Survey on Deep Learning Based Intrusion Detection System

Omar Muhammad Altoumi Alsyaibani, Ema Utami, Anggit Dwi Hartanto

Abstract


Development of computer network has changed human lives in many ways. Currently, everyone is connected to each other from everywhere. Information can be accessed easily. This massive development has to be followed by good security system. Intrusion Detection System is important device in network security which capable of monitoring hardware and software in computer network. Many researchers have developed Intrusion Detection System continuously and have faced many challenges, for instance: low detection of accuracy, emergence of new types malicious traffic and error detection rate. Researchers have tried to overcome these problems in many ways, one of them is using Deep Learning which is a branch of Machine Learning for developing Intrusion Detection System and it will be discussed in this paper. Machine Learning itself is a branch of Artificial Intelligence which is growing rapidly in the moment. Several researches have showed that Machine Learning and Deep Learning provide very promising results for developing Intrusion Detection System. This paper will present an overview about Intrusion Detection System in general, Deep Learning model which is often used by researchers, available datasets and challenges which will be faced ahead by researchers

Keywords


Deep Learning; IDS Research; IDS Review; Deep Learning Model; IDS Dataset

Full Text:

PDF (Indonesian)

References


Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moore, S., Murray, D. G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., … Zheng, X. (2016). TensorFlow: A system for large-scale machine learning. Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2016.

Aghdam, M. H., & Kabiri, P. (2016). Feature selection for intrusion detection system using ant colony optimization. International Journal of Network Security.

Al-Qatf, M., Lasheng, Y., Al-Habib, M., & Al-Sabahi, K. (2018). Deep Learning Approach Combining Sparse Autoencoder with SVM for Network Intrusion Detection. IEEE Access. https://doi.org/10.1109/ACCESS.2018.2869577

Aldweesh, A., Derhab, A., & Emam, A. Z. (2020). Deep learning approaches for anomaly-based intrusion detection systems: A survey, taxonomy, and open issues. Knowledge-Based Systems, 189. https://doi.org/10.1016/j.knosys.2019.105124

Alkasassbeh, M., Al-Naymat, G., B.A, A., & Almseidin, M. (2016). Detecting Distributed Denial of Service Attacks Using Data Mining Techniques. International Journal of Advanced Computer Science and Applications. https://doi.org/10.14569/ijacsa.2016.070159

Alom, M. Z., & Taha, T. M. (2017). Network intrusion detection for cyber security using unsupervised deep learning approaches. Proceedings of the IEEE National Aerospace Electronics Conference, NAECON. https://doi.org/10.1109/NAECON.2017.8268746

Alrawashdeh, K., & Purdy, C. (2017). Toward an online anomaly intrusion detection system based on deep learning. Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016. https://doi.org/10.1109/ICMLA.2016.167

Anderson, J. P. (1980). Computer security threat monitoring and surveillance. Technical Report James P Anderson Co Fort Washington Pa. https://doi.org/citeulike-article-id:592588

Bace, R., & Mell, P. (2001). NIST special publication on intrusion detection

systems. In Nist Special Publication.

Beer, F., Hofer, T., Karimi, D., & Bühler, U. (2017). A new attack composition for network security. Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft Fur Informatik (GI).

Beigi, E. B., Jazi, H. H., Stakhanova, N., & Ghorbani, A. A. (2014). Towards effective feature selection in machine learning-based botnet detection approaches. 2014 IEEE Conference on Communications and Network Security, CNS 2014. https://doi.org/10.1109/CNS.2014.6997492

Berman, D. S., Buczak, A. L., Chavis, J. S., & Corbett, C. L. (2019). A survey of deep learning methods for cyber security. In Information (Switzerland) (Vol. 10, Issue 4). https://doi.org/10.3390/info10040122

Bhattacharya, S., & Selvakumar, S. (2015). SSENet-2014 Dataset: A Dataset for Detection of Multiconnection Attacks. Proceedings - 2014 3rd International Conference on Eco-Friendly Computing and Communication Systems, ICECCS 2014. https://doi.org/10.1109/Eco-friendly.2014.100

Bhuyan, M. H., Bhattacharyya, D. K., & Kalita, J. K. (2015). Towards generating real-life datasets for network intrusion detection. International Journal of Network Security.

