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Detecting Spambot as Associate Degree Antispam Technique for Net Bulletin Board System

Sachin Garg

Abstract


Spam is one in every of the foremost widespread and conjointly the most relevant topic that must be understood within the current state of affairs. Everybody whether or not it's going to be a little kid or associate degree senior citizen square measure exploitation emails everyday all round the world. The state of affairs that we have a tendency to square measure seeing is that just about no one is aware or they do not recognize what actually the spam is and what they're going to waste in their systems. Spam generally means uninvited or unwanted mails. Botnets square measure thought-about one in every of the most supply of the spam. Botnet means the cluster of softwares known as bots and the function of those bots is to run on many compromised computers autonomously and mechanically. The main objective of this paper is to observe such a larva or spambots for the Bulletin Board System (BBS). BBS could be a laptop that's running software system that enables users to depart a message and access info of general interest. Originally BBSes were accessed solely over a connective employing an electronic equipment, but nowadays some BBSes allowed access via a Telnet, packet switched network, or packet radio association. The main methodology that we have a tendency to square measure planning to focus is on Behavioral-based Spam Detection (BSD) technique. Behavioral-based Spam Detector (BSD) combines many behaviors of the spam bots at completely different stages together with the behavior of spam preparation before the spam session once the spammers seek for associate degree open relay SMTP service to send e-mails through, and also the behavior of spammers whereas connecting to the mail server. Detective work on the abnormal behavior made by the spam activities provides a high rate of suspicion on the existence of bots.

Keywords: Spam, Botnet detection, detection, network worm detection

 

Cite this Article
Sachin Garg. Detecting Spambot as Associate Degree Antispam Technique for Net Bulletin Board System. Journal of Operating Systems Development & Trends. 2015; 2(1): 26–30p.


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References


Introduction to the Realtime Blackhole List (RBL). MAPS-Global Secure Systems, [website]. Nov 28, 2008. Available: http://www.mailabuse.com/wp_introrbl.html.

Zhaosheng Z, Guohan L, Yan C, et al. Botnet Research Survey. 32nd Annual IEEE International Computer Software and Applications, COMPSAC '08. 2008; 967–972p.

Miao Y, Qiu-Xiang J, Fan-Jin M. The Spam Filtering Technology Based on SVM and D-S Theory. First International Workshop on Knowledge Discovery and Data Mining, 2008. WKDD. 2008; 562–565p.

Qiong R, Yi M, Susilo W. SEFAP: An Email System for Anti-Phishing. ICIS 6th IEEE/ACIS International Conference on Computer and Information Science. 2007; 782–787p.

Joonmo Hong, Boo Joong Kang, Eul Gyu Im. Adaptable Anti-Spam Technique for the Internet Web BBS. Division of Computer Science & Engineering, Hanyang University, Seoul. 133–791p.

Sauver JS. Spam Zombies and Inbound Flows to Compromised Customer Systems. AAWG.

Mohammed Fadhil Zamil, Manasrah Ahmed M, Omar Amir, et al. A Behavior based Algorithm to Detect Spam Bots. National Advanced IPv6 Center, Universiti Sains Malaysia.


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