报告详情
  1. 活动首页
  2. 报告清单
  3. 报告详情
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：19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

演示文件 附属文件

提示：该报告下的文件权限为仅限参会人，您尚未登录，暂时无法查看。

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
jafar ababneh Jafar Ababneh Cyber Security department； Faculty of Information Technology Zarqa University Zarqa； Jordan jababneh@zu.edu.jo
Roksana Zarychta Krakow； Poland；Institute of Law； Economics and Administration； University of the National Education Commission
Jamal Alkhasawnah Mutah University
Maen Alradawneh Mutah University
Hassan Ali Al-Ababneh Zarqa University
Musab Iqtait miqtait@gmail.com
M. N. Al Refai Department of Software Engineering Science, Zarqa University, Zarqa, Jordan
Mohamed Hafez INTI-IU-University;Shinawatra University
发表评论
验证码 看不清楚，更换一张
全部评论
重要日期

  • 会议日期

    07月30日

    2026

    至

    08月01日

    2026

  • 07月28日 2026

    初稿截稿日期

  • 08月03日 2026

    注册截止日期

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
联系方式
登录查看完整联系方式
历届会议
移动端
在手机上打开
小程序
打开微信小程序
客服
扫码或点此咨询