Chung, J., Gülçehre, Ç., Cho, K., & Bengio, Y. (2014). Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. CoRR, abs/1412.3. http://arxiv.org/abs/1412.3555

CyberEdge Group. (2020). 2020 Cyberthreat Defense Report. https://cyber-edge.com/cdr/

Deng, J., Zhang, Z., Marchi, E., & Schuller, B. (2013). Sparse autoencoder-based feature transfer learning for speech emotion recognition. Proceedings - 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction, ACII 2013. https://doi.org/10.1109/ACII.2013.90

Ding, S., & Wang, G. (2018). Research on intrusion detection technology based on deep learning. 2017 3rd IEEE International Conference on Computer and Communications, ICCC 2017. https://doi.org/10.1109/CompComm.2017.8322786

Drewek-Ossowicka, A., Pietrołaj, M., & Rumiński, J. (2021). A survey of neural networks usage for intrusion detection systems. Journal of Ambient Intelligence and Humanized Computing, 12(1). https://doi.org/10.1007/s12652-020-02014-x

Erickson, B. J., Korfiatis, P., Akkus, Z., Kline, T., & Philbrick, K. (2017). Toolkits and Libraries for Deep Learning. In Journal of Digital Imaging. https://doi.org/10.1007/s10278-017-9965-6

Farahnakian, F., & Heikkonen, J. (2018). A deep auto-encoder based approach for intrusion detection system. International Conference on Advanced Communication Technology, ICACT. https://doi.org/10.23919/ICACT.2018.8323688

Fulkerson, B., Michie, D., Spiegelhalter, D. J., & Taylor, C. C. (1995). Machine Learning, Neural and Statistical Classification. Technometrics. https://doi.org/10.2307/1269742

Gamage, S., & Samarabandu, J. (2020). Deep learning methods in network intrusion detection: A survey and an objective comparison. Journal of Network and Computer Applications, 169. https://doi.org/10.1016/j.jnca.2020.102767

García, S., Grill, M., Stiborek, J., & Zunino, A. (2014). An empirical comparison of botnet detection methods. Computers and Security. https://doi.org/10.1016/j.cose.2014.05.011

Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. Advances in Neural Information Processing Systems. https://doi.org/10.3156/jsoft.29.5_177_2

Graves, A., & Jaitly, N. (2014). Towards end-to-end speech recognition with recurrent neural networks. 31st International Conference on Machine Learning, ICML 2014.

Graves, A., Mohamed, A. R., & Hinton, G. (2013). Speech recognition with deep recurrent neural networks. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. https://doi.org/10.1109/ICASSP.2013.6638947

Gringoli, F., Salgarelli, L., Dusi, M., Cascarano, N., Risso, F., & Claffy, K. C. (2009). GT: Picking up the truth from the ground for internet traffic. Computer Communication Review. https://doi.org/10.1145/1629607.1629610

Gurung, S., Kanti Ghose, M., & Subedi, A. (2019). Deep Learning Approach on Network Intrusion Detection System using NSL-KDD Dataset. International Journal of Computer Network and Information Security. https://doi.org/10.5815/ijcnis.2019.03.02

Haider, W., Hu, J., Slay, J., Turnbull, B. P., & Xie, Y. (2017). Generating realistic intrusion detection system dataset based on fuzzy qualitative modeling. Journal of Network and Computer Applications. https://doi.org/10.1016/j.jnca.2017.03.018

Hassan, M. M., Gumaei, A., Alsanad, A., Alrubaian, M., & Fortino, G. (2020). A hybrid deep learning model for efficient intrusion detection in big data environment. Information Sciences. https://doi.org/10.1016/j.ins.2019.10.069

Heberlein, L. T., Dias, G. V, Levitt, K. N., Mukherjee, B., Wood, J., & Wolber, D. (1989). A network security monitor. https://doi.org/https://doi.org/10.2172/6223037

Hindy, H., Brosset, D., Bayne, E., Seeam, A. K., Tachtatzis, C., Atkinson, R., & Bellekens, X. (2020). A Taxonomy of Network Threats and the Effect of Current Datasets on Intrusion Detection Systems. IEEE Access, 8. https://doi.org/10.1109/ACCESS.2020.3000179

Hochreiter, S., & Urgen Schmidhuber, J. J. (1997). Long short term memory. Neural computation. MEMORY Neural Computation.

