Pareto Front Learning (PFL) has been one of the effective means to resolve multi-objective optimization problems through exploring all optimal solutions to learn the entire Pareto front. Pareto Hypernetwork (PHN) is a new promising way to generate the sequence of Pareto-optimal solutions that can be further used as potential solutions to constitute the Pareto front. However, the existing PHN-based approaches suffer from two performance issues: They take as inputs human-crafted preference vector or chunk embedding, rather than the input data samples, and thus vulnerable to data distribution shifts. Such approaches cannot optimize all potential solutions when forming the Pareto front, as they merely optimize the loss pertaining to one single input at a time of optimization round. To improve the quality of the Pareto front, we propose IOP, a novel Idempotent-like Optimization method to learn the entire Pareto front accurately and enhance Hypernetwork's adaptability to distribution shifts. In particular, IOP performs idempotent-like optimization by exploiting manifold space mapping, so that the target networks generated by the optimized Hypernetwork can effectively handle samples with similar distributions of the input samples, without the pre-defined human-crafted inputs. IOP maximizes the Hypervolume indicator that is composed of all potential solutions at a higher level. Experimental results demonstrate that IOP outperforms the state-of-the-art methods by 4.7% on average in producing the Pareto front and has a 10.5% improvement in adaptability.

Time series anomaly detection is challenging due to the rarity of anomalies and the complexity and diversity of normal patterns. Most existing methods rely on a single hypothesis and learn feature patterns from a limited dataset, which restricts their generalization capabilities. At the same time, time series foundation models have shown promising results across multiple tasks due to their strong generalization capabilities. However, time series foundation models are less likely to achieve better performance in complex anomaly detection tasks. To address this issue, this paper introduces FOCA, a novel foundation-model-based one-class anomaly detection approach. This method preserves the generalization ability of the foundation model to capture normal variation patterns and provides a comprehensive feature space for one-class classification. It constrains normal features within a sufficiently small hypersphere to construct a decision boundary for detecting abnormal data. Furthermore, it is observed in practice that the introduction of fine-tuning techniques can further improve the performance of the method. Extensive experiments on two standard benchmark datasets demonstrate that our method outperforms the state-of-the-art approaches.

Accurately identifying network attacks is crucial for maintaining network security. However, these attacks are often hide within massive volumes of network traffic, posing significant challenges for traditional detection methods. Supervised learning approaches require substantial labeled data and struggle to adapt to unknown attack types, while unsupervised methods face difficulties in accurately pinpointing specific attack categories. To address these limitations, we propose a novel fewshot learning model for network flow attack classification based on graph contrastive learning. Our model leverages contrastive learning to enhance feature representation and generalization capabilities, enabling high-accuracy attack detection even with limited training data. Specifically, we first construct a multi- graph representation of network traffic and segment the data into snapshots. Then, we perform graph data augmentation within each snapshot to generate augmented sample pairs, which are used to pre-train the model via contrastive learning. Finally, we fine-tune the model parameters to achieve multi-class attack classification, leveraging the learned feature representations to identify various attack types, even those unseen during training. Experimental results demonstrate that our model exhibits excellent generalization ability and achieves high attack detection performance, even with limited training data.

Detecting time-series anomalies is extremely intricate due to the rarity of anomalies and imbalanced sample categories, which often result in costly and challenging anomaly labeling. Most of the existing approaches largely depend on assumptions of normality, overlooking labeled abnormal samples. While anomaly assumptions based methods can incorporate prior knowledge of anomalies for data augmentation in training classifiers, the adopted random or coarse-grained augmentation approaches solely focus on pointwise anomalies and lack cutting-edge domain knowledge, making them less likely to achieve better performance. This paper introduces CutAddPaste, a novel anomaly assumption-based approach for detecting time-series anomalies. It primarily employs a data augmentation strategy to generate pseudo anomalies, by exploiting prior knowledge of anomalies as much as possible. At the core of CutAddPaste is cutting patches from random positions in temporal subsequence samples, adding linear trend terms, and pasting them into other samples, so that it can well approximate a variety of anomalies, including point and pattern anomalies. Experiments on standard benchmark datasets demonstrate that our method outperforms the state-of-the-art approaches.

