Muntadher Sallal

FinTech witnessed a significant revolution in digital transformation where core processes and functionalities are delivered through fully digital platforms. AI enabled major innovations that support automated and distributed finance in FinTech by introducing machine learning and deep learning methods and techniques that enhance automation, decision-making, and security across financial ecosystems. Recently, Agentic AI emerged as a promising AI technology that takes digital transformation to another level where full autonomous and adaptive financial service operations across multiple FinTech platforms and environments are achieved. However, Agentic AI in FinTech remains critically constrained by various risks such as security vulnerabilities, regulatory challenges, and autonomous decision-making errors. This chapter explores the adoption of agentic AI in financial services and how this technology accelerated the FinTech digital transformation. Furthermore, this chapter critically analyses vulnerabilities in FinTech agentic AI and categorising the associated risks within the FinTech

Integrating intelligent grids with the internet increases the amount of unauthorized input data which directly or indirectly influences electrical system control and decision-making. Photovoltaic (PV) farms that are linked to the power grid are susceptible to cyber attacks which may disrupt energy infrastructure and compromise the security, stability, and resilience of the electrical system. This research proposes a new model for cyber threat detection in PV farm, named as Cyber Detection in PV farm (CDPV), which makes use of deep learning methods based solely on point-of-common coupling (PCC) detectors. In this paper, a thorough cyber attack model for a photovoltaic (PV) farm is developed, where the simulation of four kinds of cyber attacks is provided. Furthermore, this paper evaluates the role of three deep learning techniques including convolutional neural network (CNN), artificial neural network (ANN), and long short-term memory (LSTM), in PV cyber threat detection. The findings demonstrate that, at the DC/DC converter and DC/AC inverter sides, the proposed CDPV model based on deep learning techniques (CNN, ANN, and LSTM) can improve the cyber detection accuracy and resilience under various attack scenarios.

The radio frequency spectrum serves as a fundamental resource for wireless communication, encompassing various frequency bands allocated for diverse services and applications. Dynamic spectrum management (DSM) is essential to optimise the utilisation of this limited and valuable natural resource, with the aim of improving performance and adapting to changing wireless communication demands. Traditional static spectrum allocation methods have shown inefficiencies, leading to spectrum scarcity and under-utilisation. To address these challenges, the integration of blockchain and Cognitive Radio (CR) technologies has emerged as a promising approach. Blockchain, with its decentralised and secure attributes, can improve transparency and trust in spectrum allocation processes, while CR enables intelligent spectrum sensing and allocation to maximise utilisation. However, this promising approach comes with its own critical challenges, especially when dealing with the 6th Generation (6G) mobile communication. These challenges are related to the fact that the blockchain ecosystem needs to be interoperable and scalable enough to be compatible with the 6G high-demand and substantial resources. Specifically, integrating blockchain with CR requires efficient interoperability techniques where blockchain can easily and effectively interact with the CR platforms as well as radio spectrum environments. Furthermore, the spectrum management system over 6G networks needs to be designed in a way where massive 6G resources can be accommodated and managed without having any service performance degradation. This paper introduces a novel radio spectrum management model in 6G networks, named as BSM-6G, which integrates blockchain technology with CR where interoperability is preserved and scalability is maximised. Specifically, the proposed BSM-6G model merges blockchain’s transparent record keeping with CR’s intelligent spectrum management capabilities. To overcome the interoperability issue, BSM-6G provides an interoperable blockchain Oracle approach which facilitates the real-time interaction among the blockchain platform, the CR, and any data sources off-chain. This paper details all the technical and procedural challenges when implementing the proposed interoperability Oracle approach. To address the scalability challenge, BSM-6G utilizes the Proof-of-History (PoH) consensus protocol to align with the requirements of DSM in advanced networks like Beyond 5th Generation (B5G) and 6G. Evaluation results indicate that BSM-6G offers viable and less complex blockchain Oracle integration architecture measured by the technical implementation of BSM-6G, as well as low interoperability cost measured by transaction response time and transaction fee cost. Compared to state-of-the-art spectrum-based blockchain systems, BSM-6G shows a high scalable DSM-based blockchain in 6G networks measured by transactions per second (TPS).

