Internet of Things (IoT) has facilitated the prosperity of smart environments such as smart homes. Meanwhile, WiFi is a broadly-used technology for the wireless connectivity of IoT devices. However, smart home IoT devices are often vulnerable to various security attacks. This paper quantifies the impact of Distributed Denial of Service (DDoS) and energyoriented DDoS attacks (E-DDoS) on WiFi smart home devices and explores the underlying reasons from the perspective of attacker, victim device, and access point (AP). Compared to the existing work, which primarily focus on DDoS attacks launched by compromised IoT devices against servers, our work focuses on the connectivity and energy consumption of IoT devices when under attack. Our key findings are three-fold. First, the minimum DDoS attack rate causing service disruptions varies significantly among different IoT smart home devices, and buffer overflow within the victim device is validated as critical. Second, the group key updating process of WiFi, may facilitate DDoS attacks by causing faster victim disconnections. Third, a higher E-DDoS attack rate sent by the attacker may not necessarily lead to a victim’s higher energy consumption. Our study reveals the communication protocols, attack rates, payload sizes, and victim devices’ ports state as the vital factors to determine the energy consumption of victim devices. These findings facilitate a thorough understanding of IoT devices’ potential vulnerabilities within a smart home environment and pave solid foundations for future studies on defense solutions.

In this paper, we propose a novel General-NMF (General Non-negative Matrix Factorization) based digital watermarking scheme for copyright protection and integrity authentication of digital media content. Specifically, the proposed General-NMF algorithm is able to factorize a matrix C ${\in}R{^{s \times t}_{+}}$ decomposed into a basis matrix A ${\in}R{^{m \times n}_{+}}$ and a coefficient matrix B ${\in}R{^{p \times q}_{+}}$ by removing the dimension matching constraints required by the traditional NMF, where $s=m, n=p$ and $t=q$. In particular, $s=m \cdot l/n, t=l/p \cdot q$, and the variable l is the least common multiple of n and p. Furthermore, the generator factor of the random matrix and n are used as the keys of the proposed digital watermarking scheme. Experiment results show that the proposed digital watermarking scheme can effectively resist various attacks and tampering.

Digital signature is a major component of transactions on Blockchain platforms, especially in enterprise Blockchain platforms, where multiple signatures from a set of peers need to be produced to endorse a transaction. However, such process is often complex and time-consuming. Multi-signature, which can improve transaction efficiency by having a set of signers cooperate to produce a joint signature, has attracted extensive attentions. In this work, we propose two multi-signature schemes, GMS and AGMS, which are proved to be more secure and efficient than state-of-the-art multi-signature schemes. Besides, we implement the proposed schemes in a real Enterprise Blockchain platform, Fabric. Experiment results show that the proposed AGMS scheme helps achieve the goal of high transaction efficiency, low storage complexity, as well as high robustness against rogue-key attacks and $k$ -sum problem attacks.

Objective. The aim of this study was to identify the candidate genes in type 2 diabetes mellitus (T2DM) and explore their potential mechanisms. Methods. The gene expression profile GSE26168 was downloaded from the Gene Expression Omnibus (GEO) database. The online tool GEO2R was used to obtain differentially expressed genes (DEGs). Gene Ontology (GO) term enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed by using Metascape for annotation, visualization, and comprehensive discovery. The protein-protein interaction (PPI) network of DEGs was constructed by using Cytoscape software to find the candidate genes and key pathways. Results. A total of 981 DEGs were found in T2DM, including 301 upregulated genes and 680 downregulated genes. GO analyses from Metascape revealed that DEGs were significantly enriched in cell differentiation, cell adhesion, intracellular signal transduction, and regulation of protein kinase activity. KEGG pathway analysis revealed that DEGs were mainly enriched in the cAMP signaling pathway, Rap1 signaling pathway, regulation of lipolysis in adipocytes, PI3K-Akt signaling pathway, MAPK signaling pathway, and so on. On the basis of the PPI network of the DEGs, the following 6 candidate genes were identified: PIK3R1, RAC1, GNG3, GNAI1, CDC42, and ITGB1. Conclusion. Our data provide a comprehensive bioinformatics analysis of genes, functions, and pathways, which may be related to the pathogenesis of T2DM.