A Comprehensive Study of Artificial Intelligence and Cybersecurity on Bitcoin, Crypto Currency and Banking System

Choithani, Tamanna; Chowdhury, Asmita; Patel, Shriya; Patel, Poojan; Patel, Dharmendrakumar A.; Shah, Manan

doi:10.1007/s40745-022-00433-5

Cited by 22 publications

(19 citation statements)

References 45 publications

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“…• Phishing attacks: Phishing remains one of the most prevalent and widely-used tactics employed by cybercriminals to deceive users and gain unauthorized access to sensitive information [10,11]. These attacks often involve the use of fraudulent emails, websites, or messages designed to trick recipients into disclosing personal data, login credentials, or financial information.…”

Section: Figure 1 Road Map Of This Study 2 Evolution Of Cyber Threatsmentioning

confidence: 99%

“…AI has emerged as a transformative technology in the realm of CS, offering a wide array of capabilities to bolster defenses against evolving cyber threats [7,11]. From threat detection and anomaly detection to malware analysis and incident response, AI-driven solutions play a pivotal role in fortifying organizations' cyber resilience and mitigating risks effectively.…”

Section: Role Of Ai In Csmentioning

confidence: 99%

“…• Threat detection: AI-powered threat detection leverages advanced machine learning algorithms to analyze vast volumes of data from diverse sources, including network traffic, system logs, and user behavior patterns [11,15]. By learning from historical data and identifying patterns indicative of potential threats, AI algorithms can detect anomalous activities and security breaches in real-time, enabling proactive threat mitigation.…”

Section: Role Of Ai In Csmentioning

confidence: 99%

“…• Behavior-based detection: Behavior-based detection focuses on analyzing the behavior of software or users within a network to identify deviations from normal behavior that may indicate malicious activities. AI-powered behavior-based detection systems leverage machine learning algorithms to establish baseline behavior profiles and detect anomalies indicative of cyber threats, such as unusual network traffic patterns, unauthorized access attempts, or suspicious file activities [9][10][11]. By continuously learning and adapting to evolving threats, behavior-based detection offers improved DA and resilience against zero-day attacks.…”

Section: Ai-powered Threat Detection Mechanismsmentioning

confidence: 99%

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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: Figure 1 Road Map Of This Study 2 Evolution Of Cyber Threatsmentioning

confidence: 99%

Section: Role Of Ai In Csmentioning

confidence: 99%

Section: Role Of Ai In Csmentioning

confidence: 99%

Section: Ai-powered Threat Detection Mechanismsmentioning

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

“…AI needs a foundation of specialized hardware and software for building and training machine learning algorithms, which may take hours or days to do manually. However, AI algorithms can do anti-money laundering tasks in seconds (Choithani et al, 2022). AI is mainly used in financial systems to provide appropriate services and schemes to customers.…”

mentioning

confidence: 99%

An introduction of transaction session‐induced security scheme using blockchain technology: Understanding the features of Internet of Things–based financial security systems

Wen,

Han

2024

Manage Decis Econ

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Internet of Things (IoT)–based financial systems leverage the capabilities of blockchain and artificial intelligence (AI) to enable seamless transactions and data exchange between devices. IoT‐based financial systems involve interconnected devices and services, such as payment terminals, wearables, and smart appliances, which collect, transmit, and process sensitive financial information. The study explores security methods incorporated in the financial systems designed using IoT and blockchain technologies to improve the background features. The study data were gathered from Complaint Data from the Consumer Financial Protection Bureau 2018–2022, and data‐based analysis is used in this study for detecting illegitimate interrupted transactions. Propensity Score Matching (PSM) is used for the robustness and endogeneity test; descriptive statistics is utilized in this study. Financial security systems are introduced to reduce the forging and breaching of intruders amid transactions. This study offers a novel contribution to the field of blockchain technology by furnishing a comprehensive analysis of the features of IoT‐based financial security systems from the perspective of the transaction, broadening the understanding of the feature focusing on financial security, and providing practical recommendations to address the features of IoT‐based financial security systems in blockchain technology. The study highlights how IoT devices can securely record and verify financial transactions by leveraging the blockchain's distributed ledger, preventing tampering or unauthorized access. The results of the study identify that the TS3 program relies on the transaction gaps between financial sessions, security requests between successive transactions, and sessions saved depending on the time delay. The study finds that sessions were analyzed for violations and fraud using information stored on the blockchain. The study suggests the design and building of devices and sensors in an IoT in financial security systems. Transparency should contribute to setting data privacy and safety problems in financial security systems.

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Artificial Intelligence Applications in the Indian Financial Ecosystem

Manda,

Nihar

2024

Artificial Intelligence for Risk Mitigation in the Financial Industry

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A Comprehensive Study of Artificial Intelligence and Cybersecurity on Bitcoin, Crypto Currency and Banking System

Cited by 22 publications

References 45 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

An introduction of transaction session‐induced security scheme using blockchain technology: Understanding the features of Internet of Things–based financial security systems

Artificial Intelligence Applications in the Indian Financial Ecosystem

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