Comprehensive Review Next-Generation Intrusion Prevention System (NGIPS) Based Cyber Attacks Classification and Challenges Using Machine Learning Techniques


Authors : Katikam Mahesh; Dr. Kunjam Nageswara Rao

Volume/Issue : Volume 9 - 2024, Issue 10 - October

Google Scholar : https://tinyurl.com/5n7u5zd5

Scribd : https://tinyurl.com/58w27mbm

DOI : https://doi.org/10.38124/ijisrt/IJISRT24OCT1440

PlumX Metrics

Semantic Scholar

ResearchGate

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : At present, nearly all of international interactions in commerce, economics, culture, social interaction, and government at all level involving individuals, non-governmental organizations, authorities, and governmental institutions take occur online. Cyberattacks and hazards related to technology for wireless communication have become major issues for numerous government agencies and private businesses worldwide in recent times. Today's society relies heavily on electronic technology, and protecting this data against cyberattacks is a challenging issue. The motive behind cyberattacks is to financially harm companies. Next-Generation Intrusion Prevention System (NGIPS) keeps an eye on devices and network traffic for known suspicious tasks, suspect activity by alerting security administrators about known or potential dangers, or by sending alerts to a centralized security tool, an IDS can assist speed up and automate network threat Classification and Detection. In this paper Presenting Cyber Attacks Classification using Various Machine Learning techniques with Datasets and Accuracy.

Keywords : Cyber Attacks, Classification, Next-Generation Intrusion Prevention System, An Intrusion Detection System, Accuracy, Dataset.

References :

  1. Chen, Y.; Yuan, F. Dynamic detection of malicious intrusion in wireless network based on improved random forest algorithm. In Proceedings of the 2022 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC), Dalian, China, 14–16 April 2022; pp. 27–32. [CrossRef]
  2. Gu, J.; Lu, S. An effective intrusion detection approach using SVM with naïve Bayes feature embedding. Comput. Secur. 2021, 103, 102158. [CrossRef]
  3. Chen, L.; Kuang, X.; Xu, A.; Suo, S.; Yang, Y. A Novel Network Intrusion Detection System Based on CNN. In Proceedings of the 2020 Eighth International Conference on Advanced Cloud and Big Data (CBD), Taiyuan, China, 5–6 December 2020; pp. 243–247. [CrossRef]
  4. Sharafaldin, I.; Lashkari, A.H.; Ghorbani, A. Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization; Canadian Institute for Cybersecurity (CIC): Fredericton, NB, Canada, 2018; pp. 108–116.
  5. Manu Bijone, and Jitendra Dangra, “A Survey of Signature Based & Statistical Based Intrusion Detection Techniques”, IJSRD - International Journal for Scientific Research & Development, Vol. 4, Issue 08, pp. 583-585, 2016.
  6. A. Sawant, J. Yadav, A. K. Arora, J. Deo, and N. Dhange, “Intrusion Detection System using Data Mining,” vol. 4, no. 2, pp. 4–7, 2015
  7. S. Sharma and R. K. Gupta, “Intrusion Detection System: A Review,” vol. 9, no. 5, pp. 69–76, 2015.
  8. Kuang, F., Xu, W., and Zhang, S., “A novel hybrid KPCA and SVM with GA model for intrusion detection”, Applied Soft Computing, vol. 18, pp.178-184, 2014.
  9. Ahmad, I., Hussain, M., Alghamdi, A., and Alelaiwi, A., “Enhancing SVM performance in intrusion detection using optimal feature subset selection based on genetic principal components” Neural Computing and Applications, 24(7-8), pp.1671-1682, 2014.
  10. F.N. Sabri, N.M. Norwawi, K. Seman, “Identifying false alarm rates for intrusion detection system with Data Mining”, IJCSNS International Journal of Computer Science and Network Security, vol.11, 2011.
  11. Zorana Bankovic, Slobodan Bojanic, Octavio Nieto-Taladriz, and Atta Badii, “Increasing Detection Rate of User-to-Root Attacks Using Genetic Algorithms”, International Conference on Emerging Security Information, Systems, and Technologies, IEEE, 2007.
  12. Jian P., Shambhu U., Faisal F., Venugopal G., “Data Mining for Intrusion Detection – Techniques, Applications and Systems”, Data Mining Techniques for Intrusion Detection and Computer Security, University at Buffalo, New York, 2004.

At present, nearly all of international interactions in commerce, economics, culture, social interaction, and government at all level involving individuals, non-governmental organizations, authorities, and governmental institutions take occur online. Cyberattacks and hazards related to technology for wireless communication have become major issues for numerous government agencies and private businesses worldwide in recent times. Today's society relies heavily on electronic technology, and protecting this data against cyberattacks is a challenging issue. The motive behind cyberattacks is to financially harm companies. Next-Generation Intrusion Prevention System (NGIPS) keeps an eye on devices and network traffic for known suspicious tasks, suspect activity by alerting security administrators about known or potential dangers, or by sending alerts to a centralized security tool, an IDS can assist speed up and automate network threat Classification and Detection. In this paper Presenting Cyber Attacks Classification using Various Machine Learning techniques with Datasets and Accuracy.

Keywords : Cyber Attacks, Classification, Next-Generation Intrusion Prevention System, An Intrusion Detection System, Accuracy, Dataset.

Video Explanation for Published paper

Never miss an update from Papermashup

Get notified about the latest tutorials and downloads.

Subscribe by Email

Get alerts directly into your inbox after each post and stay updated.
Subscribe
OR

Subscribe by RSS

Add our RSS to your feedreader to get regular updates from us.
Subscribe