Biblio
Managing identity across an ever-growing digital services landscape has become one of the most challenging tasks for security experts. Over the years, several Identity Management (IDM) systems were introduced and adopted to tackle with the growing demand of an identity. In this series, a recently emerging IDM system is Self-Sovereign Identity (SSI) which offers greater control and access to users regarding their identity. This distinctive feature of the SSI IDM system represents a major development towards the availability of sovereign identity to users. uPort is an emerging open-source identity management system providing sovereign identity to users, organisations, and other entities. As an emerging identity management system, it requires meticulous analysis of its architecture, working, operational services, efficiency, advantages and limitations. Therefore, this paper contributes towards achieving all of these objectives. Firstly, it presents the architecture and working of the uPort identity management system. Secondly, it develops a Decentralized Application (DApp) to demonstrate and evaluate its operational services and efficiency. Finally, based on the developed DApp and experimental analysis, it presents the advantages and limitations of the uPort identity management system.
In today's world privacy is paramount in everyone's life. Alongside the growth of IoT (Internet of things), wearable devices are becoming widely popular for real-time user monitoring and wise service support. However, in contrast with the traditional short-range communications, these resource-scanty devices face various vulnerabilities and security threats during the course of interactions. Hence, designing a security solution for these devices while dealing with the limited communication and computation capabilities is a challenging task. In this work, PUF (Physical Unclonable Function) and lightweight cryptographic parameters are used together for performing two-way authentication between wearable devices and smartphone, while the simultaneous verification is performed by providing yoking-proofs to the Cloud Server. At the end, it is shown that the proposed scheme satisfies many security aspects and is flexible as well as lightweight.
The developments made in IoT applications have made wearable devices a popular choice for collecting user data to monitor this information and provide intelligent service support. Since wearable devices are continuously collecting and transporting a user's sensitive data over the network, there exist increased security challenges. Moreover, wearable devices lack the computation capabilities in comparison to traditional short-range communication devices. In this paper, authors propounded a Yoking Proof based remote Authentication scheme for Cloud-aided Wearable devices (YPACW) which takes PUF and cryptographic functions and joins them to achieve mutual authentication between the wearable devices and smartphone via a cloud server, by performing the simultaneous verification of these devices, using the established yoking-proofs. Relative to Liu et al.'s scheme, YPACW provides better results with the reduction of communication and processing cost significantly.
Internet of Things (IoT) is a revolutionary expandable network which has brought many advantages, improving the Quality of Life (QoL) of individuals. However, IoT carries dangers, due to the fact that hackers have the ability to find security gaps in users' IoT devices, which are not still secure enough and hence, intrude into them for malicious activities. As a result, they can control many connected devices in an IoT network, turning IoT into Botnet of Things (BoT). In a botnet, hackers can launch several types of attacks, such as the well known attacks of Distributed Denial of Service (DDoS) and Man in the Middle (MitM), and/or spread various types of malicious software (malware) to the compromised devices of the IoT network. In this paper, we propose a novel hybrid Artificial Intelligence (AI)-powered honeynet for enhanced IoT botnet detection rate with the use of Cloud Computing (CC). This upcoming security mechanism makes use of Machine Learning (ML) techniques like the Logistic Regression (LR) in order to predict potential botnet existence. It can also be adopted by other conventional security architectures in order to intercept hackers the creation of large botnets for malicious actions.
Recently, smart video security systems have been active. The existing video security system is mainly a method of detecting a local abnormality of a unit camera. In this case, it is difficult to obtain the characteristics of each local region and the situation for the entire watching area. In this paper, we developed an object map for the entire surveillance area using a combination of surveillance cameras, and developed an algorithm to detect anomalies by learning normal situations. The surveillance camera in each area detects and tracks people and cars, and creates a local object map and transmits it to the server. The surveillance server combines each local maps to generate a global map for entire areas. Probability maps were automatically calculated from the global maps, and normal and abnormal decisions were performed through trained data about normal situations. For three reporting status: normal, caution, and warning, and the caution report performance shows that normal detection 99.99% and abnormal detection 86.6%.