Use Dimensionality Reduction and SVM Methods to Increase the Penetration Rate of Computer Networks


Amir Moradibaad,Ramin Jalilian Mashhoud,



intrusion detection rate,computer networks,SVM,


In the world today computer networks have a very important position and most of the urban and national infrastructure as well as organizations are managed by computer networks, therefore, the security of these systems against the planned attacks is of great importance. Therefore, researchers have been trying to find these vulnerabilities so that after identifying ways to penetrate the system, they will provide system protection through preventive or countermeasures. SVM is considered as one of the major algorithms for intrusion detection. One of the major problems is the time of training and the need to improve its efficiency when it comes to work with large dimensions. In this research, we try to study a variety of malware and methods of intrusion detection, provide an efficient method for detecting attacks and utilizing dimension reduction. Thus, we will be able to detect attacks by carefully combining these two algorithms and pre-processes that are performed before the two on the input data. The main question raised in this study is how we can identify attacks on computer networks with the above-mentioned method. In anomalies diagnostic method, by identifying behavior as a normal behavior for the user, the host, or the whole system, any deviation from this behavior is considered as an abnormal behavior, which can be a potential occurrence of an attack. In this research, the network intrusion detection system is used by anomaly detection method that uses the SVM algorithm for classification and SVD to reduce the size. The various steps of the proposed method include pre-processing of the data set, feature selection, support vector machine, and evaluation. The NSL-KDD data set has been used to teach and test the proposed model. In this study, we inferred the intrusion detection using the SVM algorithm for classification and SVD for diminishing dimensions with no classification algorithm. And also the KNN algorithm has been compared in situations with and without diminishing dimensions and the results have shown that the proposed method has a better performance than comparable methods.


I. Alesh Kumar Sharma, Satyam Maheswari. Network Intrusion detection by
using PCA via SMO-SVM. International Journal of Advanced Research in
Computer Science and Electronics Engineering (IJARCSEE). Volume 1,
Issue 10, 2012.
II. Anke Meyer-Baese and Volker Schmid. Feature Selection and Extraction, In
Pattern Recognition and Signal Analysis in Medical Imaging (Second
Edition), edited by Anke Meyer-Baese and Volker Schmid, Academic Press,
Oxford, Pages 21-69, ISBN 9780124095458, 2014.
III. Azencott, Robert, et al. “Automatic clustering in large sets of time
series.” Contributions to Partial Differential Equations and Applications.
Springer, Cham, 65-75, 2019.

IV. Baghban, Alireza, et al. “Application of MLP-ANN as novel tool for
estimation of effect of inhibitors on asphaltene precipitation
reduction.” Petroleum Science and Technology.1-6, 2018.
V. Gao, Junbin, Qinfeng Shi, and Tibero S. Caetano. “ Dimensionality reduction
via compressive sensing,” Pattern Recognition Letters 33.9,1163-1170, 2012.
VI. Gunupudi Rajesh Kumar, Nimmala Mangathayaru and Gugulothu Narsimha.
A feature clustering based Dimensionality reduction for intrusion Detection
(FCBDR). IADIS International Journal on Computer Science and
Information Systems. 12(1), 26-44, 2017.
VII. H. Om and A. Kunda, “A Hybrid System For Reducing the False Alarm Rate
of Anomaly Intrusion Detection System”, in International Conference on
Recent Advances in Information Technology (RAIT), Dhanbad, 2012.
VIII. Hekmati, R., Azencott, R., Zhang, W., Paldino, M. “Localization of Epileptic
Seizure Focus by Computerized Analysis of fMRI Recordings”.arXiv, 2018.
IX. Hekmati, R., et al. “Machine Learning to Evaluate fMRI Recordings of Brain
Activity in Epileptic Patients, 2015.
X. Hekmati, Rasoul. “On efficiency of non-monotone adaptive trust region and
scaled trust region methods in solving nonlinear systems of
equations.” Biquarterly Control and Optimization in applied Mathematics 1.1,
31-40, 2016.
XI. Hyunsoo Kim, Peg Howland and Haesun Park.Dimension Reduction in
Text Classification with Support Vector Machines. The Journal of Machine
Learning Research archive. Volume 6, 12/1/2005. Pages 37-53, 2005.
XII. I. Ahmad, M. Hussain, A. Alghamdi, A. Alelaiwi, “Enhancing SVM
Performance In Intrusion Detection Using Optimal Feature Subset Selection
Based on Genetic Principal Components”, Neural Computing and
Applications, vol. 24, no. 7-8, pp. 1671-1682, 2014.
XIII. J.Shen and S. Mousavi, ”Least sparsity of p-norm based optimization
problems with p>1, ” arXiv preprint arXiv:1708.06055, 2017.
XIV. Li Y, Qiu R, Jing S. Intrusion detection system using Online Sequence
Extreme Learning Machine (OS-ELM) in advanced metering infrastructure of
smart grid. PLoSONE 13(2), 66-79, 2018.
XV. Luxburg U. V., Bousquet O., “Distance–based classification with Lipschitz
functions”, Journal of Machine Learning Research, Vol. 5, pp. 669-695, 2004.
XVI. M. Hasan, M. Nasser, B. Pal, “Support Vector Machine and Random Forest
Modeling for Intrusion Detection System (IDS)”, Journal of Intelligent
Learning Systems and Applications, vol. 6, no. 1, 2014.
XVII. M. Tavallaee, E. Bagheri, W. Lu, and A. A. Ghorbani, “A Detailed Analysis
of the KDD CUP 99 Data Set,” in Proceeding of the 2009 IEEE symposium
on computational Intelligence in security and defense application (CISDA),
XVIII. Mingyu Fan, Nannan Gu, Hong Qiao, Bo Zhang, Dimensionality reduction:
An interpretation from manifold regularization perspective, Information
Sciences, Volume 277, 1, 694-714, ISSN 0020-0255, 2014.

