Host mobility key management in dynamic secure group communication

The key management has a fundamental role in securing group communications taking place over vast and unprotected networks. It is concerned with the distribution and update of the keying materials whenever any changes occur in the group membership. Wireless mobile environments enable members to move freely within the networks, which causes more difficulty to design efficient and scalable key management protocols. This is partly because both member location dynamic and group membership dynamic must be managed concurrently, which may lead to significant rekeying overhead. This paper presents a hierarchical group key management scheme taking the mobility of members into consideration intended for wireless mobile environments. The proposed scheme supports the mobility of members across wireless mobile environments while remaining in the group session with minimum rekeying transmission overhead. Furthermore, the proposed scheme alleviates 1-affect-n phenomenon, single point of failure, and signaling load caused by moving members at the core network. Simulation results shows that the scheme surpasses other existing efforts in terms of communication overhead and affected members. The security requirements studies also show the backward and forward secrecy is preserved in the proposed scheme even though the members move between areas

A Novel Machine-Learning Framework Based on a Hierarchy of Dispute Models for the Identification of Fish Species Using Multi-Mode Spectroscopy

Seafood mislabeling rates of approximately 20% have been reported globally. Traditional methods for fish species identification, such as DNA analysis and polymerase chain reaction (PCR), are expensive and time-consuming, and require skilled technicians and specialized equipment. The combination of spectroscopy and machine learning presents a promising approach to overcome these challenges. In our study, we took a comprehensive approach by considering a total of 43 different fish species and employing three modes of spectroscopy: fluorescence (Fluor), and reflectance in the visible near-infrared (VNIR) and short-wave near-infrared (SWIR). To achieve higher accuracies, we developed a novel machine-learning framework, where groups of similar fish types were identified and specialized classifiers were trained for each group. The incorporation of global (single artificial intelligence for all species) and dispute classification models created a hierarchical decision process, yielding higher performances. For Fluor, VNIR, and SWIR, accuracies increased from 80%, 75%, and 49% to 83%, 81%, and 58%, respectively. Furthermore, certain species witnessed remarkable performance enhancements of up to 40% in single-mode identification. The fusion of all three spectroscopic modes further boosted the performance of the best single mode, averaged over all species, by 9%. Fish species mislabeling not only poses health-related risks due to contaminants, toxins, and allergens that could be life-threatening, but also gives rise to economic and environmental hazards and loss of nutritional benefits. Our proposed method can detect fish fraud as a real-time alternative to DNA barcoding and other standard methods. The hierarchical system of dispute models proposed in this work is a novel machine-learning tool not limited to this application, and can improve accuracy in any classification problem which contains a large number of classes

Fault-tolerant and Scalable Key Management Protocol for IoT-based Collaborative Groups

International audienceSecuring collaborative applications relies heavily on the underlying group key management protocols. Designing these protocols ischallenging, especially in the context of the Internet of Things (IoT). Indeed, the presence of heterogeneous and dynamic members within the collaborative groups usually involves resource constrained entities, which require energy-aware protocols to manage frequent arrivals and departures of members. Moreover, both fault tolerance and scalability are sought for sensitive and large collaborative groups. To address these challenges, we propose to enhance our previously proposed protocol (i.e. DBGK) with polynomial computations. In fact, our contribution in this paper, allows additional controllers to be included with no impact on storage cost regarding constrained members. To assess our protocol called DsBGK, we conducted extensive simulations. Results confirmed that DsBGK achieves a better scalability and fault tolerance compared to DBGK. In addition, energy consumption induced by group key rekeying has been reduced

In Silico Prediction of the Toxicity of Nitroaromatic Compounds: Application of Ensemble Learning QSAR Approach

In this work, a dataset of more than 200 nitroaromatic compounds is used to develop Quantitative Structure–Activity Relationship (QSAR) models for the estimation of in vivo toxicity based on 50% lethal dose to rats (LD50). An initial set of 4885 molecular descriptors was generated and applied to build Support Vector Regression (SVR) models. The best two SVR models, SVR_A and SVR_B, were selected to build an Ensemble Model by means of Multiple Linear Regression (MLR). The obtained Ensemble Model showed improved performance over the base SVR models in the training set (R2 = 0.88), validation set (R2 = 0.95), and true external test set (R2 = 0.92). The models were also internally validated by 5-fold cross-validation and Y-scrambling experiments, showing that the models have high levels of goodness-of-fit, robustness and predictivity. The contribution of descriptors to the toxicity in the models was assessed using the Accumulated Local Effect (ALE) technique. The proposed approach provides an important tool to assess toxicity of nitroaromatic compounds, based on the ensemble QSAR model and the structural relationship to toxicity by analyzed contribution of the involved descriptors

Superparamagnetic iron oxide nanoparticles alter expression of obesity and T2D-associated risk genes in human adipocytes

<p>Adipocytes hypertrophy is the main cause of obesity and its affliction such as type 2 diabetes (T2D). Since superparamagnetic iron oxide nanoparticles (SPIONs) are used for a wide range of biomedical/medical applications, we aimed to study the effect of SPIONs on 22 and 29 risk genes (Based on gene wide association studies) for obesity and T2D in human adipocytes. The mRNA expression of lipid and glucose metabolism genes was changed upon the treatment of human primary adipocytes with SPIONs. mRNA of GULP1, SLC30A8, NEGR1, SEC16B, MTCH2, MAF, MC4R, and TMEM195 were severely induced, whereas INSIG2, NAMPT, MTMR9, PFKP, KCTD15, LPL and GNPDA2 were down-regulated upon SPIONs stimulation. Since SEC16B gene assist the phagocytosis of apoptotic cells and this gene were highly expressed upon SPIONs treatment in adipocytes, it is logic to assume that SPIONs may play a crucial role in this direction, which requires more consideration in the future.</p>

REAL-TIME QUANTIFICATION OF MATRIX METALLOPROTEINASE AND INTEGRIN alpha v beta 3 EXPRESSION DURING BIOMATERIAL-ASSOCIATED INFECTION IN A MURINE MODEL

Biomaterial implants and devices increase the risk of microbial infections due to the biofilm mode of growth of infecting bacteria on implant materials, in which bacteria are protected against antibiotic treatment and the local immune system. Matrix-metalloproteinases (MMPs) and cell surface integrin receptors facilitate transmigration of inflammatory cells toward infected or inflamed tissue. This study investigates the relationship between MMP- and integrin-expression and the clearance of infecting Staphylococcus aureus around implanted biomaterials in a murine model. MMP- and integrin alpha v beta 3 expression were monitored in mice, with and without subcutaneously implanted biomaterial samples, in the absence and presence of bioluminescent S. aureus Xen36. Staphylococcal persistence was imaged longitudinally over time using bioluminescence imaging. The activatable MMPSense (R) 680 and integrin-targeted IntegriSense (R) 750 probes were injected on different days after implantation and their signal intensity and localisation monitored using fluorescence imaging. After sacrifice 7 or 16 days post-implantation, staphylococci from biomaterial samples and surrounding tissues were cultured on agar-plates and presence of host inflammatory cells was histologically evaluated. MMP- and integrin-expression were equally enhanced in presence of staphylococci or biomaterials up to 7 days post-implantation, but their localisation along the biomaterial samples differed. Bacterial clearance from tissue was higher in the absence of biomaterials. It is of clinical relevance that MMP- and integrin-expression were enhanced in presence of both staphylococci and biomaterials, although the immune system in the presence of biomaterials remained hampered in eradicating bacteria during the first 7 days post-implantation