Email Abuse Research Articles

Workplace violence in the COVID-19 era: a qualitative analysis of harassment and threats against public health professionals in Canada

ObjectivesThis study reports the results of a qualitative study involving public health professionals and documents their experiences with cyberviolence, harassment and threats during the COVID-19 pandemic.Method and analysisThe research adopted...

Optimization of K Value in KNN Algorithm for Spam and Ham Email Classification

There are many cases of email abuse that have the potential to harm others. This email abuse is commonly known as spam, which contains advertisements, phishing scams, and even malware. This study purpose to know the classification of email spam with ham using the KNN method as an effort to reduce the amount of spam. KNN can classify spam or ham in an email by checking it using a different K value approach. The results of the classification evaluation using confusion matrix resulted in the KNN method with a value of K = 1 having the highest accuracy value of 91.4%. From the results of the study, it is known that the optimization of the K value in KNN using frequency distribution clustering can produce high accuracy of 100%, while k-means clustering produces an accuracy of 99%. So based on the results of the existing accuracy values, the frequency distribution clustering and k-means clustering can be used to optimize the K-optimal value of the KNN in the classification of existing spam emails.

Disruptive behavior within the workplace

IntroductionThe aim of this study was to determine the incidence of disruptive behavior among nurses in the healthcare workplace, the details that are associated with its occurrence, and the organizational procedures utilized when disruptive incidents occur. BackgroundHealthcare workers have a higher risk of experiencing disruptive behavior among staff in the workplace compared to other industries, and nurses are more susceptible than other healthcare workers. MethodA quantitative, descriptive, survey design asked nurses if they had experienced disruptive behavior within the past 12months and how this was handled by their organization. Disruptive behavior included any type of verbal abuse, electronic or e-mail abuse, or physical abuse within the work environment. ResultsThere were 2,821 participants that validated the occurrence of verbal, electronic, and physical disruptive behavior, and the majority rated their overall work environment to be at high risk of experiencing disruptive behavior at least once every 6months. DiscussionTwenty-four statistically significant relationships were found with strongest associations (<0.001) between: age and electronic abuse; position and verbal abuse; position and physical abuse; years of experience and electronic abuse; susceptibility of the organization to workplace violence and position as well as education; missing work due to disruptive behavior and position; and feeling comfortable reporting the abuse and position as well as education. ConclusionIt is evident that disruptive behavior exists verbally, electronically, and physically, and that the overall work environment is felt to be at high risk of experiencing repeated disruptive behavior.

Applying effective feature selection techniques with hierarchical mixtures of experts for spam classification

E-mail abuse has been steadily increasing during the last decade. E-mail users find themselves targeted by massive quantities of unsolicited bulk e-mail, which often contains offensive language or has fraudulent intentions. Internet Service Providers (ISPs) on the other hand, have to face a considerable system overloading as the incoming mail consumes network and storage resources. Among the plethora of solutions, the most prominent in terms of cost efficiency and complexity are the text filtering approaches. Most of the approaches model the problem using linear statistical models. Despite their popularity - due both to their simplicity and relative ease of interpretation - the non-linearity assumption of data samples is inappropriate in practice. This is mainly due to the inability of other approaches to capture the apparent non-linear relationships, which characterize these samples. In this paper, we propose a margin-based feature selection approach integrated with a Hierarchical Mixtures of Experts (HME) system, which attempts to overcome limitations common to other machine-learning based approaches. By reducing the data dimensionality using effective algorithms for feature selection we evaluated our system with publicly available corpora of e-mails, characterized by very high similarity between legitimate and bulk e-mail (and thus low discriminative potential). We experimented with two different architectures, a hierarchical HME and a perceptron HME. As a result, we confirm the domination of our Spam Filtering (SF) - HME method against other machine learning approaches, which present lesser degree of recall, as well as against traditional rule-based approaches, which lack considerably in the achieved degrees of precision.

Methods to Identify Spammers

Unsolicited commercial email has become a major threat for email communication. Although the degree of sophistication of spam filters has increased over time, such filters still produce high rates of false positives and false negatives, thereby reducing the reliability of email and introducing communication risks on their own. Due to more and more complex filtering methods implemented, the hardware requirements for mail servers are increasing to avoid the risk of denial of service situations. Therefore, some authors point out that mail filtering has reached its limits and ask for more preventive solutions to fight spam. One way to prevent email abuse would be to significantly increase the risk of a spammer being sued for damage compensation or, if legislation permits, for criminal offence. This approach believes in an assessment of risk and expected revenue by a the offender. But by hiding their real identity, spammers are very successful in evading prosecution. This paper discusses several methods to identify spammers and analyses under which circumstances those methods might be valid evidence in court.

Applying effective feature selection techniques with hierarchical mixtures of experts for spam classification

E-mail abuse has been steadily increasing during the last decade. E-mail users find themselves targeted by massive quantities of unsolicited bulk e-mail, which often contains offensive language or has fraudulent intentions. Internet Service Providers

Moderately hard, memory-bound functions

A resource may be abused if its users incur little or no cost. For example, e-mail abuse is rampant because sending an e-mail has negligible cost for the sender. It has been suggested that such abuse may be discouraged by introducing an artificial cost in the form of a moderately expensive computation. Thus, the sender of an e-mail might be required to pay by computing for a few seconds before the e-mail is accepted. Unfortunately, because of sharp disparities across computer systems, this approach may be ineffective against malicious users with high-end systems, prohibitively slow for legitimate users with low-end systems, or both. Starting from this observation, we research moderately hard functions that most recent systems will evaluate at about the same speed. For this purpose, we rely on memory-bound computations. We describe and analyze a family of moderately hard, memory-bound functions, and we explain how to use them for protecting against abuses.

Fighting workplace e‐mail abuse: state law issues

Employment Relations TodayVolume 29, Issue 1 p. 85-93 State Regulations Update Fighting workplace e-mail abuse: state law issues Charles M. Rice, Charles M. Rice crice@kilpatrickstockton.com Atlanta office of Kilpatrick Stockton LLPSearch for more papers