AEGIS: Analytical Engine for Governance, Intelligence & Surveillance

DOI

10.2991/978-94-6463-852-3_3How to use a DOI?

Keywords

cybersecurity; threat detection; compliance management; DDoS attack detection; spam email classification; financial transaction monitoring; real-time analysis; ensemble learning; anomaly detection; data security

Abstract

The growing number of emerging businesses has increased the risk of cyber threats and financial fraud. Common methods employed by cybercriminals for malicious purposes are phishing emails, compromised credentials, and DDoS attacks. Unlike conventional single-threat detection systems, AEGIS offers complete security and diminishes operational overhead. It introduces the first-of-its-kind unified cybersecurity framework through which real-time DDoS mitigation, prevention of financial fraud, and spam filtering are carried out through a novel dual-pipeline architecture. The underlying systems consist of three core modules: a real-time DDoS attack detector based on anomaly detection algorithms, a financial fraud detection mechanism where fraudulent bank transactions are identified, and a spam email classifier using supervised-learning algorithms for maximum accuracy. Together, these modules let AEGIS detect potential threats in real-time, prevent fraud, and efficiently mitigate them as a pathway to reduction of risk. The system results demonstrate efficacy by reducing false positives and enhancing overall security safeguards. AEGIS is conceptualized as a Surveillance and Compliance Management system to safeguard the digital and network resources. AEGIS hereby aims to act as a real-time monitor to address these challenges by satisfying the regulatory requirements of government agencies and businesses.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Vishal Jadhav
AU  - Arya Khot
AU  - Aditi Honagekar
AU  - Niharika Kamat
AU  - Vanita Mane
PY  - 2025
DA  - 2025/10/07
TI  - AEGIS: Analytical Engine for Governance, Intelligence & Surveillance
BT  - Proceedings of the MULTINOVA: First International Conference on Artificial Intelligence in Engineering, Healthcare and Sciences (ICAIEHS- 2025)
PB  - Atlantis Press
SP  - 21
EP  - 43
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-852-3_3
DO  - 10.2991/978-94-6463-852-3_3
ID  - Jadhav2025
ER  -