In this paper, using a year (June 2011 to May 2012) of user reported SMS spam messages together with SMS network records collected from a large US based cellular carrier, we carry out a comprehensive study of SMS spamming. Our analysis shows various characteristics of SMS spamming activities, such as spamming rates, victim selection strategies and spatial clustering of spam numbers. Our analysi...

The main challenge of mining sequential patterns is the high processing cost of support counting for large amount of candidate patterns. For solving this problem, SPAM algorithm was proposed in SIGKDD’2002, which utilized a depth-first traversal on the search space combined with a vertical bitmap representation to provide efficient support counting. According to its experimental results, SPAM o...

This paper proposes a mutual detection mechanism between spam blogs and words with bipartite graph clustering for fi ltering spam blogs from updated blog data. Spam blogs are problematic in extracting useful marketing information from the blogosphere; they often appear to be rich sources of information based on individual opinion and social reputation. One characteristic of spam blogs is copied...

Currently, spam and scams are passive attack over the inbox which can initiated to steal some confidential information, to spread Worms, Viruses, Trojans, cookies and Sometimes they are used for phishing attacks. Spam mails are the major issue over mail boxes as well as over the internet. Spam mails can be the cause of phishing attack, hacking of banking accounts, attacks on confidential data. ...

Spam, also known as Unsolicited Commercial Email (UCE), has been an increasingly annoying problem to individuals and organizations. Most of prior research formulated spam filtering as a classical text categorization task, in which training examples must include both spam emails (positive examples) and legitimate mails (negatives). However, in many spam filtering scenarios, obtaining legitimate ...

The A theory of user expectation of system interaction is introduced in the context of User Adapted Interfaces. The usability of an intelligent email client that learns to filter spam emails is tested under three variants of adaptation: no user modeling, user modeling with fixed (optimal) spam cut-offs, and user modeling with user adjustable spam cut-offs. The results supported our hypothesis t...

In order to protect email users from receiving unsolicited commercial email or spam, anti-spam measures building on technologies, such as filters and block lists, have been deployed widely. However, there is some evidence that certain antispam measures based on the purported origin of the spam cause unintended consequences related to issues of equity of access, which we term digital redlining. ...

Spam as unsolicited e-mail has certainly crossed the border of just being bothersome. In 2003, it surpassed legitimate e-mail – growing to more than 50% of all Internet e-mails. Annually, it causes economic harms of several billion Euros. Fighting spam, beside legal approaches especially technical means are deployed in practical systems, mainly focussing on blocking and filtering mechanisms. Th...

Unsolicited bulk email (spam) is used by cybercriminals to lure users into scams and to spread malware infections. Most of these unwanted messages are sent by spam botnets, which are networks of compromised machines under the control of a single (malicious) entity. Often, these botnets are rented out to particular groups to carry out spam campaigns, in which similar mail messages are sent to a ...

We conducted an empirical study of the clustering behavior of spammers and explored the group-based anti-spam strategies. We propose to block spammers as groups instead of dealing with each spam individually. We empirically observe that, with a certain grouping criteria such as having the same URL in the spam mail, the relationship among the spammers has demonstrated highly clustering structure...