SGANFuzz: A Deep Learning-Based MQTT Fuzzing Method Using Generative Adversarial Networks

As the Internet of Things (IoT) industry grows, the risk of network protocol security threats has also increased. One protocol that has come under scrutiny for its security vulnerabilities is MQTT (Message Queuing Telemetry Transport), which is widely used. To address this issue, an automated execution program called fuzz has been developed to verify the security of MQTT brokers. This program is provided with various random and unexpected input data and monitored for different responses, such as acknowledgments, crashes, failures, or memory leaks. To generate a significant number of realistic MQTT protocols, we have proposed a Generative Adversarial Networks (GAN)-based protocol fuzzer called SGANFuzz. Our experimental results show that SGANFuzz has successfully detected 6 vulnerabilities among 7 MQTT implementations, including 3 CVE bugs. Compared to the state-of-the-art fuzzing tools, SGANFuzz has proven to be the most efficient fuzzing tool in terms of vulnerability detection and has expanded the feedback coverage by receiving more unique network responses from MQTT brokers

Effects of 1,25-dihydroxyvitamin D3 on experimental periodontitis and AhR/NF-κB/NLRP3 inflammasome pathway in a mouse model

Vitamin D has been known to have important regulatory functions in inflammation and immune response and shows inhibitory effects on experimental periodontitis in animal models. However, the potential mechanism has yet to be clarified. Recent studies have highlighted Aryl hydrocarbon receptor (AhR) and its downstream signaling as a crucial regulator of immune homeostasis and inflammatory regulation. Objective: This study aimed to clarify the effect of 1,25-dihydroxyvitamin D3 (VD3) on experimental periodontitis and AhR/nuclear factor-κB (NF-κB)/NLR pyrin domain-containing 3 (NLRP3) inflammasome pathway in the gingival epithelium in a murine model. Methodology: We induced periodontitis in male C57BL/6 wild-type mice by oral inoculation of Porphyromonas gingivalis (P. gingivalis), and subsequently gave intraperitoneal VD3 injection to the mice every other day for 8 weeks. Afterwards, we examined the alveolar bone using scanning electron microscopy (SEM) and detected the gingival epithelial protein using western blot analysis and immunohistochemical staining. Results: SEM images demonstrated that alveolar bone loss was reduced in the periodontitis mouse model after VD3 supplementation. Western blot analyses and immunohistochemical staining of the gingival epithelium showed that the expression of vitamin D receptor, AhR and its downstream cytochrome P450 1A1 were enhanced upon VD3 application. Additionally, VD3 decreased NF-κB p65 phosphorylation, and NLRP3, apoptosis-associated speck-like protein, caspase-1, interleukin-1β (IL-1β) and IL-6 protein expression. Conclusions: These results implicate the alleviation of periodontitis and the alteration of AhR/NF-κB/NLRP3 inflammasome pathway by VD3 in the mouse model. The attenuation of this periodontal disease may correlate with the regulation of AhR/NF-κB/NLRP3 inflammasome pathway by VD3

Subdomain Adaptation with Manifolds Discrepancy Alignment

Reducing domain divergence is a key step in transfer learning problems. Existing works focus on the minimization of global domain divergence. However, two domains may consist of several shared subdomains, and differ from each other in each subdomain. In this paper, we take the local divergence of subdomains into account in transfer. Specifically, we propose to use low-dimensional manifold to represent subdomain, and align the local data distribution discrepancy in each manifold across domains. A Manifold Maximum Mean Discrepancy (M3D) is developed to measure the local distribution discrepancy in each manifold. We then propose a general framework, called Transfer with Manifolds Discrepancy Alignment (TMDA), to couple the discovery of data manifolds with the minimization of M3D. We instantiate TMDA in the subspace learning case considering both the linear and nonlinear mappings. We also instantiate TMDA in the deep learning framework. Extensive experimental studies demonstrate that TMDA is a promising method for various transfer learning tasks

Loop-Mediated Isothermal Amplification for Detection of Staphylococcus aureus

To develop a rapid detection method of Staphylococcus aureus using loop-mediated isothermal amplification (LAMP), four specific primers were designed according to six distinct sequences of the nuc gene. In addition, the specificity and sensitivity of LAMP were verified and compared with those of PCR. Results showed that the LAMP reaction was completed within 45 min at 62.5°C, and ladder bands were appeared in LAMP products analyzed by gel electrophoresis. After adding 1x SYBR Green l, the positive reaction tube showed green color and the negative reaction tube remained orange, indicating that the LAMP has high specificity. The minimal detectable concentration of LAMP was 1×102 CFU/mL and that of PCR was 1×104 CFU/mL, indicating that the LAMP was 100 times more sensitive than the PCR. The LAMP method for detection of Staphylococcus aureus has many advantages, such as simple operation, high sensitivity, high specificity, and rapid analysis. Therefore, this method is more suitable for the rapid on-site detection of Staphylococcus aureus