Web Spam is the result of a number of methods to deceive search engine algorithms so as to obtain higher ranks in the search results. Advanced spammers use keyword and link stuffing methods to create farms of spam pages. Most of the recent works in the web spam detection literature utilize graph based methods to enhance the accuracy of this task. This paper is basically a probabilistic approach...

Unsolicited email campaigns remain as one of the biggest threats affecting millions of users per day. Spam filters are capable of detecting and avoiding an increasing number of messages, but researchers have quantified a response rate of a 0.006% [1], still significant to turn a considerable profit. While research directions are addressing topics such as better spam filters, or spam detection i...

Spam content is surging with an explosive increase of user generated content (UGC) on the Internet. Spammers often insert popular keywords or simply copy and paste recent articles from the Web with spam links inserted, attempting to disable content-based detection. In order to effectively detect spam in user generated content, we first conduct a comprehensive analysis of spamming activities on ...

Over the last decade, unsolicited bulk email—spam— has evolved dramatically in its volume, its delivery infrastructure and its content. Multiple reports indicate that more than 90% of all email traversing the Internet today is considered spam. This growth is partially driven by a multi-billion dollar anti-spam industry whose dedication to filtering spam in turn requires spammers to recruit botn...

Spam is a major threat to web security. The web of trust is being abused by the spammers through their ever evolving new tactics for their personal gains. In fact, there is a long chain of spammers who are running huge business campaigns under the web. Spam causes underutilization of search engine resources and creates dissatisfaction among web community. Web Security being a prime challenge fo...

The web spam detection problem was considered in the paper. Based on interconnected spam and no-spam hosts a collective classification approach based on label propagation is aimed at discovering the spam hosts. Each host is represented as network node and links between hosts constitute network’s edges. The proposed method provides reasonable results and is able to compute large data as is settl...

T witter has become one of the most commonly used communication tools in daily life. With 500 million users, Twitter now generates more than 500 million tweets per day. However, its popularity has also attracted spamming. Spammers spread many intensive tweets, which can lure legitimate users to commercial or malicious sites containing malware downloads, phishing, drug sales, scams, and more.1 S...

Spam filtering is one of the most challenging problems in electric message systems. In general, recent studies on specifying real spam source are based on content filtering because spammers usually falsify their origin. We propose a method to specify spam source based on structural analysis with complex network. We assume that each spam sources either has the same victim list or uses the same s...

During the past few years, spam has invaded electronic mail boxes and become one of the major challenges for information systems (IS) professionals. Spam decreases user productivity, erodes electronic mail reliability, and requires the maintenance of spam-filtering software that drains the IS budget. Although spam is the largest type of electronic mail received, spam levels have begun to decrea...

Introduction In this paper we analyze a very large junk e-mail corpus which was generated by a hundred thousand volunteer users of the Hotmail e-mail service. We describe how the corpus is being collected and then discuss how both the products being advertised by spam and the specific exploits being used to avoid spam filters have changed over time. Every day we randomly select one message from...