In the present work we propose a bienzymatic cascade for the oxyfunctionalisation of cycloalkanes to cyclic alcohols/cyclic ketones. By combining a H₂O₂-dependent peroxygenase with a O₂-consuming and H₂O₂-producing alcohol oxidase an overall aerobic oxidation system was established. A convincing proof-of-concept is presented and some current limitations are outlined.

Password hardening encryption (PHE) is an emerging primitive in recent years. It can resist offline attack brought by keyword guessing attack from server via adding a third party with crypto services joining the decryption process. This primitive enhances the password authentication protocol and adds encryption functionality. This paper presents an active attack from server in the first scheme that introduced this primitive. This attack combines the idea from a cutting-edge threat called algorithm substitution attack which is undetectable and makes the server capable of launching offline attack. This result shows that the original PHE scheme can not resist attacks from malicious server. Then this study tries to summarize the property that an algorithm substitution attack resistant scheme should have. After that this paper presents a PHE scheme that can resist such kind of attacks from malicious server with simulation results. Finally, this study concludes the result and gives some expectation for future systematic research on interactive protocols under algorithm substitution attack.

An algorithm substitution attack (ASA) can undermine the security of cryptographic primitives by subverting the original implementation. An ASA succeeds when it extracts secrets without being detected. To launch an ASA on signature schemes, existing studies often needed to collect signatures with successive indices to extract the signing key. However, collection with successive indices requires uninterrupted surveillance of the communication channel and a low transmission loss rate in practice. This hinders the practical implementation of current ASAs, thus causing users to misbelieve that the threat incurred by ASA is only theoretical and far from reality. In this study, we first classify a group of schemes called extractable signatures that achieve traditional security (unforgeability) by reductions ending with key extraction, thus demonstrating that there is a generic and practical approach for ASA with this class of signatures. Further, we present the implementation of ASAs in which only two signatures and no further requirements are needed for the extraction of widely used discrete log-based signatures such as DSA, Schnorr, and modified ElGamal signature schemes. Our attack presents a realistic threat to current signature applications, which can also be implemented in open and unstable environments such as vehicular ad hoc networks. Finally, we prove that the proposed ASA is undetectable against polynomial time detectors and physical timing analysis.

In recent years, the scale and diversity of malicious software on mobile networks have grown significantly, thereby causing considerable danger to users’ property and personal privacy. In this study, we propose a malware detection method that uses the URLs visited by apps to identify malware. A multi-view neural network is used to create a malware detection model that emphasizes depth and width. This neural network can create multiple views of inputs automatically and distribute soft attention weights to focus on different features of inputs. Multiple views preserve rich semantic information from inputs for classification without requiring complicated feature engineering. In addition, we conduct comprehensive experiments to compare the proposed method with others and verify the validity of the detection model. The experimental results show that our method achieves robust and timely malware detection. It can not only effectively detect malware discovered in different months of a certain year, but also detect potentially malicious apps in the third-party app market. We also compare the detection results of the proposed method on wild apps with 10 popular anti-virus scanners, and the final result shows that our approach ranks second in terms of detection performance.