96
Views
1
CrossRef citations to date
0
Altmetric
Computers and Computing

Weibull Distributive Feature Scaling Multivariate Censored Extreme Learning Classification for Malicious IoT Network Traffic Detection

&

REFERENCES

  • B. Jothi and M. Pushpalatha, “WILS-TRS—A novel optimized deep learning based intrusion detection framework for IoT networks,” Pers. Ubiquitous. Comput., pp. 1–17, Jun. 2021.
  • S. Bagui, X. Wang, and S. Bagui, “Machine learning based intrusion detection for IoT botnet,” Int. J. Mach. Learn. Comput., Vol. 11, no. 6, pp. 399–406, Nov. 2021. DOI: 10.18178/ijmlc.2021.11.6.1068
  • O. Salman, I. H. Elhajj, A. Chehab, and A. Kayssi, “A machine learning based framework for IoT device identification and abnormal traffic detection,” Trans. Emerging Telecommun. Technol., Vol. 33, no. 3, pp. e3743, 2019.
  • K. A. da Costa, J. P. Papa, C. O. Lisboa, R. Munoz, and V. H. C. de Albuquerque, “Internet of things: A survey on machine learning-based intrusion detection approaches,” Comput. Netw., Vol. 151, pp. 147–57, Mar. 2019. DOI: 10.1016/j.comnet.2019.01.023
  • R. A. Disha and S. Waheed, “Performance analysis of machine learning models for intrusion detection system using Gini Impurity-based Weighted Random Forest (GIWRF) feature selection technique,” Cybersecurity, Vol. 5, no. 1, p. 1, Jan. 2022. DOI: 10.1186/s42400-021-00103-8
  • S. Gokul Pran and S. Raja, “An efficient feature selection and classification approach for an intrusion detection system using optimal neural network,” J. Intell. Fuzzy Syst., pp. 1–11, (Preprint).
  • G. Sugitha, B. C. Preethi, and G. Kavitha, “Intrusion detection framework using stacked auto encoder based deep neural network in IOT network,” Concurrency Comput. Practice Exper., Vol. 34, no. 28, p. e7401, Dec. 2022. DOI: 10.1002/cpe.7401
  • V. Brindha Devi, K. Johny Elma, S. Rooban, and F. H. Shajin, “Self-attention based progressive generative adversarial network optimized with arithmetic optimization algorithm for kidney stone detection,” Concurrency Comput. Practice Exper., 2023.
  • J. Shu, L. Zhou, W. Zhang, X. Du, and M. Guizani, “Collaborative intrusion detection for VANETs: a deep learning-based distributed SDN approach,” IEEE Trans. Intell. Transp. Syst., Vol. 22, no. 7, pp. 4519–30, 2020.
  • A. Verma and V. Ranga, “Machine learning based intrusion detection systems for IoT applications,” Wirel. Pers. Commun., Vol. 111, no. 4, pp. 2287–310, 2020. DOI: 10.1007/s11277-019-06986-8
  • S. Manimurugan, S. Al-Mutairi, M. M. Aborokbah, N. Chilamkurti, S. Ganesan, and R. Patan, “Effective attack detection in internet of medical things smart environment using a deep belief neural network,” IEEE. Access., Vol. 8, pp. 77396–404, May. 2020. DOI: 10.1109/ACCESS.2020.2986013
  • M. A. Rahman, A. T. Asyhari, L. S. Leong, G. B. Satrya, M. H. Tao, and M. F. Zolkipli, “Scalable machine learning-based intrusion detection system for IoT-enabled smart cities,” Sustainable Cities Soc., Vol. 61, p. 102324, Oct. 2020. DOI: 10.1016/j.scs.2020.102324
  • I. Ullah and Q. H. Mahmoud, “Design and development of a deep learning-based model for anomaly detection in IoT networks,” IEEE. Access., Vol. 9, pp. 103906–26, Jul. 2021. DOI: 10.1109/ACCESS.2021.3094024
  • A. Tewari and B. B. Gupta, “Secure timestamp-based mutual authentication protocol for iot devices using RFID tags,” Int. J. Semant. Web Inf. Syst. (IJSWIS), Vol. 16, no. 3, pp. 20–34, Jul. 2020. DOI: 10.4018/IJSWIS.2020070102
  • A. Mishra, B. B. Gupta, C. H. Hsu, and K. T. Chui, “The early detection of malicious communication with DNS traffic through the use of simple features,” in 2021 IEEE 10th Global Conference on Consumer Electronics (GCCE), Kyoto, Japan, IEEE, Oct. 2021, pp. 291–3.
  • A. Mishra, B. B. Gupta, D. Peraković, F. J. G. Peñalvo, and C. H. Hsu, “Classification based machine learning for detection of DDoS attack in cloud computing,” in 2021 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, IEEE, Jan. 2021, pp. 1–4.
  • K. Alieyan, A. Almomani, M. Anbar, M. Alauthman, R. Abdullah, and B. B. Gupta, “DNS rule-based schema to botnet detection,” Enterp. Inf. Syst., Vol. 15, no. 4, pp. 545–64, Apr. 2021. DOI: 10.1080/17517575.2019.1644673
  • D. E. Salhi, A. Tari, and M. T. Kechadi, “Email classification for forensic analysis by information gain technique,” Int. J. Software Sci. Comput. Intell. (IJSSCI), Vol. 13, no. 4, pp. 40–53, Oct. 2021. DOI: 10.4018/IJSSCI.2021100103
  • R. A. Fiorini, “Computational intelligence from autonomous system to super-smart society and beyond,” Int. J. Software Sci. Comput. Intell. (IJSSCI), Vol. 12, no. 3, pp. 1–13, Jul. 2020.
