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Reconciling Privacy and Security: A Multi-Objective Optimization Framework for Ethical Cyber Defense Systems (MOOF-ECDS)
编号：91 访问权限：仅限参会人 更新：2026-07-22 16:09:52 浏览：13次 Online

报告开始：2026年07月30日 16:10（Asia/Kolkata）

报告时间：15min

所在会场：[S3] Cyber Security [S3-2] Cyber Security

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摘要
The increasing complexity of cyber threats requires correspondingly improved security systems, frequently utilizing deep learning (DL) for intrusion detection. Nonetheless, these potent systems often function with excessive permissions, resulting in considerable privacy violations due to the over-collection and examination of network and user data. This issue establishes a significant ethical and technical dichotomy between security effectiveness and privacy protection. This research introduces an innovative ethical cyber defense framework that systematically conceptualizes this issue as a multi-objective optimization problem (MOOP). Our approach employs a hybrid metaheuristic algorithm, combining the exploratory power of the Grey Wolf Optimizer (GWO) with the predictive accuracy of a Deep Neural Network (DNN) to dynamically tune the parameters of a network intrusion detection system (NIDS). The main goals are to concurrently enhance threat detection rates (true positive rate) and reduce privacy-invasive data collecting, measured by an innovative privacy impact score. The GWO-DNN hybrid is trained and validated on the CIC-IDS2017 dataset, augmented with synthetic data to simulate privacy-sensitive scenarios. Experimental findings indicate that the proposed framework attains Pareto-optimal equilibrium, substantially decreasing the privacy footprint by as much as 40% relative to a security-maximized baseline, while preserving a high detection accuracy of 98.5%. The paper indicates that multi-objective optimization offers a mathematically rigorous approach to creating cybersecurity systems that are both secure and fundamentally ethical, adhering to contemporary data protection requirements such as GDPR and CCPA.
 
关键词
Cybersecurity, Deep Learning, Ethical AI, Intrusion Detection Systems, Grey Wolf Optimizer, Multi-Objective Optimization, Privacy Preservation.
报告人
Ababneh Jafar
phd Zarqa University

稿件作者
Ali Abuzaid zarqa university
jafar ababneh Jafar Ababneh Cyber Security department； Faculty of Information Technology Zarqa University Zarqa； Jordan jababneh@zu.edu.jo
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重要日期

  • 会议日期

    07月30日

    2026

    至

    08月01日

    2026

  • 07月28日 2026

    注册截止日期

  • 07月30日 2026

    初稿截稿日期

主办单位
The United Societies of Science
承办单位
Kongunadu College of Engineering and Technology
协办单位
IEEE Section
IEEE Madras Section
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