A MODIFIED FIREFLY ALGORITHM-BASED FEATURE SELECTION METHOD AND ARTIFICIAL IMMUNE SYSTEM FOR INTRUSION DETECTION

Melike Günay; Zeynep Orman

doi:10.17482/uumfd.649003

Research Article

A MODIFIED FIREFLY ALGORITHM-BASED FEATURE SELECTION METHOD AND ARTIFICIAL IMMUNE SYSTEM FOR INTRUSION DETECTION

Year 2020, Volume: 25 Issue: 1, 269 - 288, 30.04.2020

Melike Günay Zeynep Orman

https://doi.org/10.17482/uumfd.649003

Cited By: 1

Abstract

Intrusion
detection systems generally produce high dimensional data in network-based
computer systems. It is required to analyze this data effectively and create a
successful model by selecting the important features to save only the
meaningful data and protect the system against suspicious behaviors and attacks
that can occur in a system. Firefly Algorithm (FFA) is one of the most
promising meta-heuristic methods which can be used to select important features
from big data. In this paper, a modified Firefly Algorithm-based feature
selection method is proposed. The traditional Firefly Algorithm is improved by
using the K-Nearest Neighborhood (K-NN) classifier and an additional feature
selection step. The proposed method is tested on 4 different datasets of various
types of attacks. Three different sub-feature sets are obtained for each
dataset and the classification performances are compared. Artificial Immune
System (AIS) method is also implemented to generate artificial data for the
datasets that have an insufficient number of data. This study shows that the
proposed Firefly Algorithm performs successfully to decrease the dimension of
data by selecting the features according to the obtained accuracy rates of the
K-NN method. Memory usage is dramatically decreased over 50% by reducing the
dimension with the proposed FFA. The obtained results indicate that this method
both saves time and memory usage.

Keywords

Firefly Algorithm, Artificial Immune System, K-NN, Feature Selection

References

1. Aranha C., Junior J. P., & Kanoh, H. (2018). Comparative study on discrete SI approaches to the graph coloring problem, Genetic and Evolutionary Computation Conference, Kyoto, Japan, 15-19. doi:10.1145/3205651.3205664
2. Anbu M., & Mala G. S. (2019). Feature selection using firefly algorithm in software defect prediction. Cluster Computing, 22, 10925–10934. doi:10.1007/s10586-017-1235-3
3. Aydilek İ. B. (2018). A hybrid firefly and particle swarm optimization algorithm for computationally expensive numerical problems, Applied Soft Computing, 66, 232-249. doi:10.1016/j.asoc.2018.02.025
4. B Selvakumar., & K Muneeswaran. (2018). Firefly algorithm based feature selection for network intrusion detection. Computers & Security, 81, 148-155. doi:10.1016/j.cose.2018.11.005
5. Er O., Yumusak N., & Temurtas, F. (2012). Diagnosis of chest diseases using artificial immune system, Expert Systems with Applications, 39(2), 1862-1868. doi:10.1016/j.eswa.2011.08.064
6. Eren Y., Küçükdemiral İ., & Üstoğlu İ. (2017). Introduction to Optimization, In Optimization in Renewable Energy Systems, 27-74, Elsevier Butterworth-Heinemann. ISBN:9780081010419, 0081010419
7. Fernandes, D. A., Freire, M. M., Fazendeiro, P., & Inácio, P. R. (2017). Applications of artificial immune systems to computer security: A survey, Journal of Information Security and Applications, 35, 138-159. doi:10.1016/j.jisa.2017.06.007
8. Hui W., Wenjun W., Xinyu Z., Hui S., Jia Z., Xiang Y., & Zhihua C. (2017). Firefly algorithm with neighborhood attraction. Information Sciences, 382-383, 374-387. doi:10.1016/j.ins.2016.12.024
9. Jain L., & Katarya R. (2019). Discover opinion leader in online social network using firefly algorithm, Expert Systems With Applications, 122, 1-15. doi: 10.1016/j.eswa.2018.12.043
10. JR, Q. (1993). C4.5: Programs for Machine Learning. Erişim Adresi: https://github.com/defcom17/NSL _ KDD (Erişim Tarihi: 12.11.2018)
11. Lee W., & Stolfo S. J. (1998). Data Mining Approaches for Intrusion Detection, Proceedings of the 7th USENIX Security Symposium, San Antonio, Texas: Usenix, 1-15. doi:10.5555/1267549.1267555
12. Li Z., Kamlesh M., Lim C. P., & Neoh S. C. (2017). Feature selection using firefly optimization for classification and regression models, Decision Support Systems, 106, 64-85. doi: 10.1016/j.dss.2017.12.001
13. Lunardi W. T., & Voos H. (2018). Comparative study of genetic and discrete firefly algorithm for combinatorial optimization, Proceedings of the 33rd Annual ACM Symposium on Applied Computing, Pau, France, 300-308. doi:10.1145/3167132.3167160
14. Majdouli M. A., Bougrine, S., Rbouh, I., & Imrani, A. A. (2017). A Comparative Study of the EEG Signals Big Optimization problem using evolutionary, swarm and memetic computation algorithms, The Genetic and Evolutionary Computation Conference, Berlin, Germany, 1357-1364. doi:10.1145/3067695.3082489
15. Marie-Sainte, S. L., & Alalyani, N. (2020). Firefly Algorithm based Feature Selection for Arabic Text Classification, Journal of King Saud University- Computer and Information Sciences, 32(3), 320-328. doi:10.1016/j.jksuci.2018.06.004
16. Mashhour E. M., Houby E. M., & Khaled Tawfik Wassif, A. I. (2018). A Novel Classifier based on Firefly Algorithm, Journal of King Saud University – Computer and Information Sciences, In Press, Corrected Proof. doi:10.1016/j.jksuci.2018.11.009
17. Pérez-Delgado, & María-Luisa. (2018). Artificial ants and fireflies can perform colour quantisation, Applied Soft Computing Journal, 73, 153-177. doi:10.1016/j.asoc.2018.08.018
18. Saim B. (2017). Retrieved from Bilal Saim Website: https://bilalsaim.com/ates-bocegialgoritmasi-fafirefly-algorithm-h1635 (Erişim Tarihi: 06.11.2019 )
19. Shang-fu, G., & Zhao, C.-I. (2012). Intrusion Detection System Based on Classification, 2012 IEEE International Conference on Intelligent Control, Automatic Detection and High-End Equipment, Beijing, China, 78-83. doi:10.1109/ICADE.2012.6330103
20. Sukumar J. V., Pranav I., Neetish, M., & Narayanan, J. (2018). Network Intrusion Detection Using Improved Genetic k-means Algorithm, International Conference on Advances in Computing, Communications and Informatics, Bangalore, India, 2441-2446. doi:10.1109/ICACCI.2018.8554710
21. Tariq, Z., Al-Ta'i, M., & Abdulhameed, O. Y. (2013). Features extraction of fingerprints using firefly algorithm, Proceedings of the 6th International Conference on Security of Information and Networks, Aksaray, Turkey, 392-395. doi:10.1145/2523514.2527014
22. Wang, C.-F., & Song, W.-X. (2019). A novel firefly algorithm based on gender difference and its convergence, Applied Soft Computing Journal, 80, 107-124. doi:10.1016/j.asoc.2019.03.010
23. Zhang, Y., Song, X.-f., & Gong, D.-w. (2017). A return-cost-based binary firefly algorithm for feature selection, Information Sciences, 418, 561-574. doi:10.1016/j.ins.2017.08.047

