An Intrusion Detection System Using SDAE to Enhance Dimensional Reduction in Machine Learning

Hanafi Hanafi - University of Amikom Yogyakarta, Depok, Sleman, Indonesia
Alva Hendi Muhammad - University of Amikom Yogyakarta, Depok, Sleman, Indonesia
Ike Verawati - University of Amikom Yogyakarta, Depok, Sleman, Indonesia
Richki Hardi - Universitas Mulia, Balikpapan, East Kalimantan, Indonesia


Citation Format:



DOI: http://dx.doi.org/10.30630/joiv.6.2.990

Abstract


In the last decade, the number of attacks on the internet has grown significantly, and the types of attacks vary widely. This causes huge financial losses in various institutions such as the private and government sectors. One of the efforts to deal with this problem is by early detection of attacks, often called IDS (instruction detection system). The intrusion detection system was deactivated. An Intrusion Detection System (IDS) is a hardware or software mechanism that monitors the Internet for malicious attacks. It can scan the internetwork for potentially dangerous behavior or security threats. IDS is responsible for maintaining network activity under the Network-Based Intrusion Detection System (NIDS) or Host-Based Intrusion Detection System (HIDS). IDS works by comparing known normal network activity signatures with attack activity signatures. In this research, a dimensional reduction and feature selection mechanism called Stack Denoising Auto Encoder (SDAE) succeeded in increasing the effectiveness of Naive Bayes, KNN, Decision Tree, and SVM. The researchers evaluated the performance using evaluation metrics with a confusion matrix, accuracy, recall, and F1-score. Compared with the results of previous works in the IDS field, our model increased the effectiveness to more than 2% in NSL-KDD Dataset, including in binary class and multi-class evaluation methods. Moreover, using SDAE also improved traditional machine learning with modern deep learning such as Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN). In the future, it is possible to integrate SDAE with a deep learning model to enhance the effectiveness of IDS detection

Keywords


IDS detection; SDAE; naive Bayes; decision tree; SVM; auto encoder

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References


B. Zarpelão, R. Miani, … C. K.-J. of N. and, and undefined 2017, “A survey of intrusion detection in Internet of Things,†Elsevier, 2017, doi: 10.1016/j.jnca.2017.02.009.

K. N. L. biswanath Mukherjee, L. Todd Heberlein, “Network Intrusion Detection,†IEEE Netw., 1994.

S. Wagh, A. ali shah, S. Kishor Wagh, V. K. Pachghare, and S. R. Kolhe, “Survey on Intrusion Detection System using Machine Learning Techniques,†Int. J. Comput. Appl., vol. 78, no. 16, pp. 975–8887, 2013.

N. Sultana, N. Chilamkurti, W. Peng, and R. Alhadad, “Survey on SDN based network intrusion detection system using machine learning approaches Emulated Monitoring Systems View project Deep Learning View project Survey on SDN based network intrusion detection system using machine learning approaches,†doi: 10.1007/s12083-017-0630-0.

S. M. Kasongo and Y. Sun, “Performance Analysis of Intrusion Detection Systems Using a Feature Selection Method on the UNSW-NB15 Dataset,†J. Big Data, vol. 7, no. 1, 2020, doi: 10.1186/s40537-020-00379-6.

F. E. Laghrissi, S. Douzi, K. Douzi, and B. Hssina, “IDS-attention: an efficient algorithm for intrusion detection systems using attention mechanism,†J. Big Data, vol. 8, no. 1, 2021, doi: 10.1186/s40537-021-00544-5.

H. Zhang, “Design of intrusion detection system based on a new pattern matching algorithm,†Proc. - 2009 Int. Conf. Comput. Eng. Technol. ICCET 2009, vol. 1, pp. 545–548, 2009, doi: 10.1109/ICCET.2009.244.

C. Yin, “An Improved BM Pattern Matching Algorithm in Intrusion Detection System,†Appl. Mech. Mater., vol. 148–149, pp. 1145–1148, 2012, doi: 10.4028/WWW.SCIENTIFIC.NET/AMM.148-149.1145.

D. E. Denning, “An Intrusion-Detection Model,†IEEE Trans. Softw. Eng., vol. 13, no. 2, pp. 222–232, 1987.

M. Pervez, D. F.-T. 8th I. C. on, and undefined 2014, “Feature selection and intrusion classification in NSL-KDD cup 99 dataset employing SVMs,†ieeexplore.ieee.org, 2015, doi: 10.1109/SKIMA.2014.7083539.

