Authors:
Anala Venkata Sai Abhishek

Addresses:
Department of Computer Science, University of Central Missouri, Warrensburg, Missouri, United States of America.

Abstract:

The progression of complexity in cyber-attacks requires the migration from reactive defenses to proactive defenses. This paper provides a design and assessment of an autonomous predictive threat detection system in cyberspace. The system employs machine learning, combined with real-time analysis, to predict and counter threats before they affect the system. The framework is based on a new ensemble learning algorithm that combines a deep neural network and a random forest classifier, striking a balance between high accuracy and a low false-positive rate. The framework was evaluated using the CICIDS2017 dataset, a real and extensive network traffic dataset that features a wide range of modern cyberattacks. The system was developed using Python as the programming language, along with Scikit-learn and TensorFlow, two prominent machine learning libraries. The result is that the autonomous system can detect different types of cyberattacks with an accuracy rate of over 98%, compared to traditional signature-based or individual machine learning-based detection methods. Autonomy of the system reduces human interaction, thereby enabling real-time and scalable cyber defense. The conclusion reached in this study provides a solid foundation for developing the next generation of predictive security solutions, particularly in the context of cybersecurity.

Keywords: Autonomous Systems; Predictive Threat Detection; Cyber Attacks; Machine Learning; Threat Intelligence; Scikit-Learn and TensorFlow; Pattern Recognition; Decision Making; Technology Revolution.

Received on: 22/09/2024, Revised on: 25/11/2024, Accepted on: 29/12/2024, Published on: 03/06/2025

DOI: 10.69888/FTSIN.2025.000382

FMDB Transactions on Sustainable Intelligent Networks, 2025 Vol. 2 No. 2, Pages: 80-90

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