Measuring, Characterizing, and Avoiding Spam Traffic Costs

Fonseca, Osvaldo; Fazzion, Elverton; Cunha, Ítalo; Las-Casas, Pedro; Guedes, Dorgival; Meira, Wagner; Hoepers, Cristine; Steding-Jessen, Klaus; Chaves, Marcelo H. P. C.

doi:10.1109/mic.2016.53

Cited by 25 publications

(13 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On a given day, millions of IPs are engaged in scanning behavior. Our improved models can aid cybersecurity analysts in determining which of these IPs are nefarious [118], the distribution of attacks in particular critical sectors [51], identifying spamming behavior [44], how to vaccinate against computer viruses New Phenomena in Large-Scale Internet Traffic 21 [8], obscuring web sources [52], identifying significant flow aggregates in traffic [26], and sources of rumors [95].…”

Section: Discussionmentioning

confidence: 99%

New Phenomena in Large-Scale Internet Traffic

Kepner¹,

Cho²,

claffy³

et al. 2022

Preprint

View full text Add to dashboard Cite

Internet Trafficof otherwise unseen leaf nodes and isolated links in Internet traffic. Our analysis further shows that a two-parameter modified Zipf-Mandelbrot distribution accurately describes a wide variety of source/destination statistics on moving sample windows ranging from 100,000 to 100,000,000 packets over collections that span years and continents. The measured model parameters distinguish different network streams, and the model leaf parameter strongly correlates with the fraction of the traffic in different underlying network topologies. Internet TrafficTable 1.

show abstract

Section: Discussionmentioning

confidence: 99%

New Phenomena in Large-Scale Internet Traffic

Kepner¹,

Cho²,

claffy³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The next grasshopper position is computed based on its current position, all other grasshoppers' positions, and the best grasshopper's position obtained so far, as given in Eq. (7). Note that the first component of this equation considers the location of the current grasshopper with respect to other grasshoppers.…”

Section: A the Grasshopper Optimization Algorithmmentioning

confidence: 99%

“…This method uses a collection model to extract all non-spam and spam emails of each user and implement pre-processing to convert the e-mail using feature selection/extraction, dimensionality reduction, then classify the data into two vector sets. The final step in this method uses ML techniques to train and test the datasets in order to determine whether the incoming emails are spam or non-spam [7]; (3) Heuristic or Rule Based Spam Filtering, uses previously created rules or inferences to evaluate a large number of patterns that are usually regular expressions against the chosen message. Some score of the message is increased if the known patterns are found.…”

Section: Introductionmentioning

confidence: 99%

“…Although some ranking rules do not change over time, other rules need constant updating to be able to effectively deal with the threat of spammers who constantly deliver new spam messages that can easily escape without being noticed by email filters. The SpamAssassin dataset is a popular example of a rule-based spam filter [7], [8]; (4) Adaptive Spam Filtering, is mainly based on the use of similarity measurement techniques. It is based on grouping email into different classes used in the detection and filtering of spam.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Training Neural Networks by Enhance Grasshopper Optimization Algorithm for Spam Detection System

et al. 2021

View full text Add to dashboard Cite

A significant negative impact of spam e-mail is not limited only to the serious waste of resources, time, and efforts, but also increases communications overload and cybercrime. Perhaps the most damaging aspect of spam email is that it has become such a major tool for attacks of cross-site scripting, malware infection, phishing, and cross-site request forgery, etc. Due to the nature of the adaptive unsolicited spam, it has been weakening the effect of the previous discovery techniques. This article proposes a new Spam Detection System (SDS) framework, by using a series of six different variants of enhanced Grasshopper Optimization Algorithm (EGOAs), which are investigated and combined with a Multilayer Perceptron (MLP) for the purpose of advanced spam email detection. In this context, the combination of MLP and EGOAs produces Neural Network (NN) models, referred to (EGOAMLPs). The main idea of this research is to use EGOAs to train the MLP to classify the emails as spam and non-spam. These models are applied to SpamBase, SpamAssassin, and UK-2011 Webspam benchmark datasets. In this way, the effectiveness of our models on detecting diverse forms of spam email is evidenced. The results showed that the proposed MLP model trained by EGOAs achieves a higher performance compared to other optimization methods in terms of accuracy, detection rate, and false alarm rate.

show abstract

“…Spam prevents the user from making full and good use of time, storage capacity and network bandwidth. The huge volume of spam mails flowing through the computer networks have destructive effects on the memory space of email servers, communication bandwidth, CPU power and user time [2]. The menace of spam email is on the increase on yearly basis and is responsible for over 77% of the whole global email traffic [3].…”

Section: Introductionmentioning

confidence: 99%

Machine learning for email spam filtering: review, approaches and open research problems

Dada

Bassi

Chiroma

et al. 2019

Heliyon

361

168

View full text Add to dashboard Cite

The upsurge in the volume of unwanted emails called spam has created an intense need for the development of more dependable and robust antispam filters. Machine learning methods of recent are being used to successfully detect and filter spam emails. We present a systematic review of some of the popular machine learning based email spam filtering approaches. Our review covers survey of the important concepts, attempts, efficiency, and the research trend in spam filtering. The preliminary discussion in the study background examines the applications of machine learning techniques to the email spam filtering process of the leading internet service providers (ISPs) like Gmail, Yahoo and Outlook emails spam filters. Discussion on general email spam filtering process, and the various efforts by different researchers in combating spam through the use machine learning techniques was done. Our review compares the strengths and drawbacks of existing machine learning approaches and the open research problems in spam filtering. We recommended deep leaning and deep adversarial learning as the future techniques that can effectively handle the menace of spam emails.

show abstract

Measuring, Characterizing, and Avoiding Spam Traffic Costs

Cited by 25 publications

References 7 publications

New Phenomena in Large-Scale Internet Traffic

New Phenomena in Large-Scale Internet Traffic

Training Neural Networks by Enhance Grasshopper Optimization Algorithm for Spam Detection System

Machine learning for email spam filtering: review, approaches and open research problems

Contact Info

Product

Resources

About