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Distributed ledger technologies represent decentralized databases, without the requirement for a central authority to authenticate and validate transactions. Blockchain technology is one of these technologies that provides a record of digital transactions that have been executed and validated by consensus of a majority of participants in the system. It uses software that processes on the order of the newly created and connected blocks via cryptographic means to ensure security and integrity. It has a verified record of each transaction that has been executed till date, which cannot be removed from the system, thereby ensuring the integrity of information. Although Bitcoin is the most popular application of blockchain, it has been used in diverse application domains such as healthcare, logistics, and security. While blockchain enables fairness and prevention from denial-of-service attacks, it lacks in transaction processing time as compared with credit cards and other online services. Hashgraph, another distributed ledger technology, has emerged recently, which focuses on addressing this limitation, promising extraordinary improvement in transaction processing times. Although hashgraph was initially deployed in private settings, public deployment has been recently launched, enabling its adoption for wider application domains. Within this context, this chapter is focused on presenting a comparative study of these distributed ledger technologies, identifying strengths and weaknesses to highlight potential application domains.

In our world of growing machine intelligence and increasing security risks, there is a dire need for authentication to be liberated from password dependency and restrictions. This paper discusses the implementation of keystroke biometrics to enhance security using machine-learning algorithms on both Windows and Android. Our research analyzes a user's behavior for authorization purposes by capturing the user's typing pattern. The system extracts several features from the user's typing pattern to apply unary classification for user behavior analysis so that we can detect unauthorized users. Our system implements machine learning on tap dynamics in Android, allowing both training and prediction and overcoming its computational restrictions.

Electronic voting or e-voting has been used in varying forms since 1970s with fundamental benefits over paper-based systems such as increased efficiency and reduced errors. However, challenges remain to the achieving of wide spread adoption of such systems, especially with respect to improving their resilience against potential faults. Blockchain is a disruptive technology of the current era and promises to improve the overall resilience of e-voting systems. This article presents an effort to leverage benefits of blockchain such as cryptographic foundations and transparency to achieve an effective scheme for e-voting. The proposed scheme conforms to the fundamental requirements for e-voting schemes and achieves end-to-end verifiability. The article presents details of the proposed e-voting scheme along with its implementation using Multichain platform. The article also presents an in-depth evaluation of the scheme which successfully demonstrates its effectiveness to achieve an end-to-end verifiable e-voting scheme.

UAVs have numerous emerging applications in various domains of life. However, it is extremely challenging to gain the required level of public acceptance of UAVs without proving safety and security for human life. Conventional UAVs mostly depend upon the centralized server to perform data processing with complex machine learning algorithms. In fact, all the conventional cyber attacks are applicable on the transmission and storage of data in UAVs. While their impact is extremely serious because UAVs are highly dependent on smart systems that extensively utilize machine learning techniques in order to take decisions in human absence. In this regard, we propose to enhance the performance of UAVs with a decentralized machine learning framework based on blockchain. The proposed framework has the potential to significantly enhance the integrity and storage of data for intelligent decision making among multiple UAVs. We present the use of blockchain to achieve decentralized predictive analytics and present a framework that can successfully apply and share machine learning models in a decentralized manner. We evaluate our system using collaborative intrusion detection as a case-study in order to highlight the feasibility and effectiveness of using blockchain based decentralized machine learning approach in UAVs and other similar applications.

Blockchain has been adopted to address significant challenges, such as trust in diverse domains, including voting, logistics and finance. However, transaction malleability has been identified as a threat for blockchain, which can potentially lead to an inconsistent state that can result in further attacks such as double-spending. In this context, this paper is focused on investigating the feasibility of transaction malleability within a typical blockchain application aiming to identify scenarios that may lead to a successful transaction malleability attack. Our objective in doing so is to highlight conditions which cause such attack to facilitate the development of protection mechanisms for them. Specifically, this paper presents a successful simulation of transaction malleability attack within the context of blockchain-based electronic voting. The evaluation has identified the impact of parameters, such as network delay and block generation rate in achieving a successful transaction malleability attack, which highlights future directions of research.

The inception of quantum key distribution (QKD) empowered the communication networks with unconditional security because of the unique characteristics of quantum mechanics. Since QKD genesis, various protocols have been proposed to augment the feasibility of its real-world implementation and to overcome the inherent vulnerabilities of communication channel. In the past several years' studies emphasize the significance of QKD in the field of secure communication, because it can counter critical attacks quite effectively. In this paper, we present in-depth security analysis of the notable QKD protocols which encompasses from their theoretical assertions to their practical implementation factors and simulation outcomes.

DNA technology is rapidly moving towards digitization. Scientists use software tools and applications for sequencing, synthesizing, analyzing and sharing of DNA and genomic data, operate lab equipment and store genetic information in shared datastores. Using cutting-edge computing methods and techniques, researchers have decoded human genome, created organisms with new capabilities, automated drug development and transformed food safety. Such software applications are typically developed to progress scientific understanding and as such cyber security is never a concern for these applications. However, with the increasing commercialisation of DNA technologies, coupled with the sensitivity of DNA data, there is a need to adopt a security-by-design approach. In this paper we investigate bio-cyber security threats to genomic-DNA data and software applications making use of such data to advance scientific research. Specifically, we adopt an empirical approach to analyse and identify vulnerabilities within genomic-DNA databases and bioinformatics software applications that can lead to cyber-attacks affecting the confidentiality, integrity and availability of such sensitive data. We present a detailed analysis of these threats and highlight potential protection mechanisms to help researchers pursue these research directions.

Blockchain is a disruptive technology that has been used to address a wide range of challenges in diverse domains including voting, logistics, healthcare and finance. Transaction malleability is one of the critical threats for blockchain, which can facilitate double-spending attacks by tampering with the state of a blockchain. This paper investigates the potential of transaction malleability attack within a blockchain-based application (e-voting) with the aim to identify settings which can lead to a successful transaction malleability attack. Therein, we aim to highlight conditions which can cause transaction malleability attack so as to help develop appropriate protection mechanisms. In particular, a successful execution of transaction malleability attack is presented which was conducted on a blockchain testbed hosting an e-voting application. The experiments identified significance of parameters such as network delay and block generation rate to successfully execute transaction malleability attack and have highlighted directions for future research.

Android has become a major player in smartphone software arena, thanks to the massively positive reception of Google Play by the developers and users alike. In general, Android applications follow a set of permissions, which are used for access control. However, through the privilege-escalation vulnerability, a malicious application can escalate itself and access an un-permitted resource. Consequently, serious security and safety exploits such as privacy violation, reverse-shell access to the device, and drive-by downloads may occur. We propose a flexible and efficient defense mechanism against such exploits. Our solution — SAndroid, is an extensible and a lightweight application. It provides enhanced safety and security against privilege escalation attacks through rapid detection. SAndroid is based on active monitoring and detection of malicious applications through tracking of system logs and malicious process signatures. The assurance of safety provided by SAndroid is confirmed through design, testing, and verification. SAndroid follows modular approach permitting high flexibility and efficiency. Through real experiments, we confirmed that SAndroid is an efficient and low cost solution having negligible false-positives. This paper describes the architecture and design of the SAndroid framework and provides details of our experiments.