Email spam filters are commonly trained on a sample of recent spam and ham (non-spam) messages. We investigate the effect on filter performance of using samples of spam and ham messages sent months before those to be filtered. Our results show that filter performance deteriorates with the overall age of spam and ham samples, but at different rates. Spam and ham samples of different ages may be ...

It is well known that spammers can forge the header of an email, in particular, the trace information carried in the Received: fields, as an attempt to hide the true origin of the email. Despite its critical importance for spam control and holding accountable the true originators of spam, there has been no systematic study on the forgery behavior of spammers. In this paper, we provide the first...

Spam, the electronic equivalent of junk mail, affects over 600 million users worldwide. Even as anti-spam solutions change to limit the amount of spam sent to users, the senders adapt to make sure their messages are seen. This paper looks at application of the artificial immune system model to protect email users effectively from spam. In particular, it tests the spam immune system against the ...

In this work we are concerned with the detection of spam in video sharing social networks. Specifically, we investigate how much visual content-based analysis can aid in detecting spam in videos. This is a very challenging task, because of the high-level semantic concepts involved; of the assorted nature of social networks, preventing the use of constrained a priori information; and, what is pa...

In this paper we present a detailed study of the behavioral characteristics of spammers based on a two-month email trace collected at a large US university campus network. We analyze the behavioral characteristics of spammers that are critical to spam control, including the distributions of message senders, spam and non-spam messages by spam ratios; the statistics of spam messages from differen...

Email becomes the major source of communication these days. Most humans on the earth use email for their personal or professional use. Email is an effective, faster and cheaper way of communication. The importance and usage for the email is growing day by day. It provides a way to easily transfer information globally with the help of internet. Due to it the email spamming is increasing day by d...

While enjoying the convenience of email communications, many users have also experienced annoying email spam. Even if the current spam detecting approaches have gained a competitive edge against text-based email spam, they still face the challenge arising from imagebased spam (image spam in short). Image spam normally includes embedded images that contain the spam messages in binary format rath...

In this paper, we aim to explore the possibility of Transformer model in detecting spam Short Message Service (SMS) messages by proposing a modified that is designed for SMS messages. The evaluation our proposed performed on Spam Collection v.1 dataset and UtkMl's Twitter Detection Competition dataset, with benchmark multiple established machine learning classifiers state-of-the-art detection a...

A new trend in email spam is the emergence of image spam. Although current anti-spam technologies are quite successful in filtering text-based spam emails, the new image spams are substantially more difficult to detect, as they employ a variety of image creation and randomization algorithms. Spam image creation algorithms are designed to defeat well-known vision algorithms such as optical chara...