A Study on Neural Architecture Search for Intrusion Detection in Cybersecurity: A Lightweight Reinforcement Learning Approach

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：119

、訪客IP：18.220.1.11

姓名

陳麒安(CHI-AN CHEN) 查詢紙本館藏

畢業系所

軟體工程研究所

論文名稱

(A Study on Neural Architecture Search for Intrusion Detection in Cybersecurity: A Lightweight Reinforcement Learning Approach)

相關論文

★ 應用多核特徵線嵌入法進行高光譜影像分類	★ 基於SIFT演算法進行車牌認證
★ 利用自適性權重估測機制改善傳統爬山演算法之對焦問題	★ 以核心模糊最近特徵線轉換法做人臉辨識
★ 利用模糊最近特徵線轉換做人臉辨識	★ 基於Leap Motion之三維手寫中文文字特徵擷取
★ 使用人臉辨識強化VPN身份認證	★ 應用核心最近特徵線轉換做人臉辨識
★ 應用相鄰最近特徵空間轉換法於跌倒偵測	★ 使用Sentinel -2 影像提出空間、光譜與時間的深度學習架構製作佛羅里達州西南部於2017年受艾瑪颶風影響之紅樹林退化圖
★ 利用深度學習方法檢測震前電離層異常	★ 建議的 LSST-Former 深度學習架構基於少樣本學習，用於小資料集的紅樹林損耗檢測
★ 衛星降水資料於高衝擊天氣和滑坡事件的應用研究	★ 以深度學習進行遙測影像植生區域偵測
★ 基於VIT及向日葵8號氣象衛星台灣區域雨量預測之可行性評估	★ 基於SPOT-7衛星影像之台灣土地使用分析

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 (2029-7-1以後開放)

摘要(中)

近年來，隨著網絡安全的快速發展，傳統入侵檢測系統（IDS）的局限性逐漸顯現，而深度學習（DL）在增強檢測能力方面展現了巨大的潛力。在本文中，我們介紹了一個名為輕量虛擬深度Q網絡（LV-DQN）的創新框架，該框架針對網絡安全領域的神經網絡架構搜索（NAS）進行了優化。LV-DQN使用虛擬空間概念來減少神經網絡結構的搜索空間，從而降低計算複雜性並提高學習效率。該框架包括兩個創新優化策略：搜尋中對參數量大的架構進行懲罰壓縮和真實精度補償，旨在發現入侵檢測最佳輕量化的神經架構。我們將LV-DQN應用於多個現實世界的網絡入侵數據集，展示其卓越的性能和實用性。結果顯示，LV-DQN不僅達到了高精度，而且保持了輕量化的模型規模，適合計算資源有限的實時應用。這種方法簡化了繁重的手動神經網絡配置過程，並解決了傳統NAS方法中廣泛評估和高計算需求的挑戰，為動態的網絡安全領域提供了一個實用的解決方案。

摘要(英)

Recent advancements in cybersecurity have highlighted the limitations of traditional intrusion detection systems (IDS) and the potential of deep learning (DL) to enhance detection capabilities. In this paper, we introduce the Lightweight Virtual Deep Q-Network (LV-DQN), a groundbreaking framework for neural architecture search (NAS) tailored for cybersecurity. LV-DQN employs a Virtual Space concept to reduce the search space of neural network structures, minimizing computation complexity and enhancing learning efficiency. The framework includes two innovative optimization strategies: lightweight penalty and real-accuracy compensation, designed to streamline the discovery of optimal, lightweight neural architectures for intrusion detection. We apply LV-DQN to several real-world network intrusion datasets, demonstrating its superior performance and practicality. The results show that LV-DQN not only achieves high accuracy but also maintains a compact model size, making it suitable for real-time applications where computational resources are limited. This approach simplifies the labor-intensive process of manual neural network configuration and addresses the challenges of extensive evaluation and high computational demands in traditional NAS methods, offering a practical solution for the dynamic field of cybersecurity.

