Detecting Spambot as an Antispam Technique for Web Internet BBS

International Journal of Intelligent Systems and Applications, 2012

In this paper the develop ment of anti-spam software detecting information attacks is offered. For this purpose it is considered spam filtrat ion system with the multilayered, mu ltivalent architecture, coordinating all ISP's in the country. All users and ISPs of this system involved in spam filtration p rocess. After spam filtering process, saved spam templates are analyzed and classified. This parameterizing of spam temp lates give possibility to define the thematic dependence from geographical. For example, what subjects prevail in spam messages sent from the certain countries? Analyzing origins of spam temp lates fro m spam-base, it is possible to define and solve the organized social networks of spammers. Thus, the offered system will be capable to reveal purposeful in formation attacks if those occur.

Network Security, 2009

The massive increase of spam is posing a very dangerous and serious threat to our email and social networks. It is pertinent and imperative to step up the spam detection approach and strategies in email and social networks. In recent years online spam has become a major problem for the sustainability of the internet globally, Excessive amounts of spam are not only reducing the quality of information available on the email and social networks but also creating concern among the users of email and various social networks. This paper aims to analyze existing research works in spam detection strategies and approaches, state of art, the phenomenon of spam detection, to explore the rudiment of spam detection, to proposed detection scheme and potential online mitigation schemes. The paper will surveys various anti spam strategies for email and social networking. In the literature we have studied that many anti spam strategies have been discovered and work on but they are still open challenges to these different approaches and techniques, while some of them are highlighted in this articles. It is very important to work on spam detection and reposition it for the better of the world..

Journal of Intelligent & Fuzzy Systems, 2019

A massive number of daily spam demands a method of anti-spam filtering that can efficiently detect unsolicited emails. Traditional spam filtering techniques block spam at receiver's side, unable to stop their propagation on the network, resulting in wastage of network bandwidth, processing power, and disk space. To address these issues, this paper proposes a methodology for sender oriented anti-spamming that identifies and filters spam at its origin, thus not only discourages spam but also reduces the resource wastage. To the best of our knowledge, addressing this wastage by spam has not been done before. Along with blocking spam at origin, this research proposes a diagonal hash algorithm to optimize image spam filtering, and email hash token generator to promote email authentication and discourage zombie accounts. Experiments suggest that the proposed technique has the potential to achieve better performance and accuracy compared to the current approaches.

In this paper, we focus on characterizing spamming botnets by leveraging both spam payload and spam server traffic properties. Towards this goal, we developed a spam signature generation framework called AutoRE to detect botnet-based spam emails and botnet membership. AutoRE does not require pre-classified training data or white lists. Moreover, it outputs high quality regular expression signatures that can detect botnet spam with a low false positive rate. Using a three-month sample of emails from Hotmail, AutoRE successfully identified 7,721 botnet-based spam campaigns together with 340,050 unique botnet host IP addresses.

CEUR Workshop Proceedings, vol. 2870, 5th International Conference on Computational Linguistics and Intelligent Systems, Volume I: Main Conference, COLINS 2021, 2021

There are many social media and messengers in use today, because of the situation with the corona virus pandemic the social media have become an integral part of our daily lives, including work activities. However, there is a lot of unnecessary information that comes to users in large quantities, so the problem of dealing with spam messages on social networks and messengers is now very relevant. By spam we mean any messages that a particular user (person, company, etc.) considers unnecessary in a particular text stream. The project is dedicated to solving the scientific problem of detecting spam messages in the text context of any social network or messenger using anti-spam bot that is based on various spam detection algorithms. Four algorithms were implemented and investigated: an algorithm using naive Bayesian classifier, support vector method, multilayer perceptron neural network and convolutional neural network. The main idea is to develop a complex spam detection algorithm for anti-spam bot, which is fast and easy to implement in a messenger (social network). We propose to use the application of the obtained solutions for IT companies. The developed complex algorithm can be used not only to remove spam, but also, for example, to monitor chats for messages that are important to a particular user.

Journal of Information Engineering and Applications, 2013

Spam is commonly defined as unsolicited email messages, and the goal of spam categorization is to distinguish between spam and legitimate email messages. Our main aim is classification of spam mail and solving various problem is related to web space. So we discuss the measuring parameter which are helpful for reduce the spam or junk mail. In this paper, we describe procedure that can help eliminate unsolicited commercial e-mail, viruses, Trojans, and worms, as well as frauds perpetrated electronically and other undesired and troublesome e-mail.so this research is helpful for best classification and categorization method of email spam detection.

IRJET, 2023

This paper addresses the development of a live chat messaging network that detects and blocks spam bot text messages using advanced algorithms. By preventing spam bots and hackers from infiltrating the system, the goal is to provide users with a secure and private chat environment. Using a combination of machine learning algorithms and natural language processing techniques, the proposed system assesses the content of incoming messages and detects probable spam. With the rapid rise of online communication channels, the issue of spam and unwanted messages has become a serious concern. Hackers and spammers employ automated bots to transmit unwanted and malicious emails to unsuspecting users. As a result, developing a system capable of recognizing and preventing spam bots is crucial.

2010 International Symposium on Collaborative Technologies and Systems, 2010

2008

Compromised computers, known as bots, are the major source of spamming and their detection helps greatly improve control of unwanted traffic. In this work we investigate the behavior patterns of spammers based on their underlying similarities in spamming. To our knowledge, no work has been reported on identifying spam botnets based on spammers' temporal characteristics. Our study shows that the relationship among spammers demonstrates highly clustering structures based on features such as Content length, Time of arrival, Frequency of email, Active Time, Inter-arrival Time, and Content Type. Although the dimensions of the collected feature set is low, we perform Principal Component Analysis (PCA) on feature set to identify the features which account for the maximum variance in the spamming patterns. Further, we calculate the proximity between different spammers and classify them into various groups. Each group represents similar proximity. Spammers in the same group inherit similar patterns of spamming a domain. For classification into Botnet groups, we use clustering algorithms such as Hierarchical and K-means. We identify Botnet spammers into a particular group with a precision of 90%.