Internet Traffic Surveillance & Network Monitoring in India: Case Study of NETRA

Internet traffic surveillance is gaining importance in today’s digital world. Lots of international agencies are putting in efforts to monitor the network around their countries to see suspicious activities and illegal or illegitimate transmission of messages. India, being a center of attraction for terrorist activities, is also working towards the development of such surveillance systems. NETRA or Network Traffic Analysis is one such effort being taken by the Indian Government to filter suspicious keywords from messages in the network. But is it good enough to be used at the highest level for security analysis or does the system design needs to be improved as compared to other similar systems around the world; this question is answered through this study. The comparison of NETRA is done against Dish Fire, Prism, and Echelon. The design of the NETRA scheme and implementation level analysis of the system shows few weaknesses like limited memory options, limited channels for monitoring, pre-set filters, ignoring big data demands, security concerns, social values breach and ignoring ethical issues. These can be covered through alternate options which can improve the existing system. The Inclusion of self-similarity models, Self-Configuring Network Monitoring, and smart monitoring through early intrusion detections can be embedded in the architecture of existing surveillance system to give it more depth and make it more robust.

[1]  Onno J. Boxma.  Fluid Queues and Regular Variation , 1996, Perform. Evaluation.

[2]  J. W. Roberts.  Traffic theory and the Internet , 2001 .

[3]  Francisco Herrera,et al.  On the combination of genetic fuzzy systems and pairwise learning for improving detection rates on Intrusion Detection Systems , 2015, Expert Syst. Appl..

[4]  Saibal K. Pal,et al.  Web Mining and Analytics for Improving E-Government Services in India , 2017 .

[5]  James Won-Ki Hong,et al.  The Architecture of NG-MON: A Passive Network Monitoring System for High-Speed IP Networks , 2002, DSOM.

[6]  Randy H. Katz,et al.  An algebraic approach to practical and scalable overlay network monitoring , 2004, SIGCOMM '04.

[7]  Sebastian Zander,et al.  Automated traffic classification and application identification using machine learning , 2005, The IEEE Conference on Local Computer Networks 30th Anniversary (LCN'05)l.

[8]  Antonia Micalizzi.  If It Was , 2012 .

[9]  Jiawei Han,et al.  Data-Driven Contextual Valence Shifter Quantification for Multi-Theme Sentiment Analysis , 2016, CIKM.

[10]  Marcos K. Aguilera,et al.  Detecting failures in distributed systems with the Falcon spy network , 2011, SOSP.

[11]  George Pavlou,et al.  Exploiting agent mobility for large-scale network monitoring , 2002, IEEE Netw..

[12]  Milton L. Mueller,et al.  The end of the net as we know it? Deep packet inspection and internet governance , 2011, New Media Soc..

[13]  Jeffrey E. Boyd,et al.  Statistical tracking in video traffic surveillance , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[14]  Dong Wei,et al.  IP Traffic Monitoring: An Overview and Future Considerations , 2001, IEEE Pacific Rim Conference on Multimedia.

[15]  M. Turon.  MOTE-VIEW: a sensor network monitoring and management tool , 2005, The Second IEEE Workshop on Embedded Networked Sensors, 2005. EmNetS-II..

[16]  Yonggang Wen,et al.  Network monitoring in multicast networks using network coding , 2005, Proceedings. International Symposium on Information Theory, 2005. ISIT 2005..

[17]  Azer Bestavros,et al.  Self-similarity in World Wide Web traffic: evidence and possible causes , 1996, SIGMETRICS '96.

[18]  J. Ball,et al.  Revealed: How US and UK Spy Agencies Defeat Internet Privacy and Security , 2013 .

[19]  N.D. Georganas,et al.  Self-Similar Processes in Communications Networks , 1998, IEEE Trans. Inf. Theory.

[20]  Saibal K. Pal,et al.  Network Monitoring and Internet Traffic Surveillance System: Issues and Challenges in India , 2016 .

[21]  Saibal K. Pal,et al.  Binary Division Fuzzy C-Means Clustering and Particle Swarm Optimization Based Efficient Intrusion Detection for E-Governance Systems , 2016 .

[22]  Azer Bestavros,et al.  Self-similarity in World Wide Web traffic: evidence and possible causes , 1997, TNET.

[23]  Saibal K. Pal,et al.  Design and Analysis of New Shuffle Encryption Schemes for Multimedia , 2012 .

[24]  Anja Feldmann,et al.  Measurement and analysis of IP network usage and behavior , 2000, IEEE Commun. Mag..

[25]  Sotiris Ioannidis,et al.  Efficient packet monitoring for network management , 2002, NOMS 2002. IEEE/IFIP Network Operations and Management Symposium. ' Management Solutions for the New Communications World'(Cat. No.02CH37327).

[26]  Sushil Jajodia,et al.  Tracking anonymous peer-to-peer VoIP calls on the internet , 2005, CCS '05.

[27]  Sebastian Zander,et al.  Self-Learning IP Traffic Classification Based on Statistical Flow Characteristics , 2005, PAM.

[28]  Damianos Gavalas,et al.  Advanced network monitoring applications based on mobile/intelligent agent technology , 2000, Comput. Commun..

[29]  Brian Tierney,et al.  An infrastructure for passive network monitoring of application data streams , 2003 .