關鍵字(中)

★ 網絡安全
★ 網路入侵偵測
★ 神經網路結構搜索
★ 強化學習

關鍵字(英)

★ Cybersecurity
★ Intrusion Detection
★ Neural Architecture Search
★ Reinforcement Learning

論文目次

摘要 i
Abstract ii
誌謝 iii
目錄 iv
List of Figures v
List of Table vi
1. Introduction 1
2. Related Work 5
2.1 Machine Learning and Deep Learning in Cyber Security 5
2.2 Neural Architecture Search 7
2.3 Lightweight & Network pruning 9
3. The Proposed Model: LV-DQN 11
3.1. LV-DQN Predictor 13
3.2 LV-DQN Explorer 16
4. Experiment 20
4.1 Intrusion Detection on Cybersecurity 22
4.2 Performance Comparison on NAS Baselines 24
4.3 Model Construction Time 27
4.4 The Effectiveness of Explorer 29
4.5 The Parameter Setting Discussion 31
5. Conclusion 33
References 34

參考文獻

L. Banoth, M. S. Teja, M. Saicharan, N. Chandra, "A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection," International Journal of Research, 2017.
A. Buczak, E. Guven, "A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection," IEEE Communications Surveys & Tutorials, pp.1153-1176, 2015.
M. Ferrag, L. Maglaras, S. Moschoyiannis, H. Janicke, "Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study," Journal of Information Security and Applications, 2020.
D. S. Berman, A. Buczak, J. S. Chavis, C. Corbett, "A Survey of Deep Learning Methods for Cyber Security," Inf., published 2 April 2019.
M. Roopak, G. Tian, J. Chambers, "Deep Learning Models for Cyber Security in IoT Networks," Computing and Communication Workshop and Conference, 2019.
T. Nguyen, V. Reddi, "Deep Reinforcement Learning for Cyber Security," IEEE Transactions on Neural Networks and Learning Systems, published 13 June 2019.
G. Apruzzese, M. Colajanni, L. Ferretti, A. Guido, M. Marchetti, "On the effectiveness of machine and deep learning for cyber security," Proceedings of the International Conference on Cyber Security and Protection of Digital Services (Cyber Security), 2018.
M. Husák, J. Komárková, E. Bou-Harb, P. Čeleda, "Survey of Attack Projection, Prediction, and Forecasting in Cyber Security," IEEE Communications Surveys & Tutorials, 2019.
I. H. Sarker, Y. B. Abushark, F. Alsolami, A. I. Khan, "IntruDTree: A Machine Learning Based Cyber Security Intrusion Detection Model," Symmetry, 2020.
K. Shaukat, S. Luo, V. Varadharajan, I. Hameed, M. Xu, "A Survey on Machine Learning Techniques for Cyber Security in the Last Decade," IEEE Access, 2020.
I. Rosenberg, A. Shabtai, Y. Elovici, L. Rokach, "Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain," ACM Computing Surveys, 2021.
C. Virmani, T. Choudhary, A. Pillai, M. Rani, "Applications of Machine Learning in Cyber Security," in Handbook of Research on Machine and Deep Learning Applications for Cyber Security, 2020.
K. Kim, M. E. Aminanto, H. C. Tanuwidjaja, "Network Intrusion Detection using Deep Learning," SpringerBriefs in Cyber Security Systems and Networks, 2018.
A. Javaid, Q. Niyaz, W. Sun, M. Alam, "A Deep Learning Approach for Network Intrusion Detection System," EAI Endorsed Transactions on Security and Safety, 2016.
R. Vinayakumar, M. Alazab, I. K. P. S. Senior Member, P. Poornachandran, A. Al-Nemrat, S. Venkatraman, "Deep Learning Approach for Intelligent Intrusion Detection System," IEEE Access, 2019.
L. Dhanabal, S. P. Shantharajah, "A Study on NSL-KDD Dataset for Intrusion Detection System Based on Classification Algorithms", 2015.
R. A. Raza, X. Wang, J. Huang, H. Abbas, Y. He, "Fuzziness based semi-supervised learning approach for intrusion detection system," Information Sciences, 2017.
M.-J. Kang, J.-W. Kang, "Intrusion Detection System Using Deep Neural Network for In-Vehicle Network Security," PLoS ONE, 2016.
