As the scale of the label space increases, owing to limited domain knowledge and differences among methods of data acquisition, some examples will be unlabeled or mislabeled. The introduction of such noise results in the poor performance of trained multi-label classifiers. This paper proposes a new method for multi-label learning with label noise, called low-rank-constraint-based multi-label learning with label noise (ML-LRC), by incorporating low-rank matrix recovery and features selection into the learning process. First, we assume that two strongly correlated labels share more samples than two weakly correlated labels. Based on this assumption, the ML-LRC applies low-rank constraints to the label matrix to mine the local correlations of class labels and remove noise from the observed label matrix. Second, we believe that each class label only correlates to some features. So we control the sparsity of the coefficient matrix to filter out label-specific features, which can reduce the complexity of the model and greatly improve the performance of the classifier. The accelerated proximal gradient method and alternating direction method of multipliers are adopted to solve the problem. Experiments conducted on four benchmark multi-label datasets demonstrate the competitive performance of our proposed method compared with several state-of-the-art methods.

Federated Learning (FL) enables massive local data owners to collaboratively train a deep learning model without disclosing their private data. The importance of local data samples from various data owners to FL models varies widely. This is exacerbated by the presence of noisy data that exhibit large losses similar to important (hard) samples. Currently, there lacks an FL approach that can effectively distinguish hard samples (which are beneficial) from noisy samples (which are harmful). To bridge this gap, we propose the joint Federated Meta-Weighting based Client and Sample Selection (FedMW-CSS) approach to simultaneously mitigate label noise and hard sample selection. It is a bilevel optimization approach for FL client-and-sample selection and global model construction to achieve hard sample-aware noise-robust learning in a privacy preserving manner. It performs meta-learning based online approximation to iteratively update global FL models, select the most positively influential samples and deal with training data noise. To utilize both the instance-level information and class-level information for better performance improvements, FedMW-CSS efficiently learns a class-level weight by manipulating gradients at the class level, e.g., it performs a gradient descent step on class-level weights, which only relies on intermediate gradients. Theoretically, we analyze the privacy guarantees and convergence of FedMW-CSS. Extensive experiments comparison against eight state-of-the-art baselines on six real-world datasets in the presence of data noise and heterogeneity shows that FedMW-CSS achieves up to 28.5% higher test accuracy, while saving communication and computation costs by at least 49.3% and 1.2%, respectively.

Malicious traffic detection is an important technique for network management, assisting network administrators in identifying malicious hosts and activities. With the growing proportion of encrypted traffic, recent studies incorporate Machine Learning (ML) and Deep Learning (DL) methods. These methods can achieve effective classification but rely heavily on large-scale and high-quality datasets. Due to the coarse-grained traffic collection at the level of hosts or switches in existing datasets, the introduction of noisy traffic degrades the performance of malicious traffic detection models. In this paper, to tackle the efficacy challenge caused by noisy traffic, we propose a traffic dataset purification framework named PureFlow. Specifically, PureFlow adopts an unsupervised clustering algorithm to categorize the collected traffic based on their sources, and thus overcome the feature confusion caused by mixing noisy traffic from different sources. With a set of autoencoders based on reconstruction loss, PureFlow can distinguish the feature differences between noisy and valid traffic. We conduct extensive experiments on public datasets. The results show that PureFlow can effectively filter noisy traffic and significantly enhance the performance of several malicious traffic detection models without additional modifications, achieving average accuracy and F1-score improvements of 5.58% and 0.056, respectively.

Graph contrastive learning has emerged as a powerful unsupervised graph representation learning tool. The key to the success of graph contrastive learning is to acquire high-quality positive and negative samples as contrasting pairs to learn the underlying structural semantics of the input graph. Recent works usually sample negative samples from the same training batch with the positive samples or from an external irrelevant graph. However, a significant limitation lies in such strategies: the unavoidable problem of sampling false negative samples. In this paper, we propose a novel method to utilize Counterfactual mechanism to generate artificial hard negative samples for Graph Contrastive learning, namely CGC. We utilize a counterfactual mechanism to produce hard negative samples, ensuring that the generated samples are similar but have labels that differ from the positive sample. The proposed method achieves satisfying results on several datasets. It outperforms some traditional unsupervised graph learning methods and some SOTA graph contrastive learning methods. We also conducted some supplementary experiments to illustrate the proposed method, including the performances of CGC with different hard negative samples and evaluations for hard negative samples generated with different similarity measurements. The implementation code is available online to ease reproducibility1.

Active Dynamic Weighting for multi-domain adaptation

Deep learning is widely used in hyperspectral image (HSI) classification due to its powerful learning capabilities. However, its excellent performance typically requires a large number of samples, which can be time-consuming and labor-intensive to produce. The limitation of available samples greatly constrains the model's generalization performance. To alleviate the sample pressure, we propose a semi-supervised metric learning method for HSI classification that focuses on hard samples and can obtain reliable pseudo-labels through multiscale prediction. Additionally, we employ a hard sample learning strategy for network training, which concentrates on enhancing network discrimination by adaptively optimizing intra-class and inter-class distances. Performance tests on three public datasets indicate that the proposed method surpasses other state-of-the-art methods across multiple validation metrics.