XIX. N. Revathy and R. Balasubramanian, “GA-SVM wrapper approach for gene
ranking and classification using expressions of very few genes,” Journal of
Theoretical and Applied Information Technology, vol. 40, no. 2, pp. 113–119,
XX. Najarian, M., et al. “Evolutionary Vertical Size Reduction: A Novel
Approach for Big Data Computing”. International Journal of Mathematics
and its Applications, 2018.
XXI. NSL-KDD data set for network-based intrusion detection systems.” Available
on:, 2009.
XXII. R. Lippmann, J. Haines, D. Fried, J. Korba, and K. Das, “The 1999 DARPA
off-line intrusion detection evaluation,” Computer Networks, 34, pp.579-595,
XXIII. R. Ravinder Reddy; Y Ramadevi ; K. V. N Sunitha. Effective discriminant
function for intrusion detection using SVM. 2016 International Conference
on Advances in Computing, Communications and Informatics (ICACCI).
DOI: 10.1109/ICACCI.2016.7732199, 2016.
XXIV. S. Ahmadian, H Malki, AR Sadat , “Modeling Time of Use Pricing for Load
Aggregators Using New Mathematical Programming with Equality
Constraints”, 5th International Conference on Control, Decision, 2018.
XXV. S. J. Stolfo, W. Fan, A. Prodromidis, P. K. Chan, W. Lee, “Cost-sensitive
modeling for fraud and intrusion detection: Results from the JAM project”, in
Proceedings of the 2000 DARPA Information Survivability Conference and
Exposition, 2000.
XXVI. S. Maldonado, R. Weber, and J. Basak, “Simultaneous feature selection and
classification using kernel-penalized support vector machines,” Information
Sciences, vol. 181, no. 1, pp. 115–128, 2011.
XXVII. Sebastián Maldonado, Juan Pérez, Richard Weber, Martine Labbé, Feature
selection for Support Vector Machines via Mixed Integer Linear
Programming, Information Sciences, Volume 279, 20, Pages 163-175, 2014.
XXVIII. Vinodhini G., Chandrasekaran R.M. Sentiment Mining Using SVM-Based
Hybrid Classification Model. In: Krishnan G., Anitha R., Lekshmi R., Kumar
M., Bonato A., Graña M. (eds) Computational Intelligence, Cyber Security
and Computational Models. Advances in Intelligent Systems and Computing,
vol 246, 2014.
XXIX. Vinodhini G., Chandrasekaran R.M. Sentiment Mining Using SVM-Based
Hybrid Classification Model. In: Krishnan G., Anitha R., Lekshmi R., Kumar
M., Bonato A., Graña M. (eds) Computational Intelligence, Cyber Security
and Computational Models. Advances in Intelligent Systems and Computing,
vol 246. Springer, New Delhi, 2014.
XXX. Xintao Qiu, Dongmei Fu and Zhenduo Fu.An Efficient Dimensionality
Reduction Approach for Small-sample Size and High-dimensional Data
Modeling. journal of computers, vol. 9, no. 3, march, 2014.
XXXI. Zena M. Hira and Duncan F. Gillies (2015). A Review of Feature Selection
and Feature Extraction Methods Applied on Microarray Data. Advances in
Bioinformatics, 2015.

View Download