W.-C. Lin, S.-W. Ke, C.-F. Tsai, "CANN: An intrusion detection system based on combining cluster centers and nearest neighbors," Knowledge-Based Systems, 2015.
Z. Ahmad, A. Khan, W. Cheah, J. Abdullah, F. Ahmad, "Network intrusion detection system: A systematic study of machine learning and deep learning approaches," Transactions on Emerging Telecommunications Technologies, 2020.
N. Farnaaz, M. A. Jabbar, "Random Forest Modeling for Network Intrusion Detection System," Procedia Computer Science, 2016.
E. Seo, H. M. Song, H. Kim, "GIDS: GAN based Intrusion Detection System for In-Vehicle Network," Proceedings of the Conference on Privacy, Security and Trust, 2018.
D. Floreano, P. Dürr, and C. Mattiussi, “Neuroevolution: from architectures to learning,” Evolutionary Intelligence, 1, pp. 47-62, 2008.
R. Jozefowicz, W. Zaremba, and I. Sutskever, “An Empirical Exploration of Recurrent Network Architectures,” In Proceedings of the 32nd International Conference on International Conference on Machine Learning (ICML’15), pp. 2342–2350, 2015.
L. Xie, and A. Yuille, “Genetic CNN,” In Proceedings of 2017 IEEE International Conference on Computer Vision (ICCV’17), pp. 1388-1397, 2017.
J. Dong, A. Cheng, D. Juan, W. Wei, and M. Sun, “DPP-Net: Device-Aware Progressive Search for Pareto-Optimal Neural Architectures,” In Proceedings of the European Conference on Computer Vision (ECCV’18), pp. 540–555, 2018.
H. Liu, K. Simonyan, O. Vinyals, C. Fernando, and K. Kavukcuoglu, “Hierarchical Representations for Efficient Architecture Search,”. In Proceedings of International Conference on Learning Representations (ICLR’18), 2018.
C. Liu, B. Zoph, M. Neumann, J. Shlens, W. Hua, L.J. Li, L. Fei-Fei, A. Yuille, J. Huang, and K. Murphy, “Progressive Neural Architecture Search,” In Proceedings of the European Conference on Computer Vision (ECCV’18), 2018.
E. Real, A. Aggarwal, Y. Huang, and Q. V. Le, “Regularized Evolution for Image Classifier Architecture Search”, In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI’19), pp. 4780-4789, 2019.
Y. Peng, A. Song, V. Ciesielski, H. Fayek, and X. Chang, “PRE-NAS: Predictor-Assisted Evolutionary Neural Architecture Search,” In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO’22), pp. 1066–1074, 2022.
Y. Chen, “A Progressive Genetic-based Neural Architecture Search,” Industrial Management & Data System, 122 (3), pp. 645-665, 2022.
M. Loni, A. Zoljodi, A. Majd, B. Ahn, M. Daneshtalab, M. Sjodin, H. Esmaeilzadeh, “FastStereoNet: A Fast Neural Architecture Search for Improving the Inference of Disparity Estimation on Resource-Limited Platforms,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52(8), pp. 5222-5234, 2022.
H. Liu, K. Simonyan, and Y. Yang, “DARTS: Differentiable Architecture Search,” In Proceedings of International Conference on Learning Representations (ICLR’19), 2019.
B. Wu, X. Dai, P. Zhang, Y. Wang, F. Sun, Y. Wu, Y. Tian, P. Vajda, Y. Jia, and K. Keutzer, “FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search,” In Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR’19), pp. 10726-10734, 2019.
J. Fang, Y. Sun, Q. Zhang, Y. Li, W. Liu, and X. Wang, “Densely Connected Search Space for More Flexible Neural Architecture Search,” In Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR’20), pp. 10625-10634, 2020.
Y. Wang, Y. Liu, W. Dai, C. Li, J. Zou, and H. Xiong, “Learning Latent Architectural Distribution in Differentiable Neural Architecture Search via Variational Information Maximization,” In Proceedings of 2021 IEEE/CVF International Conference on Computer Vision (ICCV’21), pp. 12292-12301, 2021.
H. Lee, E. Hyung, and S. Hwang, “Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets,” In Proceedings of the 9th International Conference on Learning Representations (ICLR’21), 2021.
