Diagnosing bot infections using Bayesian inference

Prior research in botnet detection has used the bot lifecycle to build detection systems. These systems, however, use rule-based decision engines which lack automated adaptability and learning, accuracy tunability, the ability to cope with gaps in training data, and the ability to incorporate local security policies. To counter these limitations, we propose to replace the rigid decision engines in contemporary bot detectors with a more formal Bayesian inference engine. Bottleneck, our prototype implementation, builds confidence in bot infections based on the causal bot lifecycle encoded in a Bayesian network. We evaluate Bottleneck by applying it as a post-processing decision engine on lifecycle events generated by two existing bot detectors (BotHunter and BotFlex) on two independently-collected datasets. Our experimental results show that Bottleneck consistently achieves comparable or better accuracy than the existing rule-based detectors when the test data is similar to the training data. For differing training and test data, Bottleneck, due to its automated learning and inference models, easily surpasses the accuracies of rule-based systems. Moreover, Bottleneck’s stochastic nature allows its accuracy to be tuned with respect to organizational needs. Extending Bottleneck’s Bayesian network into an influence diagram allows for local security policies to be defined within our framework. Lastly, we show that Bottleneck can also be extended to incorporate evidence trustscore for false alarm reduction.     

Role of catalytic protein and stabilising agents in the transformation of Ag ions to nanoparticles by Pseudomonas aeruginosa

Biological routes of synthesising metal nanoparticles (NPs) using microbes have been gaining much attention due to their low toxicity and eco‐friendly nature. Pseudomonas aeruginosa JP2 isolated from metal contaminated soil was evaluated towards extracellular synthesis of silver NPs (AgNPs). Cell‐free extract (24 h) of the bacterial isolate was reacted with AgNO₃ for 24 h in order to fabricate AgNPs. Preliminary observations were recorded in terms of colour change of the reaction mixture from yellow to greyish black. UV‐visible spectroscopy of the reaction mixture has shown a progressive increase in optical densities that correspond to peaks near 430 nm, depicting reduction of ionic silver (Ag⁺) to atomic silver (Ag⁰) thereby synthesising NPs. X‐ray diffraction spectra exhibited the 2θ values to be 38.4577° confirming the crystalline and spherical nature of NPs [9.6 − 26.7 (Ave. = 17.2 nm)]. Transmission electron microscopy finally confirmed the size of the particles varying from 5 to 60 nm. Moreover, rhamnolipids and proteins were identified as stabilising molecules for the AgNPs through Fourier transform‐infrared spectroscopy. Characterisation of bacterial crude and purified protein fractions confirmed the involvement of nitrate reductase (molecular weight 66 kDa and specific activity = 3.8 U/mg) in the Synthesis of AgNPs.Inspec keywords: microorganisms, silver, nanoparticles, enzymes, molecular biophysics, ultraviolet spectra, visible spectra, X‐ray diffraction, transmission electron microscopy, Fourier transform infrared spectra, catalysis, biochemistry, nanobiotechnologyOther keywords: catalytic protein, stabilising agents, Pseudomonas aeruginosa, metal nanoparticles, UV–visible spectroscopy, optical densities, ionic silver, atomic silver, X‐ray diffraction spectra, transmission electron microscopy, nitrate reductase, rhamnolipids, Fourier transform‐infrared spectroscopy, Ag     

Elemental and spectroscopic characterization of humic-acid-like compounds during composting of olive mill by-products

Humic acids (HAs) were isolated at different stages of composting from two piles of solid olive mill residues (SOR) treated for the first 30 days with tap water (pile C1) or olive mill wastewater (pile C2), for a total composting period of 9 months. The HA fractions were characterized by elemental analysis, UV-visible, Fourier transform infrared and fluorescence spectroscopy in order to monitor humification process and the maturity of the composts. As composting proceeded, the elemental composition of the humic acids showed a decrease in C and H content, and in the C/N ratio, and an increase in N and O contents and in the C/H and O/C ratios. These changes could be attributed to a loss of aliphatic groups and to an increase of aromatic character, polycondensation and degree of oxidation of the HAs. Spectroscopic data agree and support these results, suggesting that the chemical and structural features of the HAs of both composts tend to reach those typical of native soil HAs, that is compounds with a high degree of humification and a high molecular weight and complexity. Therefore, both composting processes seem suitable to produce well-humified organic matter, with important benefits for their use in soil amendment. No differences appeared between the two treatments concerning the humic character of the two final composts.     

Monitoring Assisted Robust Routing in Wireless Mesh Networks

In this paper, we present a monitoring assisted robust routing scheme for wireless mesh networks which exploits the broadcast nature of wireless transmissions at special routers with added monitoring functionalities. These routers passively listen to the transmissions in their neighborhood and compare the routing behavior against the routing state collectively maintained by them. If any inconsistency is found, as a result of software/hardware malfunction, these routers try to determine the node causing it and recover from it in a timely fashion. The scheme is developed for wireless mesh networks where the communication overhead is a critical issue. The performance evaluation of our scheme shows considerable improvement in reliability (i.e., delivery ratio achieved by standard routing protocols) with minimal overhead under situations of malfunctions.     