Saldırı Tespiti için Ateş Böceği Algoritması Tabanlı Özellik Seçim Yöntemi ve Yapay Bağışıklık Sistemi

Year 2020, Volume: 25 Issue: 1, 269 - 288, 30.04.2020

Melike Günay Zeynep Orman

https://doi.org/10.17482/uumfd.649003

Cited By: 1

Abstract

Saldırı
tespit sistemleri, genel olarak, ağ-tabanlı bilgisayar sistemlerinde yüksek
boyutlu veri üretmektedir. Sistemi meydana gelebilecek ataklardan ve ağdaki
şüpheli hareketlerden korumak ve sadece anlamlı veriyi saklamak için bu yüksek
boyutlu verinin etkili bir şekilde analiz edilmesi ve başarılı bir model
oluşturulması gerekmektedir. Ateş Böceği Algoritması, büyük veriden önemli
özelliklerin seçilmesi için kullanılan en önemli üst-sezgisel algoritmalardan
biridir. Bu çalışmada, Ateş Böceği Algoritmasına dayalı yeni bir özellik seçme
yöntemi önerilmiştir. Önerdiğimiz bu yöntemde Ateş Böceği Algoritması, K-en
yakın komşuluk algoritması ve ek bir özellik seçimi adımı ile
iyileştirilmiştir. Önerilen yöntem, çeşitli saldırı türlerini içeren dört
farklı veri kümesi ile test edilmiştir. Her veri kümesi için 3 farklı alt
özellik kümesi elde edilmiştir ve her birinin sınıflandırmadaki başarısı
ölçülerek karşılaştırılmıştır. Ayrıca, Yapay Bağışıklık Sistemi yöntemi ile
veri sayısı yetersiz veri kümeleri için yapay veri üretildikten sonra Ateş
Böceği Algoritması uygulanmıştır. Bu çalışma, önerilen Ateş Böceği
Algoritması’nın, K-en yakın komşuluk yöntemi ile elde edilen sınıflandırma
sonuçlarına göre özellikleri seçerek verilerin boyutunu azaltmak için başarılı
bir şekilde çalıştığını göstermektedir. Veri boyutunun azaltılması ile hafıza
kullanımı da %50’den fazla bir oranda azalmıştır. Elde edilen sonuçlar,
önerilen yöntem sayesinde hem zamandan ve hem de hafıza kullanımından tasarruf
edildiğini göstermektedir.

