ICTACT Journal on Communication Technology

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

An Ensemble Neural Network Technique for Improving Security Among Various Domains of Information Technology


Affiliations
1 PG Department of Computer Applications, Bishop Heber College, Affiliated to Bharathidasan University, India., India
     

   Subscribe/Renew Journal


In the era of Internet of Things (IoT), enterprise information Systems (IISs) are becoming increasingly valuable in a range of industries due to the fact that they constitute a network in which connected devices exchange data in an environment that is quite close to real time. In this context, enterprises are provided with the opportunity to make use of virus detection solutions that are either static, dynamic, or hybrid. The research uses ensemble machine learning approaches that have been implemented and are analyzed, and comparisons are drawn between them. The findings of this research have been effective in the identification of malwares in IIS.

Keywords

Ensemble Neural Network, Security, Malware, Information Technology.
Subscription Login to verify subscription
User
Notifications
Font Size

  • Chris McNab, “Network Security Assessment”, 2 nd Edition, O’Reilly Media, 2007.

  • Haifeng Wu, “Research of Network security Assessment System Based on Vulnerability Scan”, Proceedings of International Conference on Advanced Computer Control, pp. 566-569, 2011.

  • P. Jayashree, “Security Issues in Protecting Computers and Maintenance”, Journal of Global Research in Computer Science, Vol. 4, No. 1, pp. 55-58, 2013.

  • K. Praghash and T. Karthikeyan, “Data Privacy Preservation and Trade-off Balance Between Privacy and Utility using Deep Adaptive Clustering and Elliptic Curve Digital Signature Algorithm”, Wireless Personal Communications, Vol. 78, 1-16, 2021.

  • J. Kim and J.H. Yi, “MAPAS: A Practical Deep Learningbased Android Malware Detection System”, International Journal of Information Security, Vol. 21, No. 4, pp. 725-738, 2022.

  • M.E.Z.N. Kamba and K. Taghva, “A Survey on Mobile Malware Detection Methods using Machine Learning”, Proceedings of Annual Workshop and Conference on Computing and Communication, pp. 215-221, 2022.

  • X. Song and Y. Wang, “Homomorphic Cloud Computing Scheme based on Hybrid Homomorphic Encryption”, Proceedings of International Conference on Computer and Communications, pp. 13-16, 2017.

  • D.R. Kumar Raja and S. Pushpa, “Diversifying Personalized Mobile Multimedia Application Recommendations through the Latent Dirichlet Allocation and Clustering Optimization”, Multimedia Tools and Applications, pp. 1- 20, 2019.

  • C. Paar and J. Pelzl, “Understanding Cryptography”, Springer, 2010.

  • Madiha Khalid, Umar Mujahid and Najam-Ul-Islam Muhammad, “Ultralightweight RFID Authentication Protocols for Low-Cost Passive RFID Tags”, Security and Communication Networks, Vol. 2019, pp. 1-25, 2019.

  • C. Li and Y. Qiao, “A Novel Deep Framework for Dynamic Malware Detection based on API Sequence Intrinsic Features”, Computers and Security, Vol. 116, pp. 102686- 102698, 2022.

  • J.Y. Kim and S.B. Cho, “Obfuscated Malware Detection using Deep Generative Model based on Global/Local Features”, Computers and Security, Vol. 112, pp. 102501- 102513, 2022.

  • Jong Sik Moon and Im-Yeong Lee, “An AAA Scheme using ID-Based Ticket with Anonymity in Future Mobile Communication”, Computer Communications, Vol. 34, No. 3, pp. 295-304, 2011.


Abstract Views: 155

PDF Views: 0




  • An Ensemble Neural Network Technique for Improving Security Among Various Domains of Information Technology

Abstract Views: 155  |  PDF Views: 0

Authors

R. Thamarai Selvi
PG Department of Computer Applications, Bishop Heber College, Affiliated to Bharathidasan University, India., India

Abstract


In the era of Internet of Things (IoT), enterprise information Systems (IISs) are becoming increasingly valuable in a range of industries due to the fact that they constitute a network in which connected devices exchange data in an environment that is quite close to real time. In this context, enterprises are provided with the opportunity to make use of virus detection solutions that are either static, dynamic, or hybrid. The research uses ensemble machine learning approaches that have been implemented and are analyzed, and comparisons are drawn between them. The findings of this research have been effective in the identification of malwares in IIS.

Keywords


Ensemble Neural Network, Security, Malware, Information Technology.

References