J. Zhang, M. Zulkernine, and A. Haque, “Random-forests-based network intrusion detection systems,†IEEE Trans. Syst. Man Cybern. Part C Appl. Rev., vol. 38, no. 5, pp. 649–659, 2008, doi: 10.1109/TSMCC.2008.923876.

B. Ingre and A. Yadav, “Performance analysis of NSL-KDD dataset using ANN,†Int. Conf. Signal Process. Commun. Eng. Syst. - Proc. SPACES 2015, Assoc. with IEEE, pp. 92–96, Mar. 2015, doi: 10.1109/SPACES.2015.7058223.

B. Ingre, A. Yadav, and A. K. Soni, “Decision Tree Based Intrusion Detection System for NSL-KDD Dataset,†Smart Innov. Syst. Technol., vol. 84, pp. 207–218, 2017, doi: 10.1007/978-3-319-63645-0_23.

N. Rusk, “Deep learning,†Nat. Methods, vol. 13, no. 1, p. 35, 2015, doi: 10.1038/nmeth.3707.

Hanafi, A. Pranolo, and Y. Mao, “Cae-covidx: Automatic covid-19 disease detection based on x-ray images using enhanced deep convolutional and autoencoder,†Int. J. Adv. Intell. Informatics, vol. 7, no. 1, pp. 49–62, 2021, doi: 10.26555/ijain.v7i1.577.

Hanafi and B. M. Aboobaider, “Word Sequential Using Deep LSTM and Matrix Factorization to Handle Rating Sparse Data for E-Commerce Recommender System,†Comput. Intell. Neurosci., vol. 2021, no. 1, 2021, doi: https://doi.org/10.1155/2021/8751173 Research.

Hanafi, E. Pujastuti, A. Laksito, A. Arfriandi, R. Hardi, and R. Perwira, “Handling Sparse Rating Matrix for E-commerce Recommender System Using Hybrid Deep Learning Based on LSTM , SDAE and Latent Factor,†vol. 15, no. 2, pp. 379–393, 2022, doi: 10.22266/ijies2022.0430.35.

Hanafi, N. Suryana, and A. S. B. H. BASARI, “Recommender System Based Tensor Candecomp Parafact Algorithm-ALS to Handle Sparse Data In Food Commerce Information Services,†IJSSST, pp. 1–9, 2019, doi: 10.5013/IJSSST.a.19.06.60.

Hanafi, N. Suryana, and A. S. B. H. Basari, “Convolutional-NN and word embedding for making an effective product recommendation based on enhanced contextual understanding of a product review,†Int. J. Adv. Sci. Eng. Inf. Technol., vol. 9, no. 3, 2019, doi: 10.18517/ijaseit.9.3.8843.

Hanafi, N. Suryana, and A. S. B. H. Basari, “Paper Survey and Example of Collaborative Filtering Implementation in Recommender System,†J. Theor. Appl. Inf. Technol., vol. 95, no. 16, 2017.

Hanafi, R. Widyawati, and A. S. Widowati, “Effect of service quality and online servicescape toward customer satisfaction and loyalty mediated by perceived value,†IOP Conf. Ser. Earth Environ. Sci., vol. 704, no. 1, 2021, doi: 10.1088/1755-1315/704/1/012011.

Hanafi, N. Suryana, and A. S. H. Basari, “Generate Contextual Insight of Product Review Using Deep LSTM and Word Embedding,†J. Phys. Conf. Ser., vol. 1577, no. 1, 2020, doi: 10.1088/1742-6596/1577/1/012006.

Hanafi, N. Suryana, and A. S. H. Basari, “Deep Contextual of Document Using Deep LSTM Meet Matrix Factorization to Handle Sparse Data: Proposed Model,†J. Phys. Conf. Ser., vol. 1577, no. 1, 2020, doi: 10.1088/1742-6596/1577/1/012002.

Hanafi, N. Suryana, and A. S. H. Basari, “Involve Convolutional-NN to Generate Item Latent Factor Consider Product Genre to Increase Robustness in Product Sparse Data for E-commerce Recommendation,†J. Phys. Conf. Ser., vol. 1201, no. 1, 2019, doi: 10.1088/1742-6596/1201/1/012004.

Hanafi, N. Suryana, and A. Samad, “Dynamic convolutional neural network for eliminating item sparse data on recommender system,†IJAIN, vol. 4, no. 3, pp. 226–237, 2018.

A. Javaid, Q. Niyaz, W. Sun, M. A.-E. E. T. on, and undefined 2016, “A deep learning approach for network intrusion detection system,†eprints.eudl.eu, 2016, doi: 10.4108/eai.3-12-2015.2262516.

