Cryptanalysis of Multiple-Server Password-Authenticated Key

Password-based user-authentication schemes have been widely used when users access a server to avail internet services. Multiserver password-authentication schemes enable remote users to obtain service from multiple servers without separately registering with each server. In 2008, Jia-Lun Tsai proposed an improved and efficient password-authenticated key agreement scheme for a multiserver architecture based on Chang-Lee’s scheme proposed in 2004. However, we found that Tsai’s scheme does not provide forward secrecy and is weak to insider impersonation and denial of service attacks. In this article, we describe the drawbacks of Tsai’s scheme and provide a countermeasure to satisfy the forward secrecy property

Model Pengamanan End-to-End Pada M-Banking Berbasis Algoritma Kurva Hyper Elliptic

. Currently, banking transactions using mobile banking has grown rapidly. The increasing the number of mobile application users becomes one of the main factors. Several approaches have been developed to improve the transaction security. Problems of message security still requires a solution to achieve computing speed and leverage security level. In this paper, we propose a security algorithms used to improve the mobile banking security with hyperelliptic curve algorithm. It will create a safe and an efficient transactions while message will be sent via public internet. Hyperelliptic curve algorithm will run a processes for authentication and encryption. it will produce fast computation and has good security level. This research produced little computing time on m-banking application while it run on Android. Hyperelliptic curve algorithm use a smaller key to achieve a good security level at m-banking application

Biometrics for internet‐of‐things security: A review

The large number of Internet‐of‐Things (IoT) devices that need interaction between smart devices and consumers makes security critical to an IoT environment. Biometrics offers an interesting window of opportunity to improve the usability and security of IoT and can play a significant role in securing a wide range of emerging IoT devices to address security challenges. The purpose of this review is to provide a comprehensive survey on the current biometrics research in IoT security, especially focusing on two important aspects, authentication and encryption. Regarding authentication, contemporary biometric‐based authentication systems for IoT are discussed and classified based on different biometric traits and the number of biometric traits employed in the system. As for encryption, biometric‐cryptographic systems, which integrate biometrics with cryptography and take advantage of both to provide enhanced security for IoT, are thoroughly reviewed and discussed. Moreover, challenges arising from applying biometrics to IoT and potential solutions are identified and analyzed. With an insight into the state‐of‐the‐art research in biometrics for IoT security, this review paper helps advance the study in the field and assists researchers in gaining a good understanding of forward‐looking issues and future research directions

Security and Privacy for Modern Wireless Communication Systems

The aim of this reprint focuses on the latest protocol research, software/hardware development and implementation, and system architecture design in addressing emerging security and privacy issues for modern wireless communication networks. Relevant topics include, but are not limited to, the following: deep-learning-based security and privacy design; covert communications; information-theoretical foundations for advanced security and privacy techniques; lightweight cryptography for power constrained networks; physical layer key generation; prototypes and testbeds for security and privacy solutions; encryption and decryption algorithm for low-latency constrained networks; security protocols for modern wireless communication networks; network intrusion detection; physical layer design with security consideration; anonymity in data transmission; vulnerabilities in security and privacy in modern wireless communication networks; challenges of security and privacy in node–edge–cloud computation; security and privacy design for low-power wide-area IoT networks; security and privacy design for vehicle networks; security and privacy design for underwater communications networks

Symmetry-Adapted Machine Learning for Information Security

Symmetry-adapted machine learning has shown encouraging ability to mitigate the security risks in information and communication technology (ICT) systems. It is a subset of artificial intelligence (AI) that relies on the principles of processing future events by learning past events or historical data. The autonomous nature of symmetry-adapted machine learning supports effective data processing and analysis for security detection in ICT systems without the interference of human authorities. Many industries are developing machine-learning-adapted solutions to support security for smart hardware, distributed computing, and the cloud. In our Special Issue book, we focus on the deployment of symmetry-adapted machine learning for information security in various application areas. This security approach can support effective methods to handle the dynamic nature of security attacks by extraction and analysis of data to identify hidden patterns of data. The main topics of this Issue include malware classification, an intrusion detection system, image watermarking, color image watermarking, battlefield target aggregation behavior recognition model, IP camera, Internet of Things (IoT) security, service function chain, indoor positioning system, and crypto-analysis