Keywords

Ateş Böceği Algoritması, Yapay Bağışıklık Sistemi, K-NN, Özellik Seçimi

References

1. Aranha C., Junior J. P., & Kanoh, H. (2018). Comparative study on discrete SI approaches to the graph coloring problem, Genetic and Evolutionary Computation Conference, Kyoto, Japan, 15-19. doi:10.1145/3205651.3205664
2. Anbu M., & Mala G. S. (2019). Feature selection using firefly algorithm in software defect prediction. Cluster Computing, 22, 10925–10934. doi:10.1007/s10586-017-1235-3
3. Aydilek İ. B. (2018). A hybrid firefly and particle swarm optimization algorithm for computationally expensive numerical problems, Applied Soft Computing, 66, 232-249. doi:10.1016/j.asoc.2018.02.025
4. B Selvakumar., & K Muneeswaran. (2018). Firefly algorithm based feature selection for network intrusion detection. Computers & Security, 81, 148-155. doi:10.1016/j.cose.2018.11.005
5. Er O., Yumusak N., & Temurtas, F. (2012). Diagnosis of chest diseases using artificial immune system, Expert Systems with Applications, 39(2), 1862-1868. doi:10.1016/j.eswa.2011.08.064
6. Eren Y., Küçükdemiral İ., & Üstoğlu İ. (2017). Introduction to Optimization, In Optimization in Renewable Energy Systems, 27-74, Elsevier Butterworth-Heinemann. ISBN:9780081010419, 0081010419
7. Fernandes, D. A., Freire, M. M., Fazendeiro, P., & Inácio, P. R. (2017). Applications of artificial immune systems to computer security: A survey, Journal of Information Security and Applications, 35, 138-159. doi:10.1016/j.jisa.2017.06.007
8. Hui W., Wenjun W., Xinyu Z., Hui S., Jia Z., Xiang Y., & Zhihua C. (2017). Firefly algorithm with neighborhood attraction. Information Sciences, 382-383, 374-387. doi:10.1016/j.ins.2016.12.024
9. Jain L., & Katarya R. (2019). Discover opinion leader in online social network using firefly algorithm, Expert Systems With Applications, 122, 1-15. doi: 10.1016/j.eswa.2018.12.043
10. JR, Q. (1993). C4.5: Programs for Machine Learning. Erişim Adresi: https://github.com/defcom17/NSL _ KDD (Erişim Tarihi: 12.11.2018)
11. Lee W., & Stolfo S. J. (1998). Data Mining Approaches for Intrusion Detection, Proceedings of the 7th USENIX Security Symposium, San Antonio, Texas: Usenix, 1-15. doi:10.5555/1267549.1267555
12. Li Z., Kamlesh M., Lim C. P., & Neoh S. C. (2017). Feature selection using firefly optimization for classification and regression models, Decision Support Systems, 106, 64-85. doi: 10.1016/j.dss.2017.12.001
13. Lunardi W. T., & Voos H. (2018). Comparative study of genetic and discrete firefly algorithm for combinatorial optimization, Proceedings of the 33rd Annual ACM Symposium on Applied Computing, Pau, France, 300-308. doi:10.1145/3167132.3167160
14. Majdouli M. A., Bougrine, S., Rbouh, I., & Imrani, A. A. (2017). A Comparative Study of the EEG Signals Big Optimization problem using evolutionary, swarm and memetic computation algorithms, The Genetic and Evolutionary Computation Conference, Berlin, Germany, 1357-1364. doi:10.1145/3067695.3082489
15. Marie-Sainte, S. L., & Alalyani, N. (2020). Firefly Algorithm based Feature Selection for Arabic Text Classification, Journal of King Saud University- Computer and Information Sciences, 32(3), 320-328. doi:10.1016/j.jksuci.2018.06.004
16. Mashhour E. M., Houby E. M., & Khaled Tawfik Wassif, A. I. (2018). A Novel Classifier based on Firefly Algorithm, Journal of King Saud University – Computer and Information Sciences, In Press, Corrected Proof. doi:10.1016/j.jksuci.2018.11.009
17. Pérez-Delgado, & María-Luisa. (2018). Artificial ants and fireflies can perform colour quantisation, Applied Soft Computing Journal, 73, 153-177. doi:10.1016/j.asoc.2018.08.018
18. Saim B. (2017). Retrieved from Bilal Saim Website: https://bilalsaim.com/ates-bocegialgoritmasi-fafirefly-algorithm-h1635 (Erişim Tarihi: 06.11.2019 )
19. Shang-fu, G., & Zhao, C.-I. (2012). Intrusion Detection System Based on Classification, 2012 IEEE International Conference on Intelligent Control, Automatic Detection and High-End Equipment, Beijing, China, 78-83. doi:10.1109/ICADE.2012.6330103
20. Sukumar J. V., Pranav I., Neetish, M., & Narayanan, J. (2018). Network Intrusion Detection Using Improved Genetic k-means Algorithm, International Conference on Advances in Computing, Communications and Informatics, Bangalore, India, 2441-2446. doi:10.1109/ICACCI.2018.8554710
21. Tariq, Z., Al-Ta'i, M., & Abdulhameed, O. Y. (2013). Features extraction of fingerprints using firefly algorithm, Proceedings of the 6th International Conference on Security of Information and Networks, Aksaray, Turkey, 392-395. doi:10.1145/2523514.2527014
22. Wang, C.-F., & Song, W.-X. (2019). A novel firefly algorithm based on gender difference and its convergence, Applied Soft Computing Journal, 80, 107-124. doi:10.1016/j.asoc.2019.03.010
23. Zhang, Y., Song, X.-f., & Gong, D.-w. (2017). A return-cost-based binary firefly algorithm for feature selection, Information Sciences, 418, 561-574. doi:10.1016/j.ins.2017.08.047