Hofstede, R., Hendriks, L., Sperotto, A., & Pras, A. (2014). SSH compromise detection using NetFlow/IPFIX. Computer Communication Review. https://doi.org/10.1145/2677046.2677050

Ieracitano, C., Adeel, A., Gogate, M., Dashtipour, K., Morabito, F. C., Larijani, H., Raza, A., & Hussain, A. (2018). Statistical Analysis Driven Optimized Deep Learning System for Intrusion Detection. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). https://doi.org/10.1007/978-3-030-00563-4_74

Jazi, H. H., Gonzalez, H., Stakhanova, N., & Ghorbani, A. A. (2017). Detecting HTTP-based application layer DoS attacks on web servers in the presence of sampling. Computer Networks. https://doi.org/10.1016/j.comnet.2017.03.018

Kent, A. D. (2016). Cyber security data sources for dynamic network research. In Dynamic Networks and Cyber-Security. https://doi.org/10.1142/9781786340757_0002

Khan, F. A., Gumaei, A., Derhab, A., & Hussain, A. (2019). TSDL: A Two-Stage Deep Learning Model for Efficient Network Intrusion Detection. IEEE Access. https://doi.org/10.1109/ACCESS.2019.2899721

Kim, J., Kim, J., Thu, H. L. T., & Kim, H. (2016). Long Short Term Memory Recurrent Neural Network Classifier for Intrusion Detection. 2016 International Conference on Platform Technology and Service, PlatCon 2016 - Proceedings. https://doi.org/10.1109/PlatCon.2016.7456805

Kolias, C., Kambourakis, G., Stavrou, A., & Gritzalis, S. (2016). Intrusion detection in 802.11 networks: Empirical evaluation of threats and a public dataset. IEEE Communications Surveys and Tutorials. https://doi.org/10.1109/COMST.2015.2402161

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems. https://doi.org/10.1061/(ASCE)GT.1943-5606.0001284

Lawrence, S., Giles, C. L., Tsoi, A. C., & Back, A. D. (1997). Face recognition: A convolutional neural-network approach. IEEE Transactions on Neural Networks. https://doi.org/10.1109/72.554195

Lee, H., Battle, A., Raina, R., & Ng, A. Y. (2007). Efficient sparse coding algorithms. Advances in Neural Information Processing Systems. https://doi.org/10.7551/mitpress/7503.003.0105

Lee, S. M., Yoon, S. M., & Cho, H. (2017). Human activity recognition from accelerometer data using Convolutional Neural Network. 2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017. https://doi.org/10.1109/BIGCOMP.2017.7881728

Li, Z., Qin, Z., Huang, K., Yang, X., & Ye, S. (2017). Intrusion detection using convolutional neural networks for representation learning. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). https://doi.org/10.1007/978-3-319-70139-4_87

Lippmann, R. P., Fried, D. J., Graf, I., Haines, J. W., Kendall, K. R., McClung, D., Weber, D., Webster, S. E., Wyschogrod, D., Cunningham, R. K., &

Zissman, M. A. (2000). Evaluating intrusion detection systems: The 1998 DARPA off-line intrusion detection evaluation. Proceedings - DARPA Information Survivability Conference and Exposition, DISCEX 2000. https://doi.org/10.1109/DISCEX.2000.821506

Maciá-Fernández, G., Camacho, J., Magán-Carrión, R., García-Teodoro, P., & Therón, R. (2018). UGR‘16: A new dataset for the evaluation of cyclostationarity-based network IDSs. Computers and Security. https://doi.org/10.1016/j.cose.2017.11.004

Mighan, S. N., & Kahani, M. (2020). A novel scalable intrusion detection system based on deep learning. International Journal of Information Security. https://doi.org/10.1007/s10207-020-00508-5

Moustafa, N., & Slay, J. (2015). UNSW-NB15: A comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set). 2015 Military Communications and Information Systems Conference, MilCIS 2015 - Proceedings. https://doi.org/10.1109/MilCIS.2015.7348942

Nadeem, M., Marshall, O., Singh, S., Fang, X., & Yuan, X. (2016). Semi-Supervised Deep Neural Network for Network Intrusion Detection. Research and Practice.

