Abstract
An intrusion detection system works to recognize the attacks using either the signature or signature-less method. The signature-less method suffers from a lot of false alarms that affect accuracy and recall. Commonly used IDS (intrusion detection system) Dataset experiences imbalance which causes a high false alarms rate. Nowadays CNN (convolution neural network) excels in image and computer vision. Using CNN in IDS is promising. The paper proposes a hybrid approach between CNN and ML (SVM, KNN). CNN is efficiently utilized to get important features from the dataset. Then ML used to classify the data. Using the hybrid approaches to benefit from the advantage of machine learning (high accuracy, Low false alarms) and Deep learning which deal with a large amount of data and reduce the number of feature of the dataset (feature extraction). In this paper we used 10% of KDDcup1999 dataset. The experimental results showed enhancement in the detection accuracy to 99.3 and reduction in losses to 0.03.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Milan, H.S., Singh, K.: Reducing false alarms in intrusion detection systems – a survey. Int. Res. J. Eng. Technol. (IRJET) 05(02), 9–12 (2018)
Abdullah, B., Abd-Alghafar, I., Salama, G.I., Abd-Alhafez, A.: Performance evaluation of a genetic algorithm based approach to network intrusion detection system. In: 13th International Conference on Aerospace Sciences and Aviation Technology (ASAT), 26–28 May 2009 (2009)
Ashoor, A.S., Gore, S.: Importance of intrusion detection system (IDS). Int. J. Sci. Eng. Res. 2(1), 1–4 (2011)
Modi, C.N., Acha, K.: Virtualization layer security challenges and intrusion detection/prevention systems in cloud computing: a comprehensive review. J. Supercomput. 73(3), 1–43 (2016)
Louridas, P., Ebert, C.: Machine learning. IEEE Softw. 33(5), 110–115 (2016)
Jordan, M.I., Mitchell, T.M.: Machine learning: trends, perspectives, and prospects. Science 349(6245), 255–260 (2015)
Lecun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Xin, Y., Kong, L., Liu, Z.: Machine learning and deep learning methods for cyber security. IEEE Access 1–9 (2017)
Coelho, I.M., Coelho, V.N., Luz, E.J.D.: A GPU deep learning metaheuristic based model for time series forecasting. Elsevier 201(1), 412–418 (2017)
Deng, L., Yu, D.: Deep learning: methods and applications. Found Trends® Signal Process 7(3), 197–387 (2014)
Vinayakumar, R., Soman, K.P.: Applying convolutional neural network for network intrusion detection. In: International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 1222–1228 (2017)
Kapoor, A.J., Fan, H.: Intelligent detection using convolutional neural network (ID-CNN). In: Earth and Environmental Science, pp. 1–10 (2019)
Liu, H., Lang, B.: Machine learning and deep learning methods for intrusion detection systems: a survey. In: Applied Sciences, PP. 1–28 (2019)
Patgiri, R., Akutota, T.: An investigation on intrusion detection system using machine learning. In: IEEE Symposium Series on Computational Intelligence SSCI, pp. 1684–1691 (2018)
Shon, T., Kim, Y., Lee, C., Moon, J.: A machine learning framework for network anomaly detection using SVM and GA. In: Proceedings of the IEEE, pp. 176–183 (2005)
Liao, Y., Vemuri, R.V.: Use of k-nearest neighbor classifier for intrusion detection. In: ICACCI, pp. 1–10 (2016)
Shirazi, H.M.: Anomaly intrusion detection using information theory, k-NN and KMC algorithms. Aust. J. Basic Appl. Sci. 3(3), 2581–2597 (2009)
Vishwakarma, S., Sharma, V., Tiwari, A.: An intrusion detection system using KNN-ACO algorithm. Int. J. Comput. Appl. 171(10), 13–23 (2017)
Dada, E.G.: A hybridized SVM-KNN-pdAPSO approach to intrusion detection system. Fac. Semin. Ser. 8, 1–8 (2017)
Tang, T.A., Mhamdi, L., McLernon, D., Zaidi, S.A.R., Ghogho, M.: Deep learning approach for network intrusion detection in software-defined networking. In: International Conference on Wireless Networks and Mobile Communications (WINCOM), pp. 1–6, October 2016
Kokila, R.T., Selvi, S.T., Govindarajan, K.: DDoS detection and analysis in SDN-based environment using support vector machine classifier. In: Sixth International Conference on Advanced Computing, pp. 205–210 (2015)
Chowdhury, M.M.U., Hammond, F., Konowicz, G.: A few-shot deep learning approach for improved intrusion detection. In: IEEE, pp. 456–462 (2017)
Liu, Y., Liu, S.: Intrusion detection algorithm based on convolutional neural network. In: International Conference on Engineering Technology and Application, pp. 9–13 (2017)
Meena, G., Choudhary, R.R.: A review paper on IDS classification using KDD 99 and NSL KDD dataset in WEKA. In: International Conference on Computer, Communications, and Electronics, pp. 553–558 (2017)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Gamal, M., Abbas, H., Sadek, R. (2020). Hybrid Approach for Improving Intrusion Detection Based on Deep Learning and Machine Learning Techniques. In: Hassanien, AE., Azar, A., Gaber, T., Oliva, D., Tolba, F. (eds) Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020). AICV 2020. Advances in Intelligent Systems and Computing, vol 1153. Springer, Cham. https://doi.org/10.1007/978-3-030-44289-7_22
Download citation
DOI: https://doi.org/10.1007/978-3-030-44289-7_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-44288-0
Online ISBN: 978-3-030-44289-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)