On the effectiveness of IP reputation for spam filtering

Esquivel, Holly; Mori, Tatsuya

doi:10.1109/comsnets.2010.5431981

Cited by 25 publications

(8 citation statements)

References 15 publications

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“…It is used in email spam detection methods [11], where a list of IP addresses and corresponding reputation score is maintained and used to identify spam email. It is used in mobile ad hoc networks [10] to identify misbehaving nodes and also in peer to peer networks to classifying good and bad peers.…”

Section: Reputation Based Analysismentioning

confidence: 99%

RAPID: Reputation based approach for improving intrusion detection effectiveness

Thomas¹

2010

2010 Sixth International Conference on Information Assurance and Security

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Reducing false positives have been one of the toughest challenges and a very practical problem in real life deployments of intrusion detection systems. It leads to decreased confidence in the IDS alerts. The security analyst is faced with the choice between disabling valuable signatures that also generate false positives on one hand, and missing true alerts among the flood of false positives on the other hand. In this paper we present an architecture that utilizes IP reputation along with signature levels in order to reduce false positives and thereby increase the effectiveness of the IDS. In the proposed approach the IDS signatures are classified and grouped into various levels based on their false positive rating, and the incoming traffic is analyzed by one or more of the signature levels based on the reputation of the IP addresses. We also discuss a prototype implementation of the proposed approach that is based on open source IDS -Snort. Evaluation showed promising results in reducing false positives and corresponding improvement in Bayesian detection rate for the prototype system as compared to Snort.

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Section: Reputation Based Analysismentioning

confidence: 99%

RAPID: Reputation based approach for improving intrusion detection effectiveness

Thomas¹

2010

2010 Sixth International Conference on Information Assurance and Security

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“…This has essentially become an escalating arms race between spammers and security professionals. Anti-spam technologies range from IP addressbased techniques [30] to machine learning approaches [31,32]. However, anti-spam efforts are often overcome by strategic innovations by spammers.…”

Section: Introductionmentioning

confidence: 99%

Cross-country analysis of spambots

Garg

Koster

Camp

2013

EURASIP J. on Info. Security

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Spam is a vector for cybercrime and commonly legally prohibited. Why do certain national jurisdictions produce a higher percentage of spam than others despite its prohibition? Why do some countries have a higher percentage of systems acting as spambots compared to other countries? We begin to answer there questions by conducting a cross-country empirical analysis of economic factors that correlate with the prevalence of spam and associated botnets. The economic factors under consideration are grounded in traditional theories of crime offline, as well as prior research in security economics. We found that more than 50% of spam can be attributed to having originated from merely seven countries, indicating that deterrence through policy is both feasible and economically rational. As expected, higher Internet adoption is correlated with higher percentage of spam from a country. Counterintuitively, Internet adoption is also positively correlated with the percentage of infected machines.

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“…A pre-acceptance test of emails has been discussed by Esquivel et al [40], which works by analysing the features of individual SMTP transactions such as EHLO/HELO message sequences. These can be further divided into different categories based on the working mechanism such as 'Protocol Defects'.…”

Section: B Propositions Based On Architectural Modificationmentioning

confidence: 99%

A Comprehensive Survey for Intelligent Spam Email Detection

et al. 2019

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The tremendously growing problem of phishing e-mail, also known as spam including spear phishing or spam borne malware, has demanded a need for reliable intelligent anti-spam e-mail filters. This survey paper describes a focused literature survey of Artificial Intelligence (AI) and Machine Learning (ML) methods for intelligent spam email detection, which we believe can help in developing appropriate countermeasures. In this paper, we considered 4 parts in the email's structure that can be used for intelligent analysis: (A) Headers Provide Routing Information, contain mail transfer agents (MTA) that provide information like email and IP address of each sender and recipient of where the email originated and what stopovers, and final destination. (B) The SMTP Envelope, containing mail exchangers' identification, originating source and destination domains\users. (C) First part of SMTP Data, containing information like from, to, date, subject-appearing in most email clients (D) Second part of SMTP Data, containing email body including text content, and attachment. Based on the number the relevance of an emerging intelligent method, papers representing each method were identified, read, and summarized. Insightful findings, challenges and research problems are disclosed in this paper. This comprehensive survey paves the way for future research endeavors addressing theoretical and empirical aspects related to intelligent spam email detection. INDEX TERMS Machine learning, phishing attack, spear phishing, spam detection, spam email, spam filtering.

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On the effectiveness of IP reputation for spam filtering

Cited by 25 publications

References 15 publications

RAPID: Reputation based approach for improving intrusion detection effectiveness

RAPID: Reputation based approach for improving intrusion detection effectiveness

Cross-country analysis of spambots

A Comprehensive Survey for Intelligent Spam Email Detection

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