Online voting for independent elections is generally supported by trusted election providers. Typically these providers do not offer any way in which a voter can verify their vote, and hence the providers are trusted with ballot privacy and in ensuring correctness. Despite the desire to offer online voting for political elections, this lack of transparency and verifiability is often seen as a significant barrier to the large-scale adoption of online elections. Adding verifiability to an online election increases transparency and integrity, as well as allowing voters to verify that the vote they cast has been recorded correctly and included in the tally. However, replacing existing online systems with those that provide verifiable voting requires new algorithms and code to be deployed, and this presents a significant business risk to commercial election providers, as well as the societal risk for official elections selecting for public office. In this paper we present the first step in an incremental approach which minimises the business risk but demonstrates the advantages of verifiability, by developing an implementation of key elements of a Selene-based verifiability layer and adding it to an operational online voting system. Selene is a verifiable voting protocol that publishes votes in plaintext alongside a voter's tracker. These trackers enable voters to confirm that their votes have been captured correctly by the system, such that the election provider does not know which tracker has been allocated to which voter. This results in a system where even a “dishonest but cautious” election authority running the system cannot be sure of changing the result in an undetectable way, and hence gives stronger guarantees on the integrity of the election than were previously present. We explore the challenges presented by adding a verifiability layer to an operational system. The system was used in two initial trials conducted within real contested elections. We conclude by outlining the further steps in the road-map towards the deployment of a fully trustworthy online voting system.

The digital advancement of an industrial environment where human and robot collaborate to achieve specific tasks, can be vulnerable to cyber threats. These industries use the current risk management frameworks (for example CVSS-v3) to manage potential vulnerabilities. However, the current frameworks do not consider the modes of work when rating the severity. This paper introduces a novel risk driven security framework (RDSF) which evaluates potential cyber security vulnerabilities to determine their effect/impact in relation to robot in industrial environments based on robot modes of work. The paper also describes how to use RDSF as a component of the implementation of security management in autonomous robot industries.

Privacy and verifiability are crucial security requirements in e-voting systems and combining them is considered to be a challenge given that they seem to be contradictory. On one hand, privacy means that cast votes cannot be traced to the corresponding voters. On the other hand, linkability of voters and their votes is a requirement of verifiability which has the consequence that a voter is able to check their vote in the election result. These two contradictory features can be addressed by adopting privacy-preserving cryptographic primitives, which at the same time as achieving privacy, achieve verifiability. Many end-to-end schemes that support verifiability and privacy have the need for some voter action. This makes ballot casting more complex for voters. We propose the PVPBC voting system, which is an e-voting system that preserves privacy and verifiability without affecting voter usability. The PVPBC voting system uses an effective and distributed method of authorization, which is based on revocable anonymity, by making use of a permissioned distributed ledger and smart contract. In addition, the underlying PVPBC voting system satisfies election verifiability using the Selene voting scheme. The Selene protocol is a verifiable e-voting protocol. It publishes votes in plaintext accompanied by tracking numbers. This enables voters to confirm that their votes have been captured correctly by the system. Numerical experiments support the claim that PVPBC scales well as a function of the number of voters and candidates. In particular, PVPBC’s authorization time increases linearly as a function of the population size. The average latency associated with accessing the system also increases linearly with the voter population size. The latency incurred when a valid authentication transaction is created and sent on the DLT network is 6.275 ms. Empirical results suggest that the cost in GBP for casting and storing an encrypted ballot alongside a tracker commitment is a linear function of the number of candidates, which is an attractive aspect of PVPBC.

Online voting for independent elections is generally supported by trusted election providers. Typically these providers do not offer any way in which a voter can verify their vote, so the providers are trusted with ballot privacy and ensuring correctness. Despite the desire to offer online voting for political elections, this lack of transparency and verifiability is often seen as a significant barrier to the large-scale adoption of online elections. Adding verifiability to an online election increases transparency and integrity, allowing voters to verify that their vote has been recorded correctly and included in the tally. However, replacing existing online systems with those that provide verifiable voting requires new algorithms and code to be deployed, and this presents a significant business risk to commercial election providers. In this paper we present the first step in an incremental approach which minimises the business risk but demonstrates the advantages of verifiability, by developing an implementation of key elements of a Selene-based verifiability layer and adding it to an operational online voting system. Selene is a verifiable voting protocol that uses trackers to enable voters to confirm that their votes have been captured correctly while protecting voter anonymity. This results in a system where even the election authority running the system cannot change the result in an undetectable way, and gives stronger guarantees on the integrity of the election than were previously present. We explore the challenges presented by adding a verifiability layer to an operational system. We describe the results of two initial trials, which obtained that survey respondents found this form of verifiability easy to use and that they broadly appreciated it. We conclude by outlining the further steps in the road-map towards the deployment of a fully trustworthy online voting system.