Performance analysis of Mail Clients using SNORT

International Journal of Computer & Organization Trends  (IJCOT)          
© 2016 by IJCOT Journal
Volume - 6 Issue - 4
Year of Publication : 2016
Authors :  Mr. K Sreerama Murthy, Dr. S Pallam Setty, Dr. G S V P Raju
DOI : 10.14445/22492593/IJCOT-V35P301


Mr. K Sreerama Murthy, Dr. S Pallam Setty, Dr. G S V P Raju " Performance analysis of Mail Clients using SNORT ", International Journal of Computer & organization Trends (IJCOT), V6(4):39-45 Jul - Aug 2016, ISSN:2249-2593, Published by Seventh Sense Research Group.

Abstract Intrusion detection system (IDS) monitor network traffic for mistrustful activity and alerts the system or network administrator, and may also take actions such as blocking the user or source IP address from accessing the network. SNORT acts as not only IDS but also can be configured as IPS for watching and interference of security attacks on networks. In our case, we used Snort for analysing performance of different mail clients by varying the text sizes from 50 KB to 2 MB and analysed the metrics (run time, analysed packets and total packets). From simulation scenario, we found that Hotmail is best for sending larger text and Yahoo should be less preferred for the same purpose.


[1] Ahmed Patel, Qais Qassim, Christopher Wills. A survey of intrusion detection and prevention systems, Information Management & Computer Security Journal (2010).
[2] Oludele Awodele, Sunday Idowu, Omotola Anjorin, and Vincent J. Joshua, A Multi-Layered Approach to the Design of Intelligent Intrusion Detection and Prevention System (IIDPS), Babcock University, (Volume 6, 2009).
[3] Host Intrusion Prevention Systems and Beyond, SANS Institute (2008).
[4] Intrusion Detection and Prevention In-sourced or Outsourced, SANS Institute (2008).
[5] Mario Guimaraes, Meg Murray. Overview of Intrusion Detection and Intrusion Prevention, Information security curriculum development Conference by ACM (2008).
[6] Muhammad Awais Shibli, Sead Muftic. Intrusion Detection and Prevention System using Secure Mobile Agents, IEEE International Conference on Security & Cryptography (2008).
[7] David Wagner, Paolo Soto. Mimicry Attacks on Host Based Intrusion Detection Systems, 9 th ACM Conference on Computer and Communications Security (2002).
[8] Harley Kozushko. Intrusion Detection: Host-Based and Network-Based Intrusion Detection Systems, (2003).
[9] Lin Tan, Timothy Sherwood. A High Throughput String Matching Architecture for Intrusion Detection and Prevention, Proceedings of the 32 nd Annual International Symposium on Computer Architecture (ISCA 2005).
[10] S. Mrdovic, E. Zajko. Secured Intrusion Detection System Infrastructure, University of Sarajevo/Faculty of Electrical Engineering, Sarajevo, Bosnia and Herzegovina (ICAT 2005).
[11] Yeubin Bai, Hidetsune Kobayashi. Intrusion Detection Systems: technology and Development, 17 th International Conference of Advanced Information Networking and Applications, (AINA 2003).
[12] Sang-Jun Han and Sung-Bae Cho. Combining Multiple Host-Based Detectors Using Decision Tree, Australian Joint Artificial Intelligence Conference, (AUSAI 2003).
[13] Ramaprabhu Janakiraman, Marcel Waldvogel, Qi Zhang. Indra: A peer-to-peer approach to network intrusion detection and prevention, Enabling Technologies: Infrastructure for Collaborative Enterprises, WET ICE 2003.
[14] M. Laureano, C. Maziero1, E. Jamhour. Protecting Host-Based Intrusion Detectors through Virtual Machines, The International Journal of Computer and Telecommunications Networking (2007).
[15] Matt Carlson and Andrew Scharlott. Intrusion detection and prevention systems, (2006).