Naseer, S., Saleem, Y., Khalid, S., Bashir, M. K., Han, J., Iqbal, M. M., & Han, K. (2018). Enhanced network anomaly detection based on deep neural networks. IEEE Access. https://doi.org/10.1109/ACCESS.2018.2863036

Niyaz, Q., Sun, W., Javaid, A. Y., & Alam, M. (2015). A deep learning approach for network intrusion detection system. EAI International Conference on Bio-Inspired Information and Communications Technologies (BICT). https://doi.org/10.4108/eai.3-12-2015.2262516

Nweke, H. F., Teh, Y. W., Al-garadi, M. A., & Alo, U. R. (2018). Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges. In Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2018.03.056

Otoum, S., Kantarci, B., & Mouftah, H. T. (2019). On the Feasibility of Deep Learning in Sensor Network Intrusion Detection. IEEE Networking Letters. https://doi.org/10.1109/lnet.2019.2901792

Pang, R., Allman, M., Bennett, M., Lee, J., Paxson, V., & Tierney, B. (2005). A first look at modern enterprise traffic. Proceedings of the ACM SIGCOMM Internet Measurement Conference, IMC. https://doi.org/10.1145/1330107.1330110

Papamartzivanos, D., Gomez Marmol, F., & Kambourakis, G. (2019). Introducing Deep Learning Self-Adaptive Misuse Network Intrusion Detection Systems. IEEE Access. https://doi.org/10.1109/ACCESS.2019.2893871

Parvat, A., Chavan, J., Kadam, S., Dev, S., & Pathak, V. (2017). A survey of deep-learning frameworks. Proceedings of the International Conference on Inventive Systems and Control, ICISC 2017. https://doi.org/10.1109/ICISC.2017.8068684

Ranzato, M., Boureau, Y. L., & Le Cun, Y. (2009). Sparse feature learning for deep belief networks. Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference.

Razavian, A. S., Azizpour, H., Sullivan, J., & Carlsson, S. (2014). CNN features off-the-shelf: An astounding baseline for recognition. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. https://doi.org/10.1109/CVPRW.2014.131

Ring, M., Landes, D., & Hotho, A. (2018). Detection of slow port scans in flow-based network traffic. PLoS ONE. https://doi.org/10.1371/journal.pone.0204507

Ring, M., Wunderlich, S., Scheuring, D., Landes, D., & Hotho, A. (2019). A survey of network-based intrusion detection data sets. In Computers and Security. https://doi.org/10.1016/j.cose.2019.06.005

Roy, S. S., Mallik, A., Gulati, R., Obaidat, M. S., & Krishna, P. V. (2017). A deep learning based artificial neural network approach for intrusion detection. Communications in Computer and Information Science. https://doi.org/10.1007/978-981-10-4642-1_5

Saad, S., Traore, I., Ghorbani, A., Sayed, B., Zhao, D., Lu, W., Felix, J., & Hakimian, P. (2011). Detecting P2P botnets through network behavior analysis and machine learning. 2011 9th Annual International Conference on Privacy, Security and Trust, PST 2011. https://doi.org/10.1109/PST.2011.5971980

Sangster, B., O’Connor, T. J., Cook, T., Fanelli, R., Dean, E., Adams, W. J., Morrell, C., & Conti, G. (2009). Toward instrumenting network warfare competitions to generate labeled datasets. 2nd Workshop on Cyber Security Experimentation and Test, CSET 2009.

Santanna, J. J., Van Rijswijk-Deij, R., Hofstede, R., Sperotto, A., Wierbosch, M., Granville, L. Z., & Pras, A. (2015). Booters - An analysis of DDoS-as-a-service attacks. Proceedings of the 2015 IFIP/IEEE International Symposium on Integrated Network Management, IM 2015. https://doi.org/10.1109/INM.2015.7140298

Schuster, M., & Paliwal, K. K. (1997). Bidirectional recurrent neural networks. IEEE Transactions on Signal Processing. https://doi.org/10.1109/78.650093

Sharafaldin, I., Gharib, A., Lashkari, A. H., & Ghorbani, A. A. (2017). Towards a Reliable Intrusion Detection Benchmark Dataset. Software Networking. https://doi.org/10.13052/jsn2445-9739.2017.009

Sharma, R., Singla, R. K., & Guleria, A. (2018). A New Labeled Flow-based DNS Dataset for Anomaly Detection: PUF Dataset. Procedia Computer Science. https://doi.org/10.1016/j.procs.2018.05.079

Shiravi, A., Shiravi, H., Tavallaee, M., & Ghorbani, A. A. (2012). Toward developing a systematic approach to generate benchmark datasets for intrusion detection. Computers and Security. https://doi.org/10.1016/j.cose.2011.12.012