P. Ye, B. Li, Y. Li, T. Chen, J. Fan, and W. Ouyang, “β-DARTS: Beta-Decay Regularization for Differentiable Architecture Search,” In Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR’22), pp. 10864-10873, 2022.
M. Zhang, S. Pan, X. Chang, S. Su, J. Hu, G. Haffari, and B. Yang, “BaLeNAS: Differentiable Architecture Search via the Bayesian Learning Rule,” In Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR’22), pp. 11861-11870, 2022.
Y. Ding, Y. Wu, C. Huang, S. Tang, Y. Yang, L. Wei, Y. Zhuang, and Q. Tian, “Learning to Learn by Jointly Optimizing Neural Architecture and Weights,” In Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR’22), pp. 129-138, 2022.
Z. Ding, Y. Chen, N. Li, D. Zhao, “BNAS-v2: Memory-Efficient and Performance-Collapse-Prevented Broad Neural Architecture Search,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52(10), pp. 6259-6272, 2022.
B. Zoph, and Q. Le, “Neural Architecture Search with Reinforcement Learning,” In Proceedings of International Conference on Learning Representations (ICLR’17), 2017.
B. Zoph, V. Vasudevan, J. Shlens, and Q. Le, “Learning Transferable Architectures for Scalable Image Recognition,” In Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR’17), pp. 8697-8710, 2017.
B. Baker, O. Gupta, N. Naik, and R. Raskar, “Designing Neural Network Architectures using Reinforcement Learning,” In Proceedings of the 5th International Conference on Learning Representations (ICLR’17), 2017.
H. Pham, M. Guan, B. Zoph, Q. Le, and J. Dean, “Efficient Neural Architecture Search via Parameters Sharing,” In Proceedings of the 35th International Conference on Machine Learning (ICML’18), pp. 4095–4104, 2018.
Z. Zhong, J. Yan, W. Wu, J. Shao, and C. Liu, “Practical Block-Wise Neural Network Architecture Generation,” In Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR’18), pp. 2423-2432, 2018.
H. Cai, T. Chen, W. Zhang, Y. Yu, and J. Wang, “Efficient Architecture Search by Network Transformation,” In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI’18), 2018.
H. Cai, J. Yang, W. Zhang, S. Han, and Y. Yu, “Path-Level Network Transformation for Efficient Architecture Search,” In Proceedings of the 35th International Conference on Machine Learning (ICML’18), pp. 678–687, 2018.
M. Guo, Z. Zhong, W. Wu, D. Lin and J. Yan, “IRLAS: Inverse Reinforcement Learning for Architecture Search,” In Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR’19), 2019.
M. Tan, and Q. Le, “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks,” In Proceedings of the 36th International Conference on Machine Learning (ICML’19), pp. 6105-6114, 2019.
M. Tan, B. Chen, R. Pang, V. Vasudevan, and Q.V. Le, “MnasNet: Platform-Aware Neural Architecture Search for Mobile,” In Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR’19), pp. 2815-2823, 2019.
H. Cai, L. Zhu, and S. Han, “ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware,” In Proceedings of the 7th International Conference on Learning Representations (ICLR’19), 2019.
Q. Gao, Z. Luo, D. Klabjan, and F. Zhang, “Efficient Architecture Search for Continual Learning,” IEEE Transactions on Neural Networks and Learning Systems, pp. 1-11, 2022.
T. Elsken, J. Metzen, and F. Hutter, “Neural architecture search: A survey,” The Journal of Machine Learning Research, 20 (1), pp. 1997–2017, 2019.
P. Ren, Y. Xiao, X. Chang, P.y. Huang, Z. Li, X. Chen, and X. Wang. “A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions,” ACM Computing Surveys, 54 (4), pp. 1–34, 2021.
E. Son, S. Song, J. Lee, "A lightweight deep-learning radar gesture recognition based on a structured pruning-NAS," Information and Communication Technology Convergence, 2023.