Preliminary study on enzymatic hydrolysis of treated oil palm (Elaeis) empty fruit bunches fibre (EFB) by using combination of cellulase and �� 1-4 glucosidase

Preliminary study on enzymatic hydrolysis process using combination of cellulase and ?? 1-4 glucosidase on treated oil palm empty fruit bunch fibre (EFB) was performed. Crucial trends for parameters such as pH, temperature and substrate loading influencing the enzymatic hydrolysis of the treated EFB fibre were also studied. Results revealed that a combination of both cellulase and ?? 1-4 glucosidase with the ratio of 5:1 hydrolyzed more cellulose from treated EFB fibre and gave highest soluble glucose concentration up to 4 g L⁻¹. The results indicated that as pH and temperature were increased the glucose produced also increased until pH 4.8 and 50 °C; beyond these values the reverse occurred. Glucose produced in the reaction increased with the increment in the substrate loading and maximum glucose concentration (2.7 g L⁻¹) was achieved when 8% (wv⁻¹) treated EFB was used as a substrate.     

Front Cover: Development and Characterization of Selective FAK Inhibitors and PROTACs with In Vivo Activity (ChemBioChem 19/2023)

Focal adhesion kinase (FAK) is an attractive drug target due to its overexpression in cancer. FAK functions as a non-receptor tyrosine kinase and scaffolding protein, coordinating several downstream signaling effectors and cellular processes. While drug discovery efforts have largely focused on targeting FAK kinase activity, FAK inhibitors have failed to show efficacy as single agents in clinical trials. Here, using structure-guided design, we report the development of a selective FAK inhibitor (BSJ-04-175) and degrader (BSJ-04-146) to evaluate the consequences and advantages of abolishing all FAK activity in cancer models. BSJ-04-146 achieves rapid and potent FAK degradation with high proteome-wide specificity in cancer cells and induces durable degradation in mice. Compared to kinase inhibition, targeted degradation of FAK exhibits pronounced improved activity on downstream signaling and cancer cell viability and migration. Together, BSJ-04-175 and BSJ-04-146 are valuable chemical tools to dissect the specific consequences of targeting FAK through small-molecule inhibition or degradation.     

Integration of Internet of Things and blockchain technology in healthcare domain: A systematic literature review

Healthcare is a vitally important field in the industry and evolving day by day in the aspect of technology, services, computing, and management. Its potential significance can be increased by incorporating Internet of Things (IoT) technology to make it smart in the aspect of automating activities, which is then further reformed in the healthcare domain with the help of blockchain technology. Blockchain technology provides many features to IoT-based healthcare domain applications such as restructuring by securing traditional practices, data management, data sharing, patient remote monitoring, and drug analysis. In this study, a systematic literature review has been carried out in which a total of 52 studies were selected to conduct systematic literature review from databases PubMed, IEEE Access, and Scopus; the study includes IoT technology and blockchain integration in healthcare domain-related application areas. This study also includes taxonomy that mentions the aspects and areas in healthcare domain incorporating the traditional system with the integration of IoT and blockchain to provide transparency, security, privacy, and immutability. This study also includes the incorporation of related sensors, platforms of blockchain, the objective focus of selected studies, and future directions by incorporating IoT and blockchain in healthcare domain. This study will help researchers who want to work with IoT and blockchain technology integration in healthcare domain.     

Academic favoritism at work: insider bias in Turkish national journals

Scientometrics - The study utilizes a unique dataset of 16,575 research papers published in 68 national Business and Economics journals to investigate editorial bias towards insiders in Turkish...     

Automatic Detection of COVID-19 Using Chest X-Ray Images and Modified ResNet18-Based Convolution Neural Networks

The latest studies with radiological imaging techniques indicate that X-ray images provide valuable details on the Coronavirus disease 2019 (COVID-19). The usage of sophisticated artificial intelligence technology (AI) and the radiological images can help in diagnosing the disease reliably and addressing the problem of the shortage of trained doctors in remote villages. In this research, the automated diagnosis of Coronavirus disease was performed using a dataset of X-ray images of patients with severe bacterial pneumonia, reported COVID-19 disease, and normal cases. The goal of the study is to analyze the achievements for medical image recognition of state-of-the-art neural networking architectures. Transfer Learning technique has been implemented in this work. Transfer learning is an ambitious task, but it results in impressive outcomes for identifying distinct patterns in tiny datasets of medical images. The findings indicate that deep learning with X-ray imagery could retrieve important biomarkers relevant for COVID-19 disease detection. Since all diagnostic measures show failure levels that pose questions, the scientific profession should determine the probability of integration of X-rays with the clinical treatment, utilizing the results. The proposed model achieved 96.73% accuracy outperforming the ResNet50 and traditional Resnet18 models. Based on our findings, the proposed system can help the specialist doctors in making verdicts for COVID-19 detection.     

Modeling thermal conductivity enhancement of metal and metallic oxide nanofluids using support vector regression

Enhancing thermal conductivity of nanofluids is an important objective in heat transfer applications. Experimental measurement of thermal conductivity is time consuming, laborious and expensive. One of the common ways to address these limitations involves developing theoretical models to study thermo-physical properties of nanofluid. However, most classical and empirical models fail in predicting experimental results with good precision. In this study, we developed support vector regression (SVR) models that are capable of predicting the thermal conductivity enhancement for metallic and metallic-oxide nanofluids. The accuracy and reliability of the developed models were assessed using statistical parameters such as correlation coefficient (R²), root mean square error (RMSE) and mean absolute error (MAE). The models were characterized with very high correlation coefficients of 99.3 and 96.3% for the metallic and metallic oxide nanofluids, respectively. While the RMSE obtained were 1.11 and 1.33 for the metallic and metallic oxide nanofluids, respectively. In addition, the results of the models were compared with Hamilton-Crosser (HC) model and other empirical models. The SVR models performed much better than all the models examined. Furthermore, the effects of temperature, volume fractions, nanoparticle size and type, and basefluids types were correlated with experimental data in order to assess the performance of the developed models. The results indicate that SVR predictions were accurate and better than common theoretical models.