With the purpose of identifying cyber threats and possible incidents, intrusion detection systems (IDSs) are widely deployed in various computer networks. In order to enhance the detection capability of a single IDS, collaborative intrusion detection networks (or collaborative IDSs) have been developed, which allow IDS nodes to exchange data with each other. However, data and trust management still remain two challenges for current detection architectures, which may degrade the effectiveness of such detection systems. In recent years, blockchain technology has shown its adaptability in many fields such as supply chain management, international payment, interbanking and so on. As blockchain can protect the integrity of data storage and ensure process transparency, it has a potential to be applied to intrusion detection domain. Motivated by this, this work provides a review regarding the intersection of IDSs and blockchains. In particular, we introduce the background of intrusion detection and blockchain, discuss the applicability of blockchain to intrusion detection, and identify open challenges in this direction.

Designing reliable user authentication on mobile phones is becoming an increasingly important task to protect users' private information and data. Since biometric approaches can provide many advantages over the traditional authentication methods, they have become a significant topic for both academia and industry. The major goal of biometric user authentication is to authenticate legitimate users and identify impostors based on physiological and behavioral characteristics. In this paper, we survey the development of existing biometric authentication techniques on mobile phones, particularly on touch-enabled devices, with reference to 11 biometric approaches (five physiological and six behavioral). We present a taxonomy of existing efforts regarding biometric authentication on mobile phones and analyze their feasibility of deployment on touch-enabled mobile phones. In addition, we systematically characterize a generic biometric authentication system with eight potential attack points and survey practical attacks and potential countermeasures on mobile phones. Moreover, we propose a framework for establishing a reliable authentication mechanism through implementing a multimodal biometric user authentication in an appropriate way. Experimental results are presented to validate this framework using touch dynamics, and the results show that multimodal biometrics can be deployed on touch-enabled phones to significantly reduce the false rates of a single biometric system. Finally, we identify challenges and open problems in this area and suggest that touch dynamics will become a mainstream aspect in designing future user authentication on mobile phones.

Internet of Things (IoT) has gradually become one of the most important platforms across different disciplines, by enabling dedicated physical objects to communicate with other Internet-enabled things. With this trend, more devices in medical environments are capable of connecting with each other, named Internet of Medical Things (IoMT). It aims for improving efficiency and reducing communication delay, e.g., monitoring the status of patients and notifying abnormal events. However, due to the distributed nature, insider attacks are still one of the major threats to such IoT environment. How to improve the trust management in IoMT remains a challenge. Motivated by the popularity of blockchain technology, in this paper, our general goal is to investigate the performance of blockchain-based trust management. In particular, we focus on a particular type of IoMT, named medical smartphone networks (MSNs), because of the wide adoption of smartphones in the medical domain. Then, we apply blockchains for enhancing the effectiveness of Bayesian inference-based trust management to detect malicious nodes in MSNs. In the evaluation, we explore the performance of our approach in two different healthcare environments, and experimental results demonstrate that blockchain technology can help improve the detection efficiency of detecting malicious nodes with reasonable workload.