Time-series Anomaly Detection(AD) is widely used in monitoring and security applications in various industries and has become a hot spot in the field of deep learning. Normality-representation-based methods perform well in certain scenarios but may ignore some aspects of the overall normality. Feature-extraction-based methods always take a process of pre-training, whose target differs from AD, leading to a decline in AD performance. In this paper, we propose a new AD method called deep Autoencoding One-Class (AOC), which learns features with AutoEncoder(AE). Meanwhile, the normal context vectors from AE are constrained into a hypersphere small enough, similar to one-class methods. With an objective function that optimizes the two assumptions simultaneously, AOC learns various aspects of normality, which is more effective for AD. Experiments on public datasets show that our method outperforms existing baseline approaches.

Software Defined Network (SDN) enables network operators to achieve the customization of network services, which tends to be more dynamic and fine-grained. However, the distributed nature of rule updating in SDN brings consistency problems, i.e., packets travel according to different versions of rules. It leads to the issues of blackholes, loops, congestion, and deadlock in the data plane, which may further affect the service quality of the application plane. With the emergence of new computing paradigms such as edge computing and fog computing, the heterogeneity of network devices and links, as well as the diversity of network application requirements have become increasingly prominent. Traditional update methods ignore these key factors when modeling, so they cannot cope with the increasingly complex network environment, resulting in delays or packet loss rates that do not meet service requirements. This paper proposes HyCU, which takes device performance as a constraint and optimizes updates based on flow service requirements. We conduct experiments under different scenarios and constraints over two real-world topologies with real-time running flows, demonstrating the effectiveness of HyCU.

The accumulation of time-series data and the absence of labels make time-series Anomaly Detection (AD) a self- supervised deep learning task. Single-normality-assumption- based methods, which reveal only a certain aspect of the whole normality, are incapable of tasks involved with a large number of anomalies. Specifically, Contrastive Learning (CL) methods distance negative pairs, many of which consist of both normal samples, thus reducing the AD performance. Existing multi-normality-assumption-based methods are usually two-staged, firstly pre-training through certain tasks whose target may differ from AD, limiting their performance. To overcome the shortcomings, a deep Contrastive One-Class Anomaly detection method of time series (COCA) is proposed by authors, following the normality assumptions of CL and one-class classification. It treats the original and reconstructed representations as the positive pair of negative-sample-free CL, namely “sequence contrast”. Next, invariance terms and variance terms compose a contrastive one-class loss function in which the loss of the assumptions is optimized by invariance terms simultaneously and the “hypersphere collapse” is prevented by variance terms. In addition, extensive experiments on two real- world time-series datasets show the superior performance of the proposed method achieves state-of-the-art. *The full version of the paper can be accessed at https://arxiv.org/abs/2207.01472

As the Industrial Internet of Things (IIoT) develops, intelligent services applying stream computing, such as industrial robot health management, are requiring higher timeliness of data processing, which may involve scheduling of stream tasks. However, traditional scheduling methods are no longer suitable for the currently widely used cloud-edge collaboration mode, not considering the cloud-edge heterogeneity, and focusing on the scheduling of single tasks instead of the optimization of the total tasks. To improve the performance of the cloud-edge collaboration, this paper establishes a practical model for task scheduling considering respectively cloud-edge environment collaboration models. We propose a novel two-stage scheduling method for IIoT. The algorithm utilizes the idea of maximum flow to divide the task into cloud-edge deployment schemes and find the best partitioning scheme, and then deploy the operator for the edge domain based on the network topology by using dynamic programming. Experimental results show that the proposed method could reduce 7.27% the cloud-edge bandwidth usage compared with the highest greedy algorithm for traffic difference, 24.33% end-to-end latency and 11.18% back-pressure rate compared with SBON.

With the development of industrial automation and intelligence deepening, industrial robots' installations increase rapidly. The traditional robot operation and management methods are no longer suitable for current requirements. Cloud computing and big data technology together are deemed to form a promising and possible solution to this challenge. From this point of view, this paper proposes a novel industrial robotics cloud platform architecture, and application orchestration and containerized software appliance technology-enabled cloud-edge collaboration mechanism is designed therein. Two exemplary and real-world case studies, supervisory control and data acquisition, and industrial robot predictive maintenance, are further conducted to demonstrate our architecture's generality and versatility.