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Cited by 892Security and Verification in ComputingAdvanced Malware Detection TechniquesNetwork Security and Intrusion Detection

A survey of emerging threats in cybersecurity

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Jang-Jaccard, Julian, Nepal, Surya·2014·Journal of Computer and System Sciences
DOI 10.1016/j.jcss.2014.02.005
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  • AI-based techniques such as machine learning models for anomalous behavior detection and biometric analyses can quickly analyze large volumes of transactional data and detect and flag potentially fraudulent activity in real time.

    “Techniques based on AI, such as machine learning models for anomalous behavior detection and biometric analyses, can provide a way to quickly analyze large volumes of transactional data and detect and flag potentially fraudulent activity in real time (Bhatla et al. 2003; Jang-Jaccard and Nepal 2014; Homer 2020).”

    From AI-driven financial fraud detection in Pakistan’s banking sector: bridging strategic intent and operational implementation (2026) · cites this paper as Jang-Jaccard and Nepal 2014

    Section 3.4 (page unavailable) describes self-learning anomaly detection that examines network traffic, often in real time, and reports abnormal behavior. It does not establish that biometric analyses or these models rapidly process large volumes of transactional data to flag potentially fraudulent transactions; its discussion of biometrics concerns authentication. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

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Cited by

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Authors
Jang-Jaccard, J., & Nepal, S.
Venue
Journal of Computer and System Sciences
Publisher
Elsevier BV
Published
2014-08
Volume
80
Issue
5
Pages
973-993
ISSN
0022-0000 (ISSN-L)
DOI
10.1016/j.jcss.2014.02.005
OpenAlex
W1966741850
Cited by
892
References
0
Citations by year
2026: 36 · 2025: 94 · 2024: 100 · 2023: 124 · 2022: 161 · 2021: 163 · 2020: 71 · 2019: 53 · 2018: 27 · 2017: 19 · 2016: 21 · 2015: 12 · 2014: 10 · 2013: 1
FWCI
20.60
Type
journal article
Language
en
Open access
Licence
Locations
Journal of Computer and System Sciences (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Security and Verification in Computing
  • Physical Sciences › Computer Science › Signal Processing › Advanced Malware Detection Techniques
  • Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
Keywords
emerging threats, malware, malware protection, software vulnerabilities, hardware vulnerabilities, network vulnerability, vulnerability exploitation, attack patterns, social media security, cloud security, smartphone security, critical infrastructure security

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