Classification algorithms have been widely adopted to detect anomalies for various systems, e.g., IoT and cloud, under the common assumption that the data source is clean, i.e., features and labels are correctly set. However, data collected from the field can be unreliable due to careless annotations or malicious data transformation for incorrect anomaly detection. In this paper, we present a two-layer learning framework for robust anomaly detection (RAD) in the presence of unreliable anomaly labels. The first layer of quality model filters the suspicious data, where the second layer of classification model detects the anomaly types. We specifically focus on two use cases, (i) detecting 10 classes of IoT attacks and (ii) predicting 4 classes of task failures of big data jobs. Our evaluation results show that RAD can robustly improve the accuracy of anomaly detection, to reach up to 98% for IoT device attacks (i.e., +11%) and up to 83% for cloud task failures (i.e., +20%), under a significant percentage of altered anomaly labels. Index Terms—Unreliable Data; Anomaly Detection; Failures; Attacks; Machine Learning

Acquiring large amounts of high-quality real sonar data for object detection of autonomous underwater vehicles (AUVs) is challenging. Synthetic data can be an alternative, but it is hard to generate diverse data using traditional generative models when real data are limited. This study proposes a novel style transfer method, i.e., the multigranular feature alignment cycle-consistent generative adversarial network (CycleGAN), to generate sonar images leveraging remote sensing images, which can alleviate the dependence on real sonar data. Specifically, we add a spatial attention-based feature aggregation module to preserve unique features by attending to instance parts of an image. A pair of cross-domain discriminators are designed to guide generators to produce images that capture sonar styles. We also introduce a novel cycle consistency loss based on the discrete cosine transform of images, which better utilizes features that are evident in the frequency domain. Extensive experimental results show that the generated sonar images have better quality than CycleGAN, with improvements of 15.2% in IS, 56.9% in FID, 42.6% in KID, and 7.6% in learned perceptual image patch similarity, respectively. Moreover, after expanding the real sonar dataset with generated data, the average accuracy of the object detector, e.g., YOLOv6, has increased by more than 48.7%, indicating the effectiveness of the generated sonar data by our method.

This article proposes a fixed-time adaptive fault-tolerant control methodology for a larger class of high-order (powers are positive odd integers) nonlinear systems subject to asymmetric time-varying state constraints and actuator faults. In contrast with the state-of-the-art control methodologies, the distinguishing features of this study lie in that: (a) high-order asymmetric time-varying tan-type barrier Lyapunov function (BLF) is devised such that the state variables can be convergent to the preassigned compact sets all the time provided their initial values remain therein, which not only preserves the constraints satisfaction, but warrants the validity of the adopted neural network approximator; (b) the proposed control design ensures the tracking errors converge to specified residual sets within fixed time and makes the size of the convergence regions of tracking errors adjustable a priori by means of a new BLF-based tuning function and a projection operator; (c) a variable-separable lemma is delicately embedded into the control design to extract the control terms in a “linear-like” fashion which not only overcomes the difficulty that virtual control signals appear in a non-affine manner, but also solves the problem of actuator faults. Comparative simulations results finally validate the effectiveness of the proposed scheme.

This paper introduces a novel cross-modal neural network model that aims to convert long-term temporal behavior data into natural language to achieve UAV intent recognition. Our generative intent recognition model effectively utilizes the inherent redundancy present in long temporal behavior data by incorporating a sequence compression module, which enables the cross-modal generation and alignment of intents while preserving the integrity of the standard Transformer architecture. Importantly, we observe that this approach mitigates the negative impact of imbalanced database distribution by mapping intent categories onto the modality of natural language. Furthermore, we propose three comprehensive pre-training tasks specifically designed for time series data, thoroughly examining their interconnections and analyzing the impact of a hybrid pre-training framework on the accuracy of intent recognition. Our experimental results demonstrate the superiority of our proposed generative UAV intent recognition model, along with the hybrid pre-training initialization method, compared to conventional classification models. Simultaneously, the intent recognition method exhibits heightened temporal sensitivity and robust resilience, enabling it to deal with complex UAV confrontation and interference environment.

Classification algorithms have been widely adopted to detect anomalies for various systems, e.g., IoT, cloud and face recognition, under the common assumption that the data source is clean. However, data collected from the wild can be unreliable due to careless annotations or malicious data transformation for incorrect anomaly detection. In this paper, we extend a two-layer on-line data selection framework: Robust Anomaly Detector (RAD) with a newly designed ensemble prediction where both layers contribute to the final anomaly detection decision. To adapt to the on-line nature of anomaly detection, we consider additional features of conflicting opinions of classifiers, repetitive cleaning, and oracle knowledge. We on-line learn from the incoming data streams and continuously cleanse the data, so as to adapt to the increasing learning capacity from the larger accumulated data set. Moreover, we explore the concept of oracle learning that provides additional information of true labels for difficult data points. The proposed RAD and its extensions are general and can be applied to different anomaly detection algorithms.

While data sharing is crucial for knowledge development, privacy concerns and strict regulation (e.g., European General Data Protection Regulation (GDPR)) unfortunately limit its full effectiveness. Synthetic tabular data emerges as an alternative to enable data sharing while fulfilling regulatory and privacy constraints. The state-of-the-art tabular data synthesizers draw methodologies from generative Adversarial Networks (GAN) and address two main data types in the industry, i.e., continuous and categorical. In this paper, we develop CTAB-GAN, a novel conditional table GAN architecture that can effectively model diverse data types, including a mix of continuous and categorical variables. Moreover, we address data imbalance and long-tail issues, i.e., certain variables have drastic frequency differences across large values. To achieve those aims, we first introduce the information loss and classification loss to the conditional GAN. Secondly, we design a novel conditional vector, which efficiently encodes the mixed data type and skewed distribution of data variable. We extensively evaluate CTAB-GAN with the state of the art GANs that generate synthetic tables, in terms of data similarity and analysis utility. The results on five datasets show that the synthetic data of CTAB-GAN remarkably resembles the real data for all three types of variables and results into higher accuracy for five machine learning algorithms, by up to 17%.