Investigate Discriminative AutoEncoder in Few-shot Learning-based Anomaly Detection
Abstract
Discriminative AutoEncoder (DisAE) plays a crucial role in enhancing the adaptability and gener- alization of few-shot learning methods (DisAEFL) for detecting rare anomalies. DisAE captures meta- knowledge from multiple known tasks, facilitating rapid adaptation in DisAEFL. Key factors like the discriminative parameter (a) and normal proportion (pn) significantly impact DisAEFL performance. However, their influence on the DisAE manifold and DisAEFL’s efficacy in rare cyberattack detection remain understudied in cybersecurity. This study presents an investigative approach to probe DisAE’s influence on DisAEFL’s performance in addressing rare, unseen cyberattacks, aiming to gain insight into the DisAE manifold and outline future research directions. Through intensive analysis, we focus on parameters a and pn, detailing how to examine them to observe DisAE’s effects on DisAEFL. Two main experiments are conducted to investigate their influences. Experimental results on the NSL-KDD dataset reveal a strong correlation between these parameters and both the DisAE manifold and DisAEFL performance. These findings suggest strategies for more efficiently constructing the DisAE manifold to enhance DisAEFL’s adaptability and generalization. Overall, this study contributes to advancing anomaly detection methodologies in cybersecurity by shedding light on the interplay between DisAE, DisAEFL, and crucial parameters.
References
T. Nishio, M. Nakahara, N. Okui, A. Kubota, Y. Kobayashi, K. Sugiyama, and R. Shinkuma, “Anomaly traffic detection with federated learning toward network-based malware detection in IoT,” in GLOBECOM 2022 - 2022 IEEE Global Communications Conference, 2022, pp. 299–304.
G. Fernandes, J. J. Rodrigues, L. F. Carvalho, J. F. Al-Muhtadi, and M. L. Proenc ̧a, “A comprehensive survey on network anomaly detection,” Telecommunication Systems, vol. 70, pp. 447–489, 2019.
S. Liu, B. Cui, and W. Hou, “A survey on blockchain abnormal transaction detection,” in International Conference on Blockchain and Trustworthy Systems. Springer, 2023, pp. 211–225.
M. Evangelou and N. M. Adams, “An anomaly detection framework for cyber-security data,” Computers & Security, vol. 97, p. 101941, 2020.
D. Kumar, C. Verma, Z. Illes, A. Mittal, B. Bakariya, and S. Goyal, “Anomaly detection in chest X-Ray images using variational autoencoder,” in 2023 6th International Conference on Contemporary Computing and Informatics (IC3I), vol. 6, 2023, pp. 216–221.
M. Hosseini and W. Shi, “Intrusion detection in iot network using few-shot class incremental learning,” in Future of Information and Communication Conference. Springer, 2024, pp. 617–636.
X. Zhang, Q. Wang, M. Qin, Y. Wang, T. Ohtsuki, B. Adebisi, H. Sari, and G. Gui, “Enhanced few-shot malware traffic classification via integrating knowledge transfer with neural architecture search,” IEEE Transactions on Information Forensics and Security, vol. 19, pp. 5245–5256, 2024.
A. Yang, C. Lu, J. Li, X. Huang, T. Ji, X. Li, and Y. Sheng, “Application of meta-learning in cyberspace security: A survey,” Digital Communications and Networks, vol. 9, no. 1, pp. 67–78, 2023.
S. D. A. Rihan, M. Anbar, and B. A. Alabsi, “Meta-learner-based approach for detecting attacks on Internet of Things networks,” Sensors, vol. 23, no. 19, p. 8191, 2023.
Y. Yan, Y. Yang, F. Shen, M. Gao, and Y. Gu, “Meta learning-based few-shot intrusion detection for 5g-enabled industrial internet,” Complex & Intelligent Systems, pp. 1–20, 2024.
D. B. Maudsley, “A theory of meta-learning and principles of facilitation: An organismic perspective.” 1980.
C. Finn, P. Abbeel, and S. Levine, “Model-agnostic meta-learning for fast adaptation of deep networks,” in International
conference on machine learning. PMLR, 2017, pp. 1126–1135.
E. Triantafillou, H. Larochelle, J. Snell, J. Tenenbaum, K. J. Swersky, M. Ren, R. Zemel, and S. Ravi, “Meta-learning for
semi-supervised few-shot classification,” in International Conference on Learning Representations, 2018.
