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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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| Paper Title: | The Nexus of AI and Cybersecurity: An In-depth Analysis of Machine Learning and Deep Learning Techniques in Anomaly Detection |
| Authors Name: | Subharun Pal |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_186698
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| Published Paper Id: | IJRTI2305119 |
| Published In: | Volume 8 Issue 5, May-2023 |
| DOI: | https://doi.org/10.5281/zenodo.10116316 |
| Abstract: | This scholarly exploration delves into the burgeoning domain of Artificial Intelligence (AI), with particular focus on its application in anomaly detection, a pivotal component of modern cybersecurity measures. The objective of this discourse is to meticulously analyse the interplay of AI's two primary subfields - Machine Learning (ML) and Deep Learning (DL) - in the realm of anomaly detection, with an emphasis on their efficacy in forestalling fraudulent activities and cyber-attacks. The research methodology adopted is a rigorous synthesis of both quantitative and qualitative approaches, with an overarching emphasis on empirical data analysis. This approach allowed for a comprehensive exploration of the myriad facets of AI, ML, and DL, and their implications for anomaly detection. The key findings suggest that the integration of advanced AI techniques, particularly ML and DL, can significantly bolster the potency of anomaly detection systems. This, in turn, can create a fortified bulwark against cyber threats, thereby curbing potential financial losses and ensuring regulatory compliance. Nevertheless, the research also unveils certain challenges, notably the need for high-quality data, the complexity of developing and tuning models, and the ongoing requirement for human oversight to manage false positives and negatives. This research, therefore, holds profound implications for both industry and academia. From an industrial perspective, it elucidates a path towards enhanced cybersecurity systems, whilst from an academic viewpoint, it enriches the existing compendium of knowledge in this rapidly evolving field. |
| Keywords: | AI, Anomaly Detection, Machine Learning, Deep Learning, Fraud Detection, Cyber Attacks, Cybersecurity, Predictive Models, Empirical Analysis, Data Analysis, Model Tuning, False Positives, False Negatives, Cyber Threat Mitigation, Regulatory Compliance. |
| Cite Article: | "The Nexus of AI and Cybersecurity: An In-depth Analysis of Machine Learning and Deep Learning Techniques in Anomaly Detection", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.8, Issue 5, page no.759 - 763, May-2023, Available :http://www.ijrti.org/papers/IJRTI2305119.pdf |
| Downloads: | 000205238 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
| Publication Details: |
Published Paper ID: IJRTI2305119
Registration ID:186698
Published In: Volume 8 Issue 5, May-2023
DOI (Digital Object Identifier): https://doi.org/10.5281/zenodo.10116316
Page No: 759 - 763 Country: Durgapur, Paschim Bardhaman, West Bengal, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2305119 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2305119 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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