Anti-Spam Software for Detecting Information Attacks

International Journal of Engineering Research and, 2020

Nowadays, a big part of people rely on available email or messages sent by the stranger. The possibility that anybody can leave an email or a message provides a golden opportunity for spammers to write spam message about our different interests .Spam fills inbox with number of ridiculous emails. Degrades our internet speed to a great extent .Steals useful information like our details on our contact list. Identifying these spammers and also the spam content can be a hot topic of research and laborious tasks. Email spam is an operation to send messages in bulk by mail .Since the expense of the spam is borne mostly by the recipient ,it is effectively postage due advertising. Spam email is a kind of commercial advertising which is economically viable because email could be a very cost effective medium for sender .With this proposed model the specified message can be stated as spam or not using Bayes' theorem and Naive Bayes' Classifier and Also IP addresses of the sender are often detected .

Spam is one of the major problems of the today's Internet, bringing financial damage to companies and annoying individual users. Spam messages are almost always commercial or fraudulent messages, and are quite often sent with false return address information. Among the approaches developed to stop spam, filtering is an important and popular one. A spam filter is a program that is used to detect unsolicited and unwanted email and prevent those messages from getting to a user's inbox. Like other types of filtering programs, a spam filter looks for certain criteria on which it bases judgments. Researchers have proposed different types of spam filters and this paper provides a review of them.

2008

So fast, so cheap, so efficient, Internet is nowadays incontestably communication mean of choice for personal, business and academic purposes. Unfortunately, Internet has not only this beautiful face. Malicious activities enjoy as well this so fast, cheap and efficient mean. The last decade, Internet worms took the lights. In the recent years, spams are invading one of the most used services of Internet: email. This paper summarizes most of techniques used to filter spams by analyzing the email content. Povzetek: Članek pregledno opisuje metode za filtriranje elektronske pošte. 1

IJRASET, 2021

E-mail is that the most typical method of communication because of its ability to get, the rapid modification of messages and low cost of distribution. E-mail is one among the foremost secure medium for online communication and transferring data or messages through the net. An overgrowing increase in popularity, the quantity of unsolicited data has also increased rapidly. Spam causes traffic issues and bottlenecks that limit the quantity of memory and bandwidth, power and computing speed. To filtering data, different approaches exist which automatically detect and take away these untenable messages. There are several numbers of email spam filtering technique like Knowledge-based technique, Clustering techniques, Learning-based technique, Heuristic processes so on. For data filtering, various approaches exist that automatically detect and suppress these indefensible messages. This paper illustrates a survey of various existing email spam filtering system regarding Machine Learning Technique (MLT) like Naive Bayes, SVM, K-Nearest Neighbor, Bayes Additive Regression, KNN Tree, and rules. Henceforth here we give the classification, evaluation and comparison of some email spam filtering system and summarize the scenario regarding accuracy rate of various existing approaches.

Computing Research Repository, 2004

Unsolicited bulk email (aka. spam) is a major problem on the Internet. To counter spam, several techniques, ranging from spam filters to mail protocol extensions like hashcash, have been proposed. In this paper we investigate the effectiveness of several spam filtering techniques and technologies. Our analysis was performed by simulating email traffic under different conditions. We show that genetic algorithm based spam filters perform best at server level and na -"ve Bayesian filters are the most appropriate for filtering at user level.

Proceedings of the 7th International Conference on Collaborative Computing: Networking, Applications and Worksharing, 2011

Spam has become a main cause of financial loss in most of the organization. It was seen that 81.6% of the email Traffic in 2006 was spam [1]. The loss incurred by the companies is growing exponentially and so is the number of spam emails. This makes spam detection and spam filters are critically important. There are various techniques used in order to filter spam, two of most prominent techniques available are IP blacklisting/white-listing and content-based filtering. An email is identified as spam based on the reputation of the source done by grey listing the source from their previous history [2] . In this paper we introduce a method for improving the spam filters by using a hybrid technique (Content Based & Anomaly based detection approach). In this work, we show how to identify whether the email is spam or not by implementing models that capture the nature of emails' headers and patterns found in the emails' content. The general behavior of spam and legitimate emails for each of these models is obtained and assigned a score; the value of this score is used to differentiate between a legitimate emails and spam. By using this hybrid approach, we were able to detect spam with a false positive rate of .54% and a false negative rate of 1.34%. We also discuss the relation between phishing and spam and how some anti-phishing techniques can be used in spam filters.

Network Security, 2009

E-mail has become popular means for peculiar and professional announcement due to its fast and free availability but several people and companies misuse this facility to distribute unsolicited bulk messages that are commonly called as spam mails. In this paper we will present basics on which spam can be classified, various steps and method to determine whether the mail is spam or not. And we will also study the features of some of the main techniques with proper explain.

2nd International Conference on Dependability of Computer Systems (DepCoS-RELCOMEX '07), 2007

E-mailing as one of the most popular Internet communication service is very prone to spamming, i.e. the process of flooding our mailboxes with unsolicited messages. Every day, separating good messages from those coming from spammers we waste time and resources. It is urgent to find out an effective method of fighting with spam that should take into account not only the currently used spammers' methods but also dynamic aspects of constantly changing spammers' behavior.

Information Security Technical Report, 2007

Spam is a big problem for email users, and more serious for wireless mobile users with limited bandwidth. The battle between spamming and anti-spamming technologies has been going on for many years. Though many advanced antispamming technologies are progressing significantly, spam is still able to bombard many email users. The problem worsens when some anti-spamming methods unintentionally filtered legitimate emails instead! In this paper, we first review existing anti-spam technologies, then propose a layered defense framework using a combination of anti-spamming methods. Under this framework, the server-level defense is targeted for common spam while the client-level defense further filters specific spam for individual users. This layered structure improves on filtering accuracy and yet reduces the number of false positives.