M. Abdelfattah, A. Mehrotra, L. Dudziak, N. Lane, "Zero-Cost Proxies for Lightweight NAS," Proceedings of the International Conference on Learning Representations, 2021.
Y. Li, P. Zhao, G. Yuan, X. Lin, Y. Wang, X. Chen, "Pruning-as-Search: Efficient Neural Architecture Search via Channel Pruning and Structural Reparameterization," Proceedings of the International Joint Conference on Artificial Intelligence, 2022.
W. Kwon, S. Kim, M. W. Mahoney, J. Hassoun, K. Keutzer, A. Gholami, "A Fast Post-Training Pruning Framework for Transformers," Proceedings of the Neural Information Processing Systems, 2022.
M. Ding, X. Lian, L. Yang, P. Wang, X. Jin, Z. Lu, P. Luo, "HR-NAS: Searching Efficient High-Resolution Neural Architectures with Lightweight Transformers," Proceedings of the Computer Vision and Pattern Recognition (CVPR), 2021.
L. Yao, R. Pi, H. Xu, W. Zhang, Z. Li, T. Zhang, "Joint-DetNAS: Upgrade Your Detector with NAS, Pruning and Dynamic Distillation," Proceedings of the Computer Vision and Pattern Recognition(CVPR), 2021.
J. Wang, R. Huang, S. Guo, L. Li, M. Zhu, S. Yang, L. Jiao, "NAS-Guided Lightweight Multiscale Attention Fusion Network for Hyperspectral Image Classification," IEEE Transactions on Geoscience and Remote Sensing, 2021.
M. Risso, A. Burrello, F. Conti, L. Lamberti, Y. Chen, L. Benini, E. Macii, M. Poncino, D. Jahier Pagliari, "Lightweight Neural Architecture Search for Temporal Convolutional Networks at the Edge," IEEE Transactions on Computers, 2023.
X. Dai, D. Chen, M. Liu, Y. Chen, L. Yuan, "DA-NAS: Data Adapted Pruning for Efficient Neural Architecture Search," Proceedings of the European Conference on Computer Vision, 2020.
Y. Li, P. Zhao, G. Yuan, X. Lin, Y. Wang, X. Chen, "Pruning-as-Search: Efficient Neural Architecture Search via Channel Pruning and Structural Reparameterization," Proceedings of the International Joint Conference on Artificial Intelligence, 2022.
X. Luo, D. Liu, H. Kong, S. Huai, H. Chen, W. Liu, "LightNAS: On Lightweight and Scalable Neural Architecture Search for Embedded Platforms," IEEE Transactions on Computers, 2023.
A. Burrello, M. Risso, B. A. Motetti, E. Macii, L. Benini, D. J. Pagliari, "Enhancing Neural Architecture Search with Multiple Hardware Constraints for Deep Learning Model Deployment on Tiny IoT Devices," IEEE Transactions on Emerging Topics in Computing, 2023.
R. Mishra, H. P. Gupta, "Transforming Large-Size to Lightweight Deep Neural Networks for IoT Applications," ACM Computing Surveys, 2023.
J. Carter, S. Mancoridis, E. Galinkin, "Fast, lightweight IoT anomaly detection using feature pruning and PCA," Proceedings of the ACM Symposium on Applied Computing, 2022.
H. Li, X. Yue, C. Zhao, L. Meng, "Lightweight deep neural network from scratch," Applied Intelligence, 2023.
J. Wang, J. Hu, Y. Liu, Z. Hua, S. Hao, Y. Yao, "EL-NAS: Efficient Lightweight Attention Cross-Domain Architecture Search for Hyperspectral Image Classification," Remote Sensing, 2023.
A. M. Garavagno, D. Leonardis, A. Frisoli, "Colab NAS: Obtaining lightweight task-specific convolutional neural networks following Occam′s razor," Future Generations Computer Systems, 2022.
Y. Li, Z. Wu, J. Shen, Q. Zhang, "Real-time 3D shape measurement of dynamic scenes using fringe projection profilometry: lightweight NAS-optimized dual frequency deep learning approach," Optics Express, 2023.
S. Rezvy, Y. Luo, M. Petridis, A. Lasebae, T. Zebin, "An efficient deep learning model for intrusion classification and prediction in 5G and IoT networks," Published in Annual Conference on Computer Science, Engineering, 1 March 2019.

指導教授

陳映濃(Ying-Nong CHEN)

審核日期

2024-7-17

推文