Shone, N., Ngoc, T. N., Phai, V. D., & Shi, Q. (2018). A Deep Learning Approach to Network Intrusion Detection. IEEE Transactions on Emerging Topics in Computational Intelligence. https://doi.org/10.1109/TETCI.2017.2772792

Singh, R., Kumar, H., & Singla, R. K. (2015). A reference dataset for network traffic activity based intrusion detection system. International Journal of Computers, Communications and Control. https://doi.org/10.15837/ijccc.2015.3.1924

Singla, A., Bertino, E., & Verma, D. (2020). Preparing Network Intrusion Detection Deep Learning Models with Minimal Data Using Adversarial Domain Adaptation. Proceedings of the 15th ACM Asia Conference on Computer and Communications Security, ASIA CCS 2020. https://doi.org/10.1145/3320269.3384718

Song, J., Takakura, H., Okabe, Y., Eto, M., Inoue, D., & Nakao, K. (2011). Statistical analysis of honeypot data and building of Kyoto 2006+ dataset for NIDS evaluation. Proceedings of the 1st Workshop on Building Analysis Datasets and Gathering Experience Returns for Security, BADGERS 2011. https://doi.org/10.1145/1978672.1978676

Sperotto, A., Sadre, R., Van Vliet, F., & Pras, A. (2009). A labeled data set for flow-based intrusion detection. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). https://doi.org/10.1007/978-3-642-04968-2_4

Tavallaee, M., Bagheri, E., Lu, W., & Ghorbani, A. A. (2009). A detailed analysis of the KDD CUP 99 data set. IEEE Symposium on Computational Intelligence for Security and Defense Applications, CISDA 2009. https://doi.org/10.1109/CISDA.2009.5356528

Turcotte, M. J. M., Kent, A. D., & Hash, C. (2017). Unified host and network data set. In arXiv. https://doi.org/10.1142/9781786345646_001

UCI Machine Learning Repository. (2015). KDD Cup 1999 Data. In 1999]. Http://Kdd. Ics. Uci. Edu/Databases/Kddcup99/Kddcup99. Html.

Ustebay, S., Turgut, Z., & Aydin, M. A. (2019). Intrusion Detection System with Recursive Feature Elimination by Using Random Forest and Deep Learning Classifier. International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism, IBIGDELFT 2018 - Proceedings. https://doi.org/10.1109/IBIGDELFT.2018.8625318

Van, N. T., Thinh, T. N., & Sach, L. T. (2017). An anomaly-based network intrusion detection system using Deep learning. Proceedings - 2017 International Conference on System Science and Engineering, ICSSE 2017. https://doi.org/10.1109/ICSSE.2017.8030867

Vasudevan, A. R., Harshini, E., & Selvakumar, S. (2011). SSENet-2011: A Network Intrusion Detection System dataset and its comparison with KDD CUP 99 dataset. Asian Himalayas International Conference on Internet. https://doi.org/10.1109/AHICI.2011.6113948

Viegas, E. K., Santin, A. O., & Oliveira, L. S. (2017). Toward a reliable anomaly-based intrusion detection in real-world environments. Computer Networks. https://doi.org/10.1016/j.comnet.2017.08.013

Vinayakumar, R., Alazab, M., Soman, K. P., Poornachandran, P., Al-Nemrat, A., & Venkatraman, S. (2019). Deep Learning Approach for Intelligent Intrusion Detection System. IEEE Access. https://doi.org/10.1109/ACCESS.2019.2895334

Vinayakumar, R., Soman, K. P., & Poornachandrany, P. (2017). Applying convolutional neural network for network intrusion detection. 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017. https://doi.org/10.1109/ICACCI.2017.8126009

Vincent, P., Larochelle, H., Bengio, Y., & Manzagol, P. A. (2008). Extracting and composing robust features with denoising autoencoders. Proceedings of the 25th International Conference on Machine Learning. https://doi.org/10.1145/1390156.1390294

Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., & Manzagol, P. A. (2010). Stacked denoising autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion. Journal of Machine Learning Research.

Wang, H., & Yu, C. N. (2019). A Direct Approach to Robust Deep Learning Using Adversarial Networks. In arXiv.

