Faculty Publications

Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736

Publications by NITK Faculty

Browse

Filters

2023 - 20242

Settings

Subject: search.filters.subject.Data Security

Search Results

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    Recent Advancements and Challenges in FinTech
    (Institute of Electrical and Electronics Engineers Inc., 2023) Girish, K.K.; Bhowmik, B.
    The rapid advancement of technology in recent years has brought about numerous changes in various industries, and the financial sector is no exception. The rise of financial technology (FinTech) has disrupted traditional banking and financial services by offering more convenient, accessible, and personalized services to customers. Contrarily, financial services have become more efficient, cost-effective, and secure with FinTech, enabling people to manage their finances with just a few clicks, even on their smartphones. FinTech has also created new opportunities for financial inclusion, making it possible for people who were previously unbanked or underbanked to access financial services. Despite its many benefits, the rise of FinTech has also brought about several challenges. This paper gives an overview of FinTech, its progress, and its importance. Following this, significant challenges of FinTech are highlighted to ensure its long-term success and continued growth. The recent literature shows the way how it is transforming our perceptions. © 2023 IEEE.
  • No Thumbnail Available
    Item
    Enhancing Big Data Security Through Anomaly Detection
    (Institute of Electrical and Electronics Engineers Inc., 2024) Vakkund, S.; Kumar, S.; Rao, S.; Anusha Hegde, H.; Bhowmik, B.
    Securing the massive and fast-moving data streams typical in Big Data environments presents unique challenges that traditional static security measures simply can't handle. To effectively protect these data flows, we need methods that can analyze traffic in real-time and respond swiftly to potential threats. Anomaly detection is one such method, offering an automated way to identify unusual or suspicious activities within Big Data systems. In this study, we explore several widely-used anomaly detection algorithms, evaluating their effectiveness in identifying anomalies within large datasets. Specifically, we will assess these algorithms using the UNSW-NB15 Dataset, aiming to pinpoint which algorithm, or combination of algorithms, is best suited for the demands of Big Data security. © 2024 IEEE.