ICTACT Journal on Communication Technology

Open Access Open Access  Restricted Access Subscription Access

DETECTION OF MALEVOLENT NODES IN INTERNET OF THINGS NODES USING A TRUST BEHAVIOURAL FRAMEWORK


Affiliations
1 Government College of Engineering, Thirussur, India
2 IES College of Engineering, India
 

   Subscribe/Renew Journal


Secured internet routing in IoT has been an important study since last decade, but it has seriously threatened the data protection due to the impact of malicious nodes. An effective mechanism is therefore essential, since most of them are vulnerable to attack, to detect and prevent malicious nodes in IoT. A Trust Framework (TF) for improving diagnosis and preventing malignant nodes in IoTs is suggested in this article. This system monitors the disruptive activity of nodes in the network during agility and connectivity. It helps prevent the malicious node from affecting the packets that are run on the level of the confidence. This identification and avoidance helps to enhance packet routing with high privacy between IoT nodes.

Keywords

IoT, Trust, Degree of Trust, Malicious Attack.
Subscription Login to verify subscription
User
Notifications
Font Size

  • Changhua Sun, Lei Shi, Chengchen Hu and Bin Liu, “DRRSFF: A Practical Scheduling Algorithm to Improve the Performance of Short Flows”, Proceedings of 3rd International Conference on Networking and Services, pp. 1-13, 2007.

  • K. Chokkanathan and S. Koteeswaran, “An Integrated Approach for Network Traffic Analysis using Unsupervised Clustering and Supervised Classification”, International Journal of Internet Technology and Secured Transactions, Vol. 9, No. 4, pp. 1-12, 2019.

  • S. Dhivya and P. Shanmugaraja, “A Study of Internet Traffic Classification using Machine Learning Algorithms”, International Research Journal of Engineering and Technology, Vol. 2, pp. 154-157, 2015.

  • C. Gu, S. Zhuang, Y. Sun and J. Yan, “Multi-Levels Traffic Classification Technique”, Proceedings of International Conference on Future Computer and Communication, Vol. 1, pp. 448-452, 2010.

  • L. Jeong Hwa and P. Jung Min, “Dynamic Scheduling for Usable Service in Network Robot”, Proceedings of International Conference on Internet and Data Structure, pp. 252-259, 2013.

  • C. Livadas R.Walsh, D. Lapsley and W.T. Strayer, “Using Machine Learning Techniques to Identify Botnet Traffic”, Proceedings of IEEE International Conference on Local Computer Networks, pp. 967-974, 2006.

  • T.T.T. Nguyen and G. Armitage, “A Survey of Techniques for Internet Traffic Classification using Machine Learning”, IEEE Communications Surveys and Tutorials, Vol. 10, No. 4, pp. 56-76, 2008.

  • Shang Ning, Tan Minggui, Wang Yaqing, Chui Zhongfa, Chui Yan and Yang Yong, “A Bp Neural Network Method for Short-term Traffic Flow”, Computer Applications and Software, Vol. 23, No. 2, pp. 32-33, 2006.

  • M.N. Kasirian and R.M. Yusuff, “An Integration of a Hybrid Modified TOPSIS with a PGP Model for the Supplier Selection with Interdependent Criteria”, International Journal on Production Research, Vol. 51, No. 4, pp. 1037- 1054, 2013.

  • Hussein A. Mohammed, DAdnan Hussein Ali and Hawraa Jassim Mohammed, “The Effects of Different Queuing Algorithms within the Router on QoS VoIP and Communications”, International Journal of Computer Networks and Communications, Vol. 5, No. 1, pp. 1-15, 2013.

  • T. Karagiannis, A. Broido, M. Faloutsos and K.C. Claffy, “Transport Layer Identification of P2P Traffic”, Proceedings of International Conference on Internet Measurement, pp. 121-134, 2005.

  • S. Kumar, J. Turner and J. Williams, “Advanced Algorithms for Fast and Scalable Deep Packet Inspection”, Proceedings of ACM/IEEE Symposium on Architecture for Networking and Communications Systems, pp. 81-92, 2006.

  • S. Kumar, S. Dharmapurikar and F. Yu, “Algorithms to Accelerate Multiple Regular Expressions Matching for Deep Packet Inspection”, ACM SIGCOMM Computer Communication Review, Vol. 36, No. 4, pp. 339-350, 2006.

  • S. Kandula, S. Sengupta and R. Chaiken, “The Nature of Data Center Traffic: Measurements and Analysis”, Proceedings of ACM Conference on Internet Measurement, pp. 202-208, 2009.

  • P. Shanmugaraja, K. Chokkanathan, J. Anitha, A. Parveen Begam and N. Naveenkumar, “Dynamic Packet Scheduler for Queuing Real Time and Non-Real Time Internet Traffic”, International Journal of Recent Technology and Engineering, Vol. 8, No. 3, pp. 1-12, 2019


Abstract Views: 323

PDF Views: 129




  • DETECTION OF MALEVOLENT NODES IN INTERNET OF THINGS NODES USING A TRUST BEHAVIOURAL FRAMEWORK

Abstract Views: 323  |  PDF Views: 129

Authors

P K Swaraj
Government College of Engineering, Thirussur, India
G Kiruthiga
IES College of Engineering, India

Abstract


Secured internet routing in IoT has been an important study since last decade, but it has seriously threatened the data protection due to the impact of malicious nodes. An effective mechanism is therefore essential, since most of them are vulnerable to attack, to detect and prevent malicious nodes in IoT. A Trust Framework (TF) for improving diagnosis and preventing malignant nodes in IoTs is suggested in this article. This system monitors the disruptive activity of nodes in the network during agility and connectivity. It helps prevent the malicious node from affecting the packets that are run on the level of the confidence. This identification and avoidance helps to enhance packet routing with high privacy between IoT nodes.

Keywords


IoT, Trust, Degree of Trust, Malicious Attack.

References