The rapid advancements in artificial intelligence (AI) have presented new opportunities for enhancing efficiency and economic competitiveness across various industries, espcially in banking. Machine learning (ML), as a subset of artificial intelligence, enables systems to adapt and learn from vast datasets, revolutionizing decision-making processes, fraud detection, and customer service automation. However, these innovations also introduce new challenges, particularly in the realm of cybersecurity. Adversarial attacks, such as data poisoning and evasion attacks, represent critical threats to machine learning models, exploiting vulnerabilities to manipulate outcomes or compromise sensitive information. Furthermore, this study highlights the dual-use nature of AI tools, which can be used by malicious users. To address these challenges, the paper emphasizes the importance of developing machine learning models with key characteristics such as security, trust, resilience and robustness. These features are essential to mitigating risks and ensuring the secure deployment of AI technologies in banking sectors, where the protection of financial data is paramount. The findings underscore the urgent need for enhanced cybersecurity frameworks and continuous improvements in defensive mechanisms. By exploring both opportunities and risks, this paper aims to guide the responsible integration of AI in the banking sector, paving the way for innovation while safeguarding against emerging threats.
Cyber security threats to the payment and banking system have become a worldwide menace. The phenomenon has forced financial institutions to take risks as part of their business model. Hence, deliberate investment in sophisticated technologies and security measures has become imperative to safeguard against heavy financial losses and information breaches that may occur due to cyber-attacks. The proliferation of cyber crimes is a huge concern for various stakeholders in the banking sector. Usually, cyber-attacks are carried out via software systems running on a computing system in cyberspace. A…
Systematic Review of Cybersecurity in Banking: Evolution from Pre-Industry 4.0 to Post-Industry 4.0 in Artificial Intelligence, Blockchain, Policies and Practice
Innovations in Cardless Artificial Intelligence Banking: A Comprehensive Framework for Cyber Secure and Fraud Mitigation using Machine Learning Algorithms
Computer Engineering, University of Central Florida, Orlando, Florida, USA
The promise of quantum computing is not speeding up conventional computing rather delivering an exponential advantage for certain classes of problems, with profound implications for cybersecurity for instance. With the advent and development of quantum computers, cyberspace security can surely become the most critical problem for the Internet in near future. On contrary, prosaic quantum technology can be promising to transform cybersecurity. This research aims to synthesize basic and fundamental studies concerning quantum cybersecurity that can be emerged both as a threat and solution to criti…
As device interconnectivity and ubiquitous computing continues to proliferate healthcare, the Medical Internet of Things (MIoT), also well known as the, Internet of Medical Things (IoMT) or the Internet of Healthcare Things (IoHT), is certain to play a major role in the health, and well-being of billions of people across the globe. When it comes to issues of cybersecurity risks and threats connected to the IoT in all of its various flavors the emphasis has been on technical challenges and technical solution. However, especially in the area of healthcare there is another substantial and potenti…
Quantum Computing and Cybersecurity in Accounting and Finance: Current and Future Challenges and the Opportunities for Securing Accounting and Finance Systems in the Post-Quantum World
Increasing volatilities within power transmission and distribution force power grid operators to amplify their use of communication infrastructure to monitor and control their grid. The resulting increase in communication creates a larger attack surface for malicious actors. Indeed, cyber attacks on power grids have already succeeded in causing temporary, large-scale blackouts in the recent past. In this paper, we analyze the communication infrastructure of power grids to derive resulting fundamental challenges of power grids with respect to cybersecurity. Based on these challenges, we identif…
Neuro-Symbolic AI for Cybersecurity: State of the Art, Challenges, and Opportunities
Department of Information Systems, University of Maryland, Baltimore County（信息系统系，马里兰大学巴尔的摩分校）；Department of Computer Science and Engineering, University at Buffalo（计算机科学与工程系，布法罗大学）；Department of Computer Science, University of Colorado Boulder（计算机科学系，科罗拉多大学博尔德分校）；Laboratory for Cybersecurity Dynamics, Department of Computer Science, University of Colorado Colorado Springs（网络安全动力实验室，科罗拉多大学科罗拉多斯普林斯分校）
A technology platform that is gradually bridging the gap between object visibility and remote accessibility is the Internet of Things (IoT). Rapid deployment of this application can significantly transform the health, housing, and power (distribution and generation) sectors, etc. It has considerably changed the power sector regarding operations, services optimization, power distribution, asset management and aided in engaging customers to reduce energy consumption. Despite its societal opportunities and the benefits it presents, the power generation sector is bedeviled with many security chall…
The incremental diffusion of machine learning algorithms in supporting cybersecurity is creating novel defensive opportunities but also new types of risks. Multiple researches have shown that machine learning methods are vulnerable to adversarial attacks that create tiny perturbations aimed at decreasing the effectiveness of detecting threats. We observe that existing literature assumes threat models that are inappropriate for realistic cybersecurity scenarios because they consider opponents with complete knowledge about the cyber detector or that can freely interact with the target systems. B…
AI Potentiality and Awareness: A Position Paper from the Perspective of Human-AI Teaming in Cybersecurity
Cyber Security Cooperative Research Centre（网络安全合作研究中心）；Data61, CSIRO（联邦科学与工业研究组织数据61所）；Security Research Institute, Edith Cowan University（伊迪丝·考恩大学安全研究院）；Prince Mohammad Bin Fahd University（穆罕默德·本·法赫德王子大学）；Cyberstronomy Pty Ltd（Cyberstronomy私人有限公司）
Key components of current cybersecurity methods are the Intrusion Detection Systems (IDSs) were different techniques and architectures are applied to detect intrusions. IDSs can be based either on cross-checking monitored events with a database of known intrusion experiences, known as signature-based, or on learning the normal behavior of the system and reporting whether some anomalous events occur, named anomaly-based. This work is dedicated to survey the application of IDS to the Internet of Things (IoT) networks, where also the edge computing is used to support the IDS implementation. New c…
Explainable AI-based Intrusion Detection System for Industry 5.0: An Overview of the Literature, associated Challenges, the existing Solutions, and Potential Research Directions
Hamad bin Khalifa University（哈马德·本·哈利法大学）；Qatar Computing Research Institute（卡塔尔计算研究所）；Munster Technological University（芒斯特理工大学）；Toronto Metropolitan University（多伦多都会大学）
Electricity is an essential comfort to support our daily activities. With the competitive increase and energy costs by the industry, new values and opportunities for delivering electricity to customers are produced. One of these new opportunities is electric vehicles. With the arrival of electric vehicles, various challenges and opportunities are being presented in the electric power system worldwide. For example, under the traditional electric power billing scheme, electric power has to be consumed where it is needed so that end-users could not charge their electric vehicles at different poin…
Mississippi State University（密西西比州立大学）；U.S Army Engineer Research and Development Center（美国陆军工程研究与发展中心）
The application of Artificial Intelligence (AI) and Machine Learning (ML) to cybersecurity challenges has gained traction in industry and academia, partially as a result of widespread malware attacks on critical systems such as cloud infrastructures and government institutions. Intrusion Detection Systems (IDS), using some forms of AI, have received widespread adoption due to their ability to handle vast amounts of data with a high prediction accuracy. These systems are hosted in the organizational Cyber Security Operation Center (CSoC) as a defense tool to monitor and detect malicious network…
Islamic University (IU)（伊斯兰大学）；Queensland University of Technology (QUT)（昆士兰科技大学）；University of Information, Technology and Science (UITS)（信息科技大学）；University of the Sunshine Coast (USC)（阳光海岸大学）
Artificial Intelligence (AI) is one of the disruptive technologies that is shaping the future. It has growing applications for data-driven decisions in major smart city solutions, including transportation, education, healthcare, public governance, and power systems. At the same time, it is gaining popularity in protecting critical cyber infrastructure from cyber threats, attacks, damages, or unauthorized access. However, one of the significant issues of those traditional AI technologies (e.g., deep learning) is that the rapid progress in complexity and sophistication propelled and turned out t…
