Spam Detection Using Character N-Grams

Kanaris, Ioannis; Kanaris, Konstantinos; Stamatatos, Efstathios

doi:10.1007/11752912_12

Cited by 15 publications

(13 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As seen in Tables , the obtained accuracies are in an acceptable range. The obtained accuracies are acceptable values based on the results reported in the literature . The distinguishable powers of three data sets are strongly related to each of the P L and P R parameters, which are important parameters for detection of micro–macro patterns in email texts.…”

Section: Resultssupporting

confidence: 64%

“…As seen in Table , optimum P L and P R values were ( P L = 1, P R = 7), ( P L = 6, P R = 2) and ( P L = 3, P R = 5) for the TREC, the Spamassasian, and the Ling‐Spam corpus data sets, respectively, while P = 8 . The obtained spam recall (95.2%, 93.2%, and 92.4% for three corpuses), which is an indication of filter effectiveness , and spam precision (95.2%, 93.3%, and 92.5%), which is an indication of filter safety , rates are also acceptable. As a summary, the higher the recall, the fewer spam messages pass, and the higher the precision, the less non‐spam messages blocked.…”

Section: Resultsmentioning

confidence: 81%

See 1 more Smart Citation

A novel approach for spam email detection based on shifted binary patterns

Kaya

Ertuğrul

2016

Security Comm. Networks

View full text Add to dashboard Cite

Advances in communication allow people flexibility to communicate in various ways. Electronic mail (email) is one of the most used communication methods for personal or business purposes. However, it brings one of the most tackling issues, called spam email, which also raises concerns about data safety. Thus, the requirement of detecting spams is crucial for keeping the users safe and saving them from the waste of time while tackling those issues. In this study, an effective approach based on the probability of the usage of the characters that has similar orders with respect to their UTF‐8 value by employing shifted one‐dimensional local binary pattern (shifted‐1D‐LBP) was used to extract quantitative features from emails for spam email detection. Shifted‐1D‐LBP, which can be described as an ordered set of binary comparisons of the center value with its neighboring values, is a content‐based approach to spam detection with low‐level information. To validate the performance of the proposed approach, three benchmark corpora, Spamassasian, Ling‐Spam, and TREC email corpuses, were used. The average classification accuracies of the proposed approach were 92.34%, 92.57%, and 95.15%, respectively. Analysis and promising experimental results indicated that the proposed approach was a very competitive feature extraction method in spam email filtering. Copyright © 2016 John Wiley & Sons, Ltd.

show abstract

Section: Resultssupporting

confidence: 64%

Section: Resultsmentioning

confidence: 81%

A novel approach for spam email detection based on shifted binary patterns

Kaya

Ertuğrul

2016

Security Comm. Networks

View full text Add to dashboard Cite

show abstract

“…This may due to the insufficient number of recognizable features used in classification. In keyword-based methods, thousands of keywords are usually needed to gain satisfactory performance [22,23,30]; while in the proposed approach, only 26 features are used. It may be deduced that using behaviorbased features can sufficiently distinguish spam and ham than keyword-based features do.…”

Section: Experiments IIImentioning

confidence: 99%

Robust classification for spam filtering by back-propagation neural networks using behavior-based features

Tsai

2008

Appl Intell

View full text Add to dashboard Cite

Earlier works on detecting spam e-mails usually compare the contents of e-mails against specific keywords, which are not robust as the spammers frequently change the terms used in e-mails. We have presented in this paper a novel featuring method for spam filtering. Instead of classifying e-mails according to keywords, this study analyzes the spamming behaviors and extracts the representative ones as features for describing the characteristics of e-mails. An back-propagation neural network is designed and implemented, which builds classification model by considering the behavior-based features revealed from e-mails' headers and syslogs. Since spamming behaviors are infrequently changed, compared with the change frequency of keywords used in spams, behavior-based features are more robust with respect to the change of time; so that the behavior-based filtering mechanism outperform keyword-based filtering. The experimental results indicate that our methods are more useful in distinguishing spam e-mails than that of keywordbased comparison.

show abstract

“…Character N-grams as features have been investigated for spam messages in [16], where Support Vector Machine was used as the only classifier. The proposed method gave a precision of 99.60% with a recall of around 98%.…”

Section: Related Researchmentioning

confidence: 99%

What's in a URL

Verma

Das

2017

Proceedings of the 3rd ACM on International Workshop on Security and Privacy Analytics

View full text Add to dashboard Cite

Phishing is an online social engineering attack with the goal of digital identity theft carried out by pretending to be a legitimate entity. The attacker sends an attack vector commonly in the form of an email, chat session, blog post etc., which contains a link (URL) to a malicious website hosted to elicit private information from the victims.We focus on building a system for URL analysis and classification to primarily detect phishing attacks. URL analysis is attractive to maintain distance between the attacker and the victim, rather than visiting the website and getting features from it. It is also faster than Internet search, retrieving content from the destination web site and network-level features used in previous research.We investigate several facets of URL analysis, e.g., performance analysis on both balanced and unbalanced datasets in a static as well as live experimental setup and online versus batch learning.

show abstract

Spam Detection Using Character N-Grams

Cited by 15 publications

References 5 publications

A novel approach for spam email detection based on shifted binary patterns

A novel approach for spam email detection based on shifted binary patterns

Robust classification for spam filtering by back-propagation neural networks using behavior-based features

What's in a URL

Contact Info

Product

Resources

About