Nidhi Sood

Bio: Nidhi Sood is an academic researcher. The author has contributed to research in topics: Spambot & Bag-of-words model. The author has an hindex of 1, co-authored 1 publications receiving 10 citations.

Spam Detection System Using Hidden Markov Model

Abstract: Spams are the textual context of the system which can damage our system. E-mail is an essential communication tool that has been greatly abused by spam sender to disseminate unwanted information and spread malicious contents to Internet users. Spam filters provide better protective mechanisms that are able to design a system to recognize the spams. We propose an image spam detection system that uses detect spam words. We rely on filtering methods to detect stemming words of spam images and then use Hidden Markov Model of spam filters to detect all the spam images. In the first section of the paper analysis the Introduction of Spam. In the second section of the paper described the related work. In the third section analyzed the problem formulation. In the fourth section described spam detection techniques, different steps for spam detection. In the fifth section described methodology of spam detection and then the different spam feature extraction. Finally present the Conclusion & future works with the references.

E-Mail Spam Detection Using SVM and RBF

Abstract: In today‘s life internet is an important part. We spend most of our time on internet. One of the important features of internet is communication. Email is a mode of communication which is used for the personal and business purpose. Spam emails are the emails recipient does not wish to take delivery of; it is also called unwanted bulk email. Emails are used each day by number of user to converse around the world. At present large volumes of spam emails are reasoning serious trouble for Internet user and Internet service. Such as it degrade user investigate knowledge, it assists transmission of virus in network, it increases load on network traffic. It also misuses user time, and energy for legal emails among the spam. For evade spam there are so many conventional anti-spam technique includes Bayesian based sort, rule based system, IP blacklist, Heuristic based filter, White list and DNS black holes. These methods are based on satisfied of the post or links of the mail. In this paper we proposed an efficient spam filtering technique based on neural network. The technique used is RBF a neural network technique in which neuron are trained. The results obtained by using this technique are compared with SVM. The parameter meter for comparison is precision and accuracy. On the basis of these two parameters we compared the proposed technique with SVM.

E-Mail Spam Detection and Classfication Using SVM and Feature Extraction

Abstract: Today emails have become to be a standout amongst the most well-known and efficient types of correspondence for Internet clients. Hence because of its fame, the email will be misused. One such misuse is the posting of unwelcome, undesirable messages known as spam or junk messages. Email spam has different consequences. It diminishes productivity, consumes additional space in mail boxes, additional time, expand programming damaging viruses, and materials that contains conceivably destructive data for Internet clients, destroy stability of mail servers, and subsequently clients invest lots of time for sorting approaching mail and erasing undesirable correspondence. So there is a need of spam detection so that its outcomes can be reduced. In this paper, propose a novel method for email spam detection using SVM and feature extraction which achieves accuracy of 98% with the test datasets.

A Review on Different Spam Detection Approaches

Abstract: Email is one of the crucial aspects of web data communication. The increasing use of email has led to a lucrative business opportunity called spamming. A spam is an unwanted data that a web user receives in the form of email or messages. This spamming is actually done by sending unsolicited bulk messages to indiscriminate set of recipients for advertising purpose. These spams messages not only increases the network communication and memory space but can also be used for some attack. This attack can be used to destroy user's information or reveal his identity or data. In this paper we discuss some approaches for spam detection.

Stochastic Methods to Find Maximum Likelihood for Spam E-mail Classification

Abstract: The increasing volume of unsolicited bulk e-mails leads to the need for reliable stochastic spam detection methods for the classification of the received sequence of e-mails. When a sequence of emails is received by a recipient during a time period, the spam filters have already classified them as spam or not spam. Due to the dynamic nature of the spam, there might be emails marked as not spam but are actually real spams and vice versa. For the sake of security, it is important to be able to detect real spam emails. This paper utilizes stochastic methods to refine the preliminary spam detection and to find maximum likelihood for spam e-mail classification. The method is based on the Bayesian theorem, hidden Markov model (HMM), and the Viterbi algorithm.

A survey on spam detection techniques

Abstract: Today e-mails have become one of the most popular and economical forms of communication for Internet users. Thus due to its popularity, the e-mail is going to be misused. One such misuse is the posting of unwelcome, unwanted e-mails known as spam or junk e-mails (1). E-mail spam has various consequences. It reduces productivity, takes extra space in mail boxes, extra time, extend software damaging viruses, and materials that contains potentially harmful information for Internet users, destroy stability of mail servers, and as a result users spend lots of time for sorting incoming mail and deleting unwanted correspondence. So there is a need of spam detection so that its consequences can be reduced (2). In this paper, we present various spam detection techniques.