Intelligent Hybrid Intrusion Detection System for Financial Network Environments using Cryptography and Network Security

P Sai Priya; M Laasya; V Anusha anusha

Intelligent Hybrid Intrusion Detection System for Financial Network Environments using Cryptography and Network Security

Author(s)	Ms. P Sai Priya, Ms. M Laasya, Prof. V Anusha anusha
Country	India
Abstract	Recent changes in banking technology have introduced modern financial institutions, enabling seamless services such as online financial institutions, mobile transactions, ATM networks, and real-time fund transfers. However, this transformation has greatly increased the amount of sensitive data transmission, making banking systems they are highly susceptible to advanced cyber threats. The main challenge is achieving real-time intrusion detection while handling evolving and unknown attacks without disrupting transaction performance or customer experience. Traditional By comparing network activity with pre-established attack signatures, traditional signature-based intrusion detection systems identify intrusion attack traffic behavior and manual updates. which Consequently, their ability to identify zero-day attacks and adaptive cyber threats is limited. To address this problem, a hybrid intrusion detection approach combining Support Vector Machine (SVM) along with Isolation Forest is proposed. The SVM model effectively classifies known attack patterns, while the Isolation Forest detects anomalous and previously unseen network behaviours, improving zero-day attack detection capability. Training and assessment were carried out using benchmark network cybersecurity datasets for anomaly detection, such as NSL-KDD (or CICIDS dataset, if used), ensuring realistic evaluation including both legitimate and adversarial network activity. The evaluation results confirm that the hybrid model achieved an overall detection correctness of approximately 97–99 per cent, with improved anomaly detection rates and reduced false positives compared to standalone classifiers. In conclusion, the proposed hybrid SVM–Isolation Forest model delivers an efficient and reliable live intrusion detection security mechanism suitable for modern banking network environments.
Keywords	Secure Financial Network, Intrusion Detection Models, Hybrid Machine Learning, SVM, Isolation Forest, RealTime Detection, Cybersecurity
Field	Engineering
Published In	Volume 7, Issue 2, March-April 2026
Published On	2026-04-30

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Intelligent Hybrid Intrusion Detection System for Financial Network Environments using Cryptography and Network Security

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