机器学习与数据科学最新进展：成就与研究指南

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《机器学习与数据科学的最新进展与研究指南》（Advances in Machine Learning and Data Science: Recent Achievements and Research Directives）是"智能系统与计算"系列书籍的第705卷，由Damodar Reddy Edla、Pawan Lingras和Venkatanareshbabu K.共同编辑。该系列由Janusz Kacprzyk担任系列编辑，他是波兰科学院华沙分部的研究员，电子邮箱为kacprzyk@ibspan.waw.pl。这本书涵盖了广泛的智能系统和计算领域，旨在提供理论、应用和智能系统及计算设计方法的深度探讨。书中主题包括但不限于： 1. 计算智能：深入研究了人工智能的核心概念和技术，如神经网络、模糊系统、进化计算等，这些方法在处理复杂问题和模拟人类决策方面发挥着关键作用。 2. 软计算融合：探讨了不同智能方法如何相互融合，以增强系统的适应性和泛化能力，如将模糊逻辑与遗传算法相结合。 3. 社会智能与环境感知：研究了如何使智能系统更好地理解和适应社会环境，以及它们在物联网、智能家居和虚拟现实中的应用。 4. 计算神经科学与人工生命：结合生物学原理，探索如何构建具有学习和适应性的机器模型，以及如何通过模拟生物过程来解决实际问题。 5. 感知与认知科学：书中还涵盖了感知技术，如计算机视觉和自然语言处理，以及认知科学的基本原理，这些技术对于开发能理解用户意图的人机交互至关重要。 6. 商业、经济与环境应用：讨论了机器学习和数据科学在各个行业的应用，如金融预测、市场分析和可持续发展策略中的角色。《机器学习与数据科学的最新进展与研究指南》不仅提供了最新的研究成果，还提出了未来研究的方向和指导原则，对于从事该领域的研究人员、工程师和学生来说，是一本不可或缺的参考书。同时，该PDF版本经过FoxitReader、PDF-XChangeViewer、SumatraPDF和Firefox等软件验证兼容性良好，适合学术交流和专业学习使用。如果在使用过程中遇到版权问题，请及时与上传者或CSDN联系处理。

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3 Proposed Method

The proposed PO-ART1 intrusion detection system has four main phases shown in

Fig. 1. Selection of this parameter is important for the performance enhancement of

the ART1 so as to efﬁciently discover data based on similarity measures. Once the

optimum vigilance is obtained, the data is classiﬁed using ART1 and testing is

performed.

Figure 2 shows the randomly generated population is fed into particle swarm

optimization algorithm where position of every particle represents the value of

vigilance parameter, and using PSO and updating Eq. (1a) and (1b), optimum

vigilance parameter is optimized. The ART1 is trained using the internal training

strategy, and vigilance for each iteration is applied to evaluate the minimum clas-

siﬁcation error.

If the vigilance parameter is obtained, then this opti mized vigilance parameter is

used in training ART1 which categorizes the given data into anomalous data and

normal data. Figure 3 shows the architecture of a novel methodology PO-ART1 for

network IDS. Stepwise architecture is shown where initially features as input pat-

terns are provided to ART1, and simultaneously randomly generated particles are

fed as input to PSO algorithm. Next classiﬁcation error is evaluated, and ART1 is

optimized using PSO. In order to ﬁnd clusters, following classiﬁcation error E is

used as E =

, where Ic is correctly classiﬁed and Tc is the total number of patterns.

Finally, output is obtained using optimized ART1.

KDD

Training

Data

Preprocessing

Vigilance

OpƟmizaƟon

Using PSO

ClassiﬁcaƟon

ART1

Testing

Fig. 1 Block diagram for the intrusion detection system

OpƟmal

Vigilance

Evaluate

ART

Apply

PSO

Random

PopulaƟon

Is vigilance

Optimum?

Train

ART1

Fig. 2 Selection of vigilance parameter

Optimization of Adaptive Resonance Theory Neural Network … 3

5 Conclusion

Intrusion detection using an unsupervised neural network ART1 is proposed which

is optimized using particle swarm optimization. Compared with the results of

conventional ART1 in IDS, experimental results show that accuracy of 98.8% with

alarm rate (false) 0.2%. Also the algorithm has very low time and space complexity,

is simple and has no complex and difﬁcult computation.

References

1. Eldos, T.M., Almazyad, A.S.: Adaptive resonance theory training parameters: pretty good

sets. J. Comput. Sci. 6, 1443–1449 (2010). https://doi.org/10.3844/jcssp.2010.1438.1444

2. Prothives, K., Srinoy, S.: Member, IAENG. “Integrating ART and rough set approach for

computer security” In: Proceedings of the International Multi Conference of Engineers and

Computer Scientists, IMECS 2009, vol. I. Hong Kong, March 18–20 (2009)

3. Granger, E., Henniges, P., Sabourin, R., Oliveira, L.S.: Supervised learning of fuzzy artmap

neural networks through particle swarm optimization. J. Pattern Recogn. Res. 2 (1), 27–60

(2007)

4. Al-Natsheh, H.T., Eldos, T.M.: Performance optimization of adaptive resonance neural

networks using genetic algorithms. In: Foundations of computational intelligence, 2007. IEEE

Symposium, 143–148 (2007)

5. Amini, M., Jalili, R., Shahriari, H.R.: RT-UNNID: a practical solution to real-time

network-based intrusion detection using unsupervised neural networks. Elsevier Comput.

Secur. J. 25(6) (2006)

6. Amini, M., Jalili, R.: Network-based intrusion detection using unsupervised adaptive

resonance theory (ART). In: Proceeding s of the Fourth Conference on Engineering of

Intelligent Systems (EIS 2004). Madeira, Portugal (2004)

7. Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of the IEEE

International Joint Conference on Neural Networks, p. 1942e1948 (1995)

8. Carpenter, G.A., Grossberg, S.: Adaptive resonance theory. In: Encyclopedia of Machine

Learning, Springer US, 22–35 (2010)

9. KDD’99 dataset, University of California, Irvine (1999). http://kdd.ics.uci.edu/databases/

kddcup99/kddcup99.html

10. Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A.A.: A detailed analysis of the kddcup 99 data

set (2009) http://nsl.cs.unb.ca/NSL-KDD/

Optimization of Adaptive Resonance Theory Neural Network … 7

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