Wang, Z. (2018). Deep Learning-Based Intrusion Detection with Adversaries. IEEE Access. https://doi.org/10.1109/ACCESS.2018.2854599

Wheelus, C., Khoshgoftaar, T. M., Zuech, R., & Najafabadi, M. M. (2014). A session based approach for aggregating network traffic data - The SANTA dataset. Proceedings - IEEE 14th International Conference on Bioinformatics and Bioengineering, BIBE 2014. https://doi.org/10.1109/BIBE.2014.72

Xin, Y., Kong, L., Liu, Z., Chen, Y., Li, Y., Zhu, H., Gao, M., Hou, H., & Wang, C. (2018). Machine Learning and Deep Learning Methods for Cybersecurity. IEEE Access. https://doi.org/10.1109/ACCESS.2018.2836950

Yang, K., Liu, J., Zhang, C., & Fang, Y. (2019). Adversarial Examples Against the Deep Learning Based Network Intrusion Detection Systems. Proceedings - IEEE Military Communications Conference MILCOM. https://doi.org/10.1109/MILCOM.2018.8599759

Yin, C., Zhu, Y., Fei, J., & He, X. (2017). A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks. IEEE Access. https://doi.org/10.1109/ACCESS.2017.2762418

Yu, Y., Long, J., & Cai, Z. (2017). Session-based network intrusion detection using a deep learning architecture. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). https://doi.org/10.1007/978-3-319-67422-3_13

Zavrak, S., & Iskefiyeli, M. (2020). Anomaly-Based Intrusion Detection from Network Flow Features Using Variational Autoencoder. IEEE Access. https://doi.org/10.1109/ACCESS.2020.3001350

Zeng, Y., Gu, H., Wei, W., & Guo, Y. (2019). Deep-Full-Range: A Deep Learning Based Network Encrypted Traffic Classification and Intrusion Detection Framework. IEEE Access. https://doi.org/10.1109/ACCESS.2019.2908225

Zhang, H., Wu, C. Q., Gao, S., Wang, Z., Xu, Y., & Liu, Y. (2018). An Effective Deep Learning Based Scheme for Network Intrusion Detection. Proceedings - International Conference on Pattern Recognition. https://doi.org/10.1109/ICPR.2018.8546162

Zhao, G., Zhang, C., & Zheng, L. (2017). Intrusion detection using deep belief network and probabilistic neural network. Proceedings - 2017 IEEE International Conference on Computational Science and Engineering and IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, CSE and EUC 2017. https://doi.org/10.1109/CSE-EUC.2017.119

Zuech, R., Khoshgoftaar, T. M., Seliya, N., Najafabadi, M. M., & Kemp, C. (2015). A new intrusion detection benchmarking system. Proceedings of the 28th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2015.




DOI: http://dx.doi.org/10.35671/telematika.v14i2.1317

Refbacks

  • There are currently no refbacks.




Indexed by:

     http://click.accelo.com/wf/click?upn=KMJOFt8368XHDV6m09YF-2BTGnIfzAj8ov81j3S3dKrgX-2FSP8SBOSe2Y-2FRl3XtyVdizj-2FkXxL-2F-2FBp-2BQ3h3JmTUMA-3D-3D_m-2BrHp932aZXzO0XgkbwedgKvn5QWlonE5sMgaivZdq7OsTVSTY4hEqzD-2Bq18nXAyLJBneuiZlt38H2UV92XxYUTcMxEriSXBXl4R62YQbqlgPCj4HTJTRlEeMBija8NFLIgPs2I1UuCR2UCZXSiKb2ocM6V4QaW-2FslHJUiSZesKuX9OlsnCNztILLyuQC4ZZvCegHVeQWDMYSYLvWzv-2FxgZ4v9s-2B2Ehf-2FEsLNi2Ea97Xe1t2vA4kmxioKhj90qGfUs7WlNUb-2B3FL0DjX8F4BTUuUiemqtsGMdQg-2By7qV9RY-3D       

Telematika

ISSN 2442-4528 (online) | ISSN 1979-925X (print)
Published by : Universitas Amikom Purwokerto
Jl. Let. Jend. POL SUMARTO Watumas, Purwonegoro - Purwokerto Telp (0281) 623321 Fax (0281) 621662
Email: telematika@amikompurwokerto.ac.id

Creative Commons License
This work is licensed under a  Creative Commons Attribution 4.0 International License.