There are 23 citations in total.

Details

Primary Language	English
Subjects	Artificial Intelligence
Journal Section	Research Articles
Authors	Melike Günay 0000-0001-9135-3204 Zeynep Orman 0000-0002-0205-4198
Publication Date	April 30, 2020
Submission Date	November 20, 2019
Acceptance Date	March 27, 2020
Published in Issue	Year 2020 Volume: 25 Issue: 1

Cite

APA	Günay, M., & Orman, Z. (2020). A MODIFIED FIREFLY ALGORITHM-BASED FEATURE SELECTION METHOD AND ARTIFICIAL IMMUNE SYSTEM FOR INTRUSION DETECTION. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, 25(1), 269-288. https://doi.org/10.17482/uumfd.649003
AMA	Günay M, Orman Z. A MODIFIED FIREFLY ALGORITHM-BASED FEATURE SELECTION METHOD AND ARTIFICIAL IMMUNE SYSTEM FOR INTRUSION DETECTION. UUJFE. April 2020;25(1):269-288. doi:10.17482/uumfd.649003
Chicago	Günay, Melike, and Zeynep Orman. “A MODIFIED FIREFLY ALGORITHM-BASED FEATURE SELECTION METHOD AND ARTIFICIAL IMMUNE SYSTEM FOR INTRUSION DETECTION”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 25, no. 1 (April 2020): 269-88. https://doi.org/10.17482/uumfd.649003.
EndNote	Günay M, Orman Z (April 1, 2020) A MODIFIED FIREFLY ALGORITHM-BASED FEATURE SELECTION METHOD AND ARTIFICIAL IMMUNE SYSTEM FOR INTRUSION DETECTION. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 25 1 269–288.
IEEE	M. Günay and Z. Orman, “A MODIFIED FIREFLY ALGORITHM-BASED FEATURE SELECTION METHOD AND ARTIFICIAL IMMUNE SYSTEM FOR INTRUSION DETECTION”, UUJFE, vol. 25, no. 1, pp. 269–288, 2020, doi: 10.17482/uumfd.649003.
ISNAD	Günay, Melike - Orman, Zeynep. “A MODIFIED FIREFLY ALGORITHM-BASED FEATURE SELECTION METHOD AND ARTIFICIAL IMMUNE SYSTEM FOR INTRUSION DETECTION”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 25/1 (April 2020), 269-288. https://doi.org/10.17482/uumfd.649003.
JAMA	Günay M, Orman Z. A MODIFIED FIREFLY ALGORITHM-BASED FEATURE SELECTION METHOD AND ARTIFICIAL IMMUNE SYSTEM FOR INTRUSION DETECTION. UUJFE. 2020;25:269–288.
MLA	Günay, Melike and Zeynep Orman. “A MODIFIED FIREFLY ALGORITHM-BASED FEATURE SELECTION METHOD AND ARTIFICIAL IMMUNE SYSTEM FOR INTRUSION DETECTION”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, vol. 25, no. 1, 2020, pp. 269-88, doi:10.17482/uumfd.649003.
Vancouver	Günay M, Orman Z. A MODIFIED FIREFLY ALGORITHM-BASED FEATURE SELECTION METHOD AND ARTIFICIAL IMMUNE SYSTEM FOR INTRUSION DETECTION. UUJFE. 2020;25(1):269-88.

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