  • S. Li, D. Qin, X. Wu, J. Li, B. Li, and W. Han, “False alert detection based on deep learning and machine learning,” Int. J. Semant. Web Inf. Syst. (IJSWIS), Vol. 18, no. 1, pp. 1–21, Jan. 2022.
  • A. Aldweesh, A. Derhab, and A. Z. Emam, “Deep learning approaches for anomaly-based intrusion detection systems: A survey, taxonomy, and open issues,” Knowl. Based. Syst., Vol. 189, p. 105124, Feb. 2020. DOI: 10.1016/j.knosys.2019.105124
  • O. Alkadi, N. Moustafa, B. Turnbull, and K. K. R. Choo, “A deep blockchain framework-enabled collaborative intrusion detection for protecting IoT and cloud networks,” IEEE Internet Things J., Vol. 8, no. 12, pp. 9463–72, 2021. DOI: 10.1109/JIOT.2020.2996590
  • Y. Zeng, H. Gu, W. Wei, and Y. Guo, “$Deep-Full-Range$: A deep learning based network encrypted traffic classification and intrusion detection framework,” IEEE. Access., Vol. 7, pp. 45182–90, Apr. 2019. DOI: 10.1109/ACCESS.2019.2908225
  • Q. Chang, X. Ma, M. Chen, X. Gao, and M. Dehghani, “A deep learning based secured energy management framework within a smart island,” Sustainable Cities Soc., Vol. 70, p. 102938, Jul. 2021. DOI: 10.1016/j.scs.2021.102938
  • D. Chen, P. Wawrzynski, and Z. Lv, “Cyber security in smart cities: A review of deep learning-based applications and case studies,” Sustainable Cities Soc., Vol. 66, pp. 102655, Mar. 2020.
  • L. F. Maimó, A. H. Celdrán, M. G. Pérez, F. J. G. Clemente, and G. M. Pérez, “Dynamic management of a deep learning-based anomaly detection system for 5G networks,” J. Ambient. Intell. Humaniz. Comput., Vol. 10, no. 8, pp. 3083–97, Aug. 2019. DOI: 10.1007/s12652-018-0813-4
  • I. F. Akyildiz and A. Kak, “The internet of space things/CubeSats: A ubiquitous cyber-physical system for the connected world,” Comput. Netw., Vol. 150, pp. 134–49, Feb. 2019. DOI: 10.1016/j.comnet.2018.12.017
  • MIT: 1998 DARPA Intrusion Detection Evaluation Dataset. Lincoln Laboratory MIT (1998). Available: https://www.ll.mit.edu/r-d/datasets/1998-darpa-intrusion-detection-evaluation-dataset. Accessed Mar 2019.
  • UCI: KDD Cup 1999 Data. University of California, Irvine (1999). Available: http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html. Accessed Mar 2019.
  • UNB: NSL-KDD dataset. University of New Brunswick (2009). Available: https://www.unb.ca/cic/datasets/nsl.html. Accessed Mar 2019.
  • https://www.kaggle.com/mrwellsdavid/unsw-nb15.
  • B. Hao, N. Lazic, D. Yin, Y. Abbasi-Yadkori, and C. Szepesvari, “Confident least square value iteration with local access to a simulator,” in International Conference on Artificial Intelligence and Statistics, PMLR, May 2022, pp. 2420–35.
  • E. M. Almetwally, H. Z. Muhammed, and E. S. A. El-Sherpieny, “Bivariate Weibull distribution: properties and different methods of estimation,” Ann. Data Sci., Vol. 7, no. 1, pp. 163–93, Mar. 2020. DOI: 10.1007/s40745-019-00197-5
  • T. B. Mattos, V. H. Lachos, L. M. Castro, and L. A. Matos, “Extending multivariate Student’s-t t semiparametric mixed models for longitudinal data with censored responses and heavy tails,” Stat. Med., vol. 41, no.19, pp. 3696-719, 2022.
  • 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, pp. 1–20, Dec. 2020. DOI: 10.1186/s40537-020-00379-6
  • S. Choudhary and N. Kesswani, “Analysis of KDD-Cup’99, NSL-KDD and UNSW-NB15 datasets using deep learning in IoT,” Procedia. Comput. Sci., Vol. 167, pp. 1561–73, Jan. 2020. DOI: 10.1016/j.procs.2020.03.367
  • S. Latif, Z. Idrees, Z. Zou, and J. Ahmad, “DRaNN: A deep random neural network model for intrusion detection in industrial IoT,” in 2020 on UK-China Emerging Technologies (UCET), Glasgow, UK, IEEE, Aug. 2020, pp. 1–4.
  • M. Zeeshan, Q. Riaz, M. A. Bilal, M. K. Shahzad, H. Jabeen, S. A. Haider, and A. Rahim, “Protocol-Based deep intrusion detection for DoS and DDoS attacks using UNSW-NB15 and Bot-IoT data-sets,” IEEE. Access, Vol. 10, pp. 2269–83, Dec. 2021. DOI: 10.1109/ACCESS.2021.3137201
  • V. Kumar, A. K. Das, and D. Sinha, “UIDS: A unified intrusion detection system for IoT environment,” Evol. Intell., Vol. 14, no. 1, pp. 47–59, Mar. 2021. DOI: 10.1007/s12065-019-00291-w
  • F. B. Iqbal, S. Biswas, and R. Urba. “Performance analysis of intrusion detection systems using the PyCaret machine learning library on the UNSW-NB15 dataset,” Doctoral dissertation, Brac University. Jan. 2021.
  • Z. E. Huma, et al., “A hybrid deep random neural network for cyberattack detection in the industrial internet of things,” IEEE Access, Vol. 9, pp. 55595–605, Apr. 2021. DOI: 10.1109/ACCESS.2021.3071766

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.