J. Snell, K. Swersky, and R. Zemel, “Prototypical networks for few-shot learning,” Advances in neural information
processing systems, vol. 30, 2017.
O. Vinyals, C. Blundell, T. Lillicrap, D. Wierstra et al., “Matching networks for one shot learning,” Advances in neural
information processing systems, vol. 29, 2016.
V. L. Cao, M. T. Nguyen, and T. D. Le Dinh, “Few-shot learning with discriminative representation for cyberattack
detection,” in 2023 15th International Conference on Knowledge and Systems Engineering (KSE), 2023, pp. 1–6.
A.S.Iliyasu,U.A.Abdurrahman,andL.Zheng,“Few-shotnetworkintrusiondetectionusingdiscriminativerepresentation
learning with supervised autoencoder,” Applied Sciences, vol. 12, no. 5, p. 2351, 2022.
S. Razakarivony and F. Jurie, “Discriminative autoencoders for small targets detection,” in 2014 22nd International
conference on pattern recognition. IEEE, 2014, pp. 3528–3533.
C. Xu, J. Shen, and X. Du, “A method of few-shot network intrusion detection based on meta-learning framework,” TIFS,
vol. 15, pp. 3540–3552, 2020.
S. Ravi and H. Larochelle, “Optimization as a model for few-shot learning,” in International conference on learning representations, 2016.
H. Bourlard and Y. Kamp, “Auto-association by multilayer perceptrons and singular value decomposition,” Biological cybernetics, vol. 59, no. 4, pp. 291–294, 1988.
Y. Yu and N. Bian, “An intrusion detection method using few-shot learning,” IEEE Access, vol. 8, pp. 49730–49740, 2020.
C. Lu, X. Wang, A. Yang, Y. Liu, and Z. Dong, “A few-shot-based model-agnostic meta-learning for intrusion detection in security of internet of things,” IEEE Internet of Things Journal, vol. 10, no. 24, pp. 21 309–21 321, 2023.
[
F.Rustam,A.Raza,M.Qasim,S.Posa,andA.Jurcut,“Anovelapproachforreal-timeserver-basedattackdetectionusing meta-learning,” IEEE Access, vol. PP, 03 2024.
T.Ye,G.Li,I.Ahmad,C.Zhang,X.Lin,andJ.Li,“Flag:Few-shotlatentdirichletgenerativelearningforsemantic-aware traffic detection,” IEEE Transactions on Network and Service Management, vol. 19, no. 1, pp. 73–88, 2022.
J.He,L.Yao,X.Li,M.Khan,W.Niu,X.Zhang,andF.Li,“Model-agnosticgeneration-enhancedtechnologyforfew-shot intrusion detection,” Applied Intelligence, pp. 1–24, 02 2024.
K. Ding, Q. Zhou, H. Tong, and H. Liu, “Few-shot network anomaly detection via cross-network meta-learning,” 2021.
J.Moon,Y.Noh,S.Jung,J.Lee,andE.Hwang,“Anomalydetectionusingamodel-agnosticmeta-learning-basedvariational
auto-encoder for facility management,” Journal of Building Engineering, vol. 68, p. 106099, 2023.
V. L. Cao, M. Nicolau, and J. McDermott, “Learning neural representations for network anomaly detection,” IEEE
Transactions on Cybernetics, vol. 49, no. 8, pp. 3074–3087, 2019.
S. Revathi and A. Malathi, “A detailed analysis on nsl-kdd dataset using various machine learning techniques for intrusion
detection,” International Journal of Engineering Research & Technology (IJERT), vol. 2, no. 12, pp. 1848–1853, 2013.
M. Tavallaee, E. Bagheri, W. Lu, and A. A. Ghorbani, “A detailed analysis of the kdd cup 99 data set,” in 2009 IEEE
symposium on computational intelligence for security and defense applications. Ieee, 2009, pp. 1–6.
D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning internal representations by error propagation, parallel distributed processing, explorations in the microstructure of cognition, ed. de rumelhart and j. mcclelland. vol. 1. 1986,”
Biometrika, vol. 71, pp. 599–607, 1986.
J.Duchi,E.Hazan,andY.Singer,“Adaptivesubgradientmethodsforonlinelearningandstochasticoptimization.”Journal
of machine learning research, vol. 12, no. 7, 2011.
DOI: http://dx.doi.org/10.21553/rev-jec.375
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