Utilizing neural networks with CICIDS2018 dataset for detecting brute force attack anomalies in intrusion detection systems
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Published:
Feb 28, 2024
Keywords:
Brute force, Detection, K-Medoids, Intrusion, Neural Network
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Computer Science
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Abstract
In this study, the effectiveness of neural networks in Intrusion Detection Systems (IDS) has been tested using the CICIDS2018 dataset to achieve accurate intrusion detection results. The research findings reveal that several neural network parameters will reach optimal results with a learning rate of 0.1, a training and testing data proportion of 80:20, and an optimal number of nodes in the hidden layer of 4. Other parameters such as a minimum error of 0.0001 and 2500 iterations also play a crucial role in improving IDS capability. Based on the research, it is shown that neural network models can provide optimal results in detecting intrusion patterns. This study can assist in the development of reliable and efficient neural network-based IDS to address the challenges of intrusion detection
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Heryanto, A. ., Hermansyah, A. ., Septian, T. . and Bardadi, A. . (2024) “Utilizing neural networks with CICIDS2018 dataset for detecting brute force attack anomalies in intrusion detection systems”, Jurnal Mantik, 7(4), pp. 3906-3916. doi: 10.35335/mantik.v7i4.4919.
References
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Najafian, Z., Aghazarian, V., & Hedayati, A. (2015). Signature-Based Method and Stream Data Mining Technique Performance Evaluation for Security and Intrusion Detection in Advanced Metering Infrastructures (AMI). International Journal of Computer and Electrical Engineering, 7(2), 128–139. https://doi.org/10.17706/ijcee.2015.v7.879
Otoum, Y., & Nayak, A. (2021). AS-IDS: Anomaly and Signature Based IDS for the Internet of Things. Journal of Network and Systems Management, 29(3). https://doi.org/10.1007/S10922-021-09589-6
Pawlicki, M., Kozik, R., & Chora?, M. (2022). A survey on neural networks for (cyber-) security and (cyber-) security of neural networks. Neurocomputing, 500, 1075–1087. https://doi.org/10.1016/J.NEUCOM.2022.06.002/A_SURVEY_ON_NEURAL_NETWORKS_FOR_CYBER_SECURITY_AND_CYBER_SECURITY_OF_NEURAL_NETWORKS.PDF
Sadasivam, G. K., Hota, C., & Anand, B. (2016). Classification of SSH attacks using machine learning algorithms. 2016 6th International Conference on IT Convergence and Security, ICITCS 2016. https://doi.org/10.1109/ICITCS.2016.7740316
Shah, S. A. R., & Issac, B. (2018). Performance comparison of intrusion detection systems and application of machine learning to Snort system. Future Generation Computer Systems, 80, 157–170. https://doi.org/10.1016/j.future.2017.10.016
Sung, Y., Jang, S., Jeong, Y. S., & Park, J. H. (James J. ). (2020). Malware classification algorithm using advanced Word2vec-based Bi-LSTM for ground control stations. Computer Communications, 153(January), 342–348. https://doi.org/10.1016/j.comcom.2020.02.005
Wang, Y. C., Houng, Y. C., Chen, H. X., & Tseng, S. M. (2023). Network Anomaly Intrusion Detection Based on Deep Learning Approach. Sensors 2023, Vol. 23, Page 2171, 23(4), 2171. https://doi.org/10.3390/S23042171
Amijoyo, T., Umar, R., & Yudhana, A. (2020). Bruteforce In The Hydra Process And Telnet Service Using The Naïve Bayes Method. Jurnal Mantik, 4(1).
Ananin, E. V., Nikishova, A. V., & Kozhevnikova, I. S. (2017). Port scanning detection based on anomalies. 11th International IEEE Scientific and Technical Conference &Amp;Quot;Dynamics of Systems, Mechanisms and Machines&Amp;Quot;, Dynamics 2017 - Proceedings, 2017-Novem, 1–5. https://doi.org/10.1109/Dynamics.2017.8239427
Aziz Sharfuddin, A., Nafis Tihami, M., & Saiful Islam, M. (2018). A Deep Recurrent Neural Network with BiLSTM model for Sentiment Classification. 2018 International Conference on Bangla Speech and Language Processing, ICBSLP 2018, 1–4. https://doi.org/10.1109/ICBSLP.2018.8554396
Bharati, M., & Tamane, S. (2017). Intrusion detection systems (IDS) & future challenges in cloud based environment. Proceedings - 1st International Conference on Intelligent Systems and Information Management, ICISIM 2017, 2017-January, 240–250. https://doi.org/10.1109/ICISIM.2017.8122180
Bhatia, V., Choudhary, S., & Ramkumar, K. R. (2020). A Comparative Study on Various Intrusion Detection Techniques Using Machine Learning and Neural Network. ICRITO 2020 - IEEE 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), 232–236. https://doi.org/10.1109/ICRITO48877.2020.9198008
