Synthesis of coumarins linked with 1,2,3-triazoles under microwave irradiation and evaluation of their antimicrobial and antioxidant activity

A series of coumarin derivatives linked with 1,2,3-triazoles has been synthesized by utilizing the copper catalyzed azide-alkyne cycloaddition reaction and were screened for their antimicrobial and antioxidant properties. Some of the compounds displayed promising antibacterial activities (MIC ranging from 5-150 µg/mL) and moderate antifungal activities as compared to the respective standards. The compounds 4k and 4g displayed good antibacterial activity when compared with the standard, Ciprofloxacin, and 4n exhibited better antifungal activity when compared to other synthesized compounds. The in silico docking studies of the active compounds were carried out against the gyrase enzyme and from those studies, it was acknowledged that 4k possessed significant hydrogen bonding and hydrophobic interactions which could be the plausible reason for its superior activity as compared to the other synthesized compounds. The compounds 4h and 4q showed promising antioxidant activity when compared with the standard, BHT, which could be attributed to the presence of electron donating substituents. © 2020, Sociedad Química de México.Russian Foundation for Basic Research, RFBR: 170300641AThe authors are thankful to the Department of Industrial Chemistry, Kuvempu University for rendering all the facilities to carry out the experiments. Vasiliy Bakulev is thankful to Russian Foundation for Basic Research (Grant # 170300641A)

Integrity verification and behavioral classification of a large dataset applications pertaining smart OS

© 2020 John Wiley & Sons, Ltd Malware analysis and detection over the Android have been the focus of considerable research, during recent years, as customer adoption of Android attracted a corresponding number of malware writers. Antivirus companies commonly rely on signatures and are error-prone. Traditional machine learning techniques are based on static, dynamic, and hybrid analysis; however, for large scale Android malware analysis, these approaches are not feasible. Deep neural architectures are able to analyze large scale static details of the applications, but static analysis techniques can ignore many malicious behaviors of applications. The study contributes to the documentation of various approaches for detection of malware, traditional and state-of-the-art models, developed for analysis that facilitates the provision of basic insights for researchers working in malware analysis, and the study also provides a dynamic approach that employs deep neural network models for detection of malware. Moreover, the study uses Android permissions as a parameter to measure the dynamic behavior of around 16,900 benign and intruded applications. A dataset is created which encompasses a large set of permissions-based dynamic behavior pertaining applications, with an aim to train deep learning models for prediction of behavior. The proposed architecture extracts representations from input sequence data with no human intervention. The state-of-the-art Deep Convolutional Generative Adversarial Network extracted deep features and accomplished a general validation accuracy of 97.08% with an F1-score of 0.973 in correctly classifying input. Furthermore, the concept of blockchain is utilized to preserve the integrity of the dataset and the results of the analysis