Machine Learning for Synthetic Data Generation: A Review

Data plays a crucial role in machine learning. However, in real-world applications, there are several problems with data, e.g., data are of low quality; a limited number of data points lead to under-fitting of the machine learning model; it is hard to access the data due to privacy, safety and regulatory concerns. Synthetic data generation offers a promising new avenue, as it can be shared and used in ways that real-world data cannot. This paper systematically reviews the existing works that leverage machine learning models for synthetic data generation. Specifically, we discuss the synthetic data generation works from several perspectives: (i) applications, including computer vision, speech, natural language, healthcare, and business; (ii) machine learning methods, particularly neural network architectures and deep generative models; (iii) privacy and fairness issue. In addition, we identify the challenges and opportunities in this emerging field and suggest future research directions

Few-shot Multi-domain Knowledge Rearming for Context-aware Defence against Advanced Persistent Threats

Advanced persistent threats (APTs) have novel features such as multi-stage penetration, highly-tailored intention, and evasive tactics. APTs defense requires fusing multi-dimensional Cyber threat intelligence data to identify attack intentions and conducts efficient knowledge discovery strategies by data-driven machine learning to recognize entity relationships. However, data-driven machine learning lacks generalization ability on fresh or unknown samples, reducing the accuracy and practicality of the defense model. Besides, the private deployment of these APT defense models on heterogeneous environments and various network devices requires significant investment in context awareness (such as known attack entities, continuous network states, and current security strategies). In this paper, we propose a few-shot multi-domain knowledge rearming (FMKR) scheme for context-aware defense against APTs. By completing multiple small tasks that are generated from different network domains with meta-learning, the FMKR firstly trains a model with good discrimination and generalization ability for fresh and unknown APT attacks. In each FMKR task, both threat intelligence and local entities are fused into the support/query sets in meta-learning to identify possible attack stages. Secondly, to rearm current security strategies, an finetuning-based deployment mechanism is proposed to transfer learned knowledge into the student model, while minimizing the defense cost. Compared to multiple model replacement strategies, the FMKR provides a faster response to attack behaviors while consuming less scheduling cost. Based on the feedback from multiple real users of the Industrial Internet of Things (IIoT) over 2 months, we demonstrate that the proposed scheme can improve the defense satisfaction rate.Comment: It has been accepted by IEEE SmartNet

A systems biology approach identifies a regulator, BplERF1, of cold tolerance in Betula platyphylla

Cold is an abiotic stress that can greatly affect the growth and survival of plants. Here, we reported that an AP2/ERF family gene, BplERF1, isolated from Betula platyphylla played a contributing role in cold stress tolerance. Overexpression of BplERF1 in B. platyphylla transgenic lines enhanced cold stress tolerance by increasing the scavenging capability and reducing H2O2 and malondialdehyde (MDA) content in transgenic plants. Construction of BplERF-mediated multilayered hierarchical gene regulatory network (ML-hGRN), using Top-down GGM algorithm and the transcriptomic data of BplERF1 overexpression lines, led to the identification of five candidate target genes of BplERF1 which include MPK20, ERF9, WRKY53, WRKY70, and GIA1. All of them were then verified to be the true target genes of BplERF1 by chromatin-immunoprecipitation PCR (ChIP-PCR) assay. Our results indicate that BplERF1 is a positive regulator of cold tolerance and is capable of exerting regulation on the expression of cold signaling and regulatory genes, causing mitigation of reactive oxygen species