Bostani, H., & Sheikhan, M. (2017). Hybrid of anomaly-based and specification-based IDS for Internet of Things using unsupervised OPF based on MapReduce approach. Computer Communications, 98, 52–71. https://doi.org/10.1016/j.comcom.2016.12.001
Bul’ajoul, W., James, A., & Pannu, M. (2013). Network intrusion detection systems in high-speed traffic in computer networks. Proceedings - 2013 IEEE 10th International Conference on e-Business Engineering, ICEBE 2013, 168–175. https://doi.org/10.1109/ICEBE.2013.26
Chaudhary, A. (2015). Development of a new intrusion detection system for mobile and ADHOC networks using soft computing techniques. University. http://hdl.handle.net/10603/184962
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, 102767. https://doi.org/10.1016/J.JNCA.2020.102767
Gunjan, V. K. (2015). Advancement of artificial neural network algorithms for intrusion detection in computer network. INFLIBNET. http://hdl.handle.net/10603/148064
Guri, M., Puzis, R., Choo, K. K. R., Rubinshtein, S., Kedma, G., & Elovici, Y. (2019). Using malware for the greater good: Mitigating data leakage. Journal of Network and Computer Applications, 145, 102405. https://doi.org/10.1016/J.JNCA.2019.07.006
Hossain, M. D., Ochiai, H., Doudou, F., & Kadobayashi, Y. (2020). SSH and FTP brute-force attacks detection in computer networks: Lstm and machine learning approaches. 2020 5th International Conference on Computer and Communication Systems, ICCCS 2020, 491–497. https://doi.org/10.1109/ICCCS49078.2020.9118459
Hosseini, S., & Zade, B. M. H. (2020). New hybrid method for attack detection using combination of evolutionary algorithms, SVM, and ANN. Computer Networks, 173, 107168. https://doi.org/10.1016/J.COMNET.2020.107168
Imrana, Y., Xiang, Y., Ali, L., & Abdul-Rauf, Z. (2021). A bidirectional LSTM deep learning approach for intrusion detection. Expert Systems with Applications, 185(July), 115524. https://doi.org/10.1016/j.eswa.2021.115524
Kiranyaz, S., Ince, T., Iosifidis, A., & Gabbouj, M. (2020). Operational neural networks. Neural Computing and Applications, 32(11), 6645–6668. https://doi.org/10.1007/S00521-020-04780-3
Kowsher, M., Tahabilder, A., Islam Sanjid, M. Z., Prottasha, N. J., Uddin, M. S., Hossain, M. A., & Kader Jilani, M. A. (2021). LSTM-ANN & BiLSTM-ANN: Hybrid deep learning models for enhanced classification accuracy. Procedia Computer Science, 193, 131–140. https://doi.org/10.1016/j.procs.2021.10.013
Laghrissi, F., Douzi, S., Douzi, K., & Hssina, B. (2021). Intrusion detection systems using long short-term memory (LSTM). Journal of Big Data, 8(1), 65. https://doi.org/10.1186/s40537-021-00448-4
Liao, H. J., Richard Lin, C. H., Lin, Y. C., & Tung, K. Y. (2013). Intrusion detection system: A comprehensive review. Journal of Network and Computer Applications, 36(1), 16–24. https://doi.org/10.1016/j.jnca.2012.09.004
Najafabadi, M. M., Khoshgoftaar, T. M., Calvert, C., & Kemp, C. (2016). Detection of SSH brute force attacks using aggregated netflow data. Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015, 283–288. https://doi.org/10.1109/ICMLA.2015.20
Najafian, Z., Aghazarian, V., & Hedayati, A. (2015). Signature-Based Method and Stream Data Mining Technique Performance Evaluation for Security and Intrusion Detection in Advanced Metering Infrastructures (AMI). International Journal of Computer and Electrical Engineering, 7(2), 128–139. https://doi.org/10.17706/ijcee.2015.v7.879
Otoum, Y., & Nayak, A. (2021). AS-IDS: Anomaly and Signature Based IDS for the Internet of Things. Journal of Network and Systems Management, 29(3). https://doi.org/10.1007/S10922-021-09589-6
Pawlicki, M., Kozik, R., & Chora?, M. (2022). A survey on neural networks for (cyber-) security and (cyber-) security of neural networks. Neurocomputing, 500, 1075–1087. https://doi.org/10.1016/J.NEUCOM.2022.06.002/A_SURVEY_ON_NEURAL_NETWORKS_FOR_CYBER_SECURITY_AND_CYBER_SECURITY_OF_NEURAL_NETWORKS.PDF
Sadasivam, G. K., Hota, C., & Anand, B. (2016). Classification of SSH attacks using machine learning algorithms. 2016 6th International Conference on IT Convergence and Security, ICITCS 2016. https://doi.org/10.1109/ICITCS.2016.7740316
Shah, S. A. R., & Issac, B. (2018). Performance comparison of intrusion detection systems and application of machine learning to Snort system. Future Generation Computer Systems, 80, 157–170. https://doi.org/10.1016/j.future.2017.10.016
Sung, Y., Jang, S., Jeong, Y. S., & Park, J. H. (James J. ). (2020). Malware classification algorithm using advanced Word2vec-based Bi-LSTM for ground control stations. Computer Communications, 153(January), 342–348. https://doi.org/10.1016/j.comcom.2020.02.005
Wang, Y. C., Houng, Y. C., Chen, H. X., & Tseng, S. M. (2023). Network Anomaly Intrusion Detection Based on Deep Learning Approach. Sensors 2023, Vol. 23, Page 2171, 23(4), 2171. https://doi.org/10.3390/S23042171
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