Artificial Intelligence and Cyber Defense System for Banking Industry: A Qualitative Study of AI Applications and Challenges

AL-Dosari, Khalifa; Fetais, Noora; Küçükvar, Murat

doi:10.1080/01969722.2022.2112539

Cited by 39 publications

(12 citation statements)

References 28 publications

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“…• Signature-based detection: Signature-based detection, also known as rule-based detection, involves matching known patterns or signatures of malicious code against incoming data to identify threats. While effective at detecting known malware and well-defined attack patterns, signature-based detection is limited by its inability to detect zeroday exploits or previously unseen threats [19]. Additionally, maintaining up-to-date signature databases can be challenging, leaving organizations vulnerable to emerging threats.…”

Section: Ai-powered Threat Detection Mechanismsmentioning

confidence: 99%

“…• Machine learning-based detection: Machine learning-based detection leverages advanced algorithms and statistical models to analyze vast amounts of data and identify patterns indicative of cyber threats. By learning from labeled training data, machine learning algorithms can classify and prioritize security alerts, detect previously unseen threats, and adapt to changing threat landscapes over time [14,19]. Machine learning-based detection techniques, including supervised learning, unsupervised learning, and semi-supervised learning, offer scalable and adaptive solutions for threat detection, enabling organizations to stay ahead of emerging threats and improve their overall CS posture.…”

Section: Ai-powered Threat Detection Mechanismsmentioning

confidence: 99%

“…• Deep learning-based detection: Deep learning-based detection represents the cutting-edge of AI-powered threat detection, leveraging neural networks with multiple layers of interconnected nodes to analyze complex data and extract meaningful features [18,19]. Deep learning algorithms, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), excel at identifying subtle patterns and anomalies in large datasets, making them well-suited for detecting sophisticated cyber threats, including advanced malware, phishing attacks, and insider threats [13,14].…”

Section: Ai-powered Threat Detection Mechanismsmentioning

confidence: 99%

“…The bank collaborated with a team of data scientists and CS experts to develop and deploy an AI-driven threat detection system [19]. The system utilized machine learning algorithms, including supervised and unsupervised learning techniques, to analyze vast volumes of network traffic, system logs, and user behavior data in real-time.…”

Section: Case Studymentioning

confidence: 99%

See 3 more Smart Citations

Exploring the Impact of Artificial Intelligence Integration on Cybersecurity: A Comprehensive Analysis

Goswami,

Mondal,

Halder

et al. 2024

JII

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The rapid advancement of technology has correspondingly escalated the sophistication of cyber threats. In response, the integration of artificial intelligence (AI) into cybersecurity (CS) frameworks has been recognized as a crucial strategy to bolster defenses against these evolving challenges. This analysis scrutinizes the effects of AI implementation on CS effectiveness, focusing on a case study involving company XYZ's adoption of an AI-driven threat detection system. The evaluation centers on several pivotal metrics, including False Positive Rate (FPR), Detection Accuracy (DA), Mean Time to Detect (MTTD), and Operational Efficiency (OE). Findings from this study illustrate a marked reduction in false positives, enhanced DA, and more streamlined security operations. The integration of AI has demonstrably fortified CS resilience and expedited incident response capabilities. Such improvements not only underscore the potential of AI-driven solutions to significantly enhance CS measures but also highlight their necessity in safeguarding digital assets within a continuously evolving threat landscape. The implications of these findings are profound, suggesting that leveraging AI technologies is imperative for effectively mitigating cyber threats and ensuring robust digital security in contemporary settings.

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Section: Ai-powered Threat Detection Mechanismsmentioning

confidence: 99%

Section: Ai-powered Threat Detection Mechanismsmentioning

confidence: 99%

Section: Ai-powered Threat Detection Mechanismsmentioning

confidence: 99%

Section: Case Studymentioning

confidence: 99%

See 2 more Smart Citations

Exploring the Impact of Artificial Intelligence Integration on Cybersecurity: A Comprehensive Analysis

Goswami,

Mondal,

Halder

et al. 2024

JII

View full text Add to dashboard Cite

show abstract

“…The banking sector is now going through a significant technological change. Understanding how emerging technologies like artificial intelligence (AI) affect banks' cybersecurity becomes crucial [5]. The development of FinTech and other technologies like IoT, Big Data, blockchain, artificial intelligence (AI), and machine learning are occurring simultaneously in the banking and financial services industry [6].…”

Section: Introductionmentioning

confidence: 99%

Utilizing Bio Metric System for Enhancing Cyber Security in Banking Sector: A Systematic Analysis

et al. 2023

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Biometric authentication is gaining the interest of private, public, consumer electronics and corporate security systems. For the protection of cyberspace from hackers and other harmful people, biometric security is growing more and more popular among organizations, individuals and enterprises. The word ''cyber security'' refers to the procedures, techniques, and tools used to safeguard data, network system, computer networks and software from potential attacks online. Online financial service delivery is referred to as ''cyber banking.'' As the trend of exchanging things has changed, internet banking has grown. Despite the benefits, there have been instances of security threat-related issues with Internet banking. To identify persons, biometric security verifies their physical attributes and behavioral traits. For identification verification, it is the most reliable and effective physical security method. According to biometric authentication, people can be recognized precisely based on their innate behavioral or physical traits. Numerous security measures have been implemented throughout the entire Internet banking service to address these issues. Globally, cybercrime has deep roots and poses a significant threat to the occurrence of criminal or terrorist behavior. Without being addressed by a single authority, these risks can compromise security on the inside as well as the outside. If the cybercrime goes unnoticed, both money and personal data are lost. Internet services and information infrastructure have previously been targeted in assaults. Online fraud and hacker attacks are only two examples of the daily computer-related crimes that take place. The Internet of Things (IoT) is the most reliable foundation for facilitating high-quality, comfortable human living. IoT has had a substantial impact across a range of application domains. Smart gadgets are more vulnerable to hackers because of their rapid development and trust in wireless mechanics for data transport. As a result, the rate of cybercrime is rising daily. Artificial Intelligence (AI)-based cybersecurity emerges because of technological advancement and poses a risk to public safety, personal property rights, and privacy protection for people. The study elaborates on the key features of biometrics system in conventional and Islamic banking to counter the risk of cybersecurity and provide high safety and security to the banking industry. For this systematic literature review, the most suitable and most relevant 101 articles from the reputed online libraries are selected. This analysis absorbed four research questions and pertinent keywords from the period of 2009 to 2022 (a part of 2023 was included).

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Addressing Current and Emerging Challenges in DF: An Examination and Recommendations

Cho,

Montasari

2024

Advanced Sciences and Technologies for Security Applications

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Artificial Intelligence and Cyber Defense System for Banking Industry: A Qualitative Study of AI Applications and Challenges

Cited by 39 publications

References 28 publications

Exploring the Impact of Artificial Intelligence Integration on Cybersecurity: A Comprehensive Analysis

Exploring the Impact of Artificial Intelligence Integration on Cybersecurity: A Comprehensive Analysis

Utilizing Bio Metric System for Enhancing Cyber Security in Banking Sector: A Systematic Analysis

Addressing Current and Emerging Challenges in DF: An Examination and Recommendations

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