G. Zhao, C. Zhang, L. Z.-2017 I. International, and undefined 2017, “Intrusion detection using deep belief network and probabilistic neural network,†ieeexplore.ieee.org, 2017, doi: 10.1109/CSE-EUC.2017.119.

F. Qu, J. Zhang, Z. Shao, S. Q.-P. of the 2017 V. international, and undefined 2017, “An intrusion detection model based on deep belief network,†dl.acm.org, pp. 97–101, Dec. 2017, doi: 10.1145/3171592.3171598.

M. Z. Alom, V. Bontupalli, and T. M. Taha, “Intrusion detection using deep belief networks,†in National Aerospace and Electronics Conference (NAECON), 2015, pp. 333–344, doi: 10.1109/NAECON.2015.7443094.

J. Kim, N. Shin, S. Y. Jo, and S. H. Kim, “Method of intrusion detection using deep neural network,†2017 IEEE Int. Conf. Big Data Smart Comput. BigComp 2017, pp. 313–316, Mar. 2017, doi: 10.1109/BIGCOMP.2017.7881684.

K. Wu, Z. Chen, and W. Li, “A Novel Intrusion Detection Model for a Massive Network Using Convolutional Neural Networks,†IEEE Access, vol. 6, pp. 50850–50859, 2018, doi: 10.1109/ACCESS.2018.2868993.

K. Hara and K. Shiomoto, “Intrusion Detection System using Semi-Supervised Learning with Adversarial Auto-encoder,†Proc. IEEE/IFIP Netw. Oper. Manag. Symp. 2020 Manag. Age Softwarization Artif. Intell. NOMS 2020, 2020, doi: 10.1109/NOMS47738.2020.9110343.

S. Hochreiter and J. Urgen Schmidhuber, “Lstm,†Neural Comput., vol. 9, no. 8, pp. 1735–1780, 1997, doi: 10.1162/neco.1997.9.8.1735.

hanafi and andi sunyoto, “Enhance Intrusion Detection (IDS) System Using Deep SDAE to Increase Effectiveness of Dimensional Reduction in Machine Learning and Deep Learning,†vol. 15, no. 4, pp. 125–141, Jun. 2022.

F. E. Laghrissi, S. Douzi, K. Douzi, and B. Hssina, “Intrusion detection systems using long short-term memory (LSTM),†J. Big Data, vol. 8, no. 1, 2021, doi: 10.1186/s40537-021-00448-4.

M. Tavallaee, E. Bagheri, W. Lu, and A. A. Ghorbani, “A detailed analysis of the KDD CUP 99 data set,†IEEE Symp. Comput. Intell. Secur. Def. Appl. CISDA 2009, no. July, 2009, doi: 10.1109/CISDA.2009.5356528.

W. Li, P. Yi, Y. Wu, L. Pan, and J. Li, “A new intrusion detection system based on KNN classification algorithm in wireless sensor network,†J. Electr. Comput. Eng., vol. 2014, no. 1, 2014, doi: 10.1155/2014/240217.

R. Taguelmimt and R. Beghdad, “DS-kNN: An intrusion detection system based on a distance sum-based K-nearest neighbors,†Int. J. Inf. Secur. Priv., vol. 15, no. 2, pp. 131–144, 2021, doi: 10.4018/IJISP.2021040107.

S. Choi, “Combined kNN classification and hierarchical similarity hash for fast malware detection,†Appl. Sci., vol. 10, no. 15, pp. 1–16, 2020, doi: 10.3390/app10155173.

M. A. Ferrag, L. Maglaras, A. Ahmim, M. Derdour, and H. Janicke, “RDTIDS: Rules and decision tree-based intrusion detection system for internet-of-things networks,†Futur. Internet, vol. 12, no. 3, pp. 1–14, 2020, doi: 10.3390/fi12030044.

K. Rai, M. S. Devi, and A. Guleria, “Decision Tree Based Algorithm for Intrusion Detection,†Int. J. Adv. Netw. Appl., vol. 07, no. 04, pp. 2828–2834, 2016.

T. Su, H. Sun, J. Zhu, S. Wang, and Y. Li, “BAT: Deep Learning Methods on Network Intrusion Detection Using NSL-KDD Dataset,†IEEE Access, vol. 8, pp. 29575–29585, 2020, doi: 10.1109/ACCESS.2020.2972627.

C. Ieracitano, A. Adeel, F. C. Morabito, and A. Hussain, “A novel statistical analysis and autoencoder driven intelligent intrusion detection approach,†Neurocomputing, vol. 387, pp. 51–62, 2020, doi: 10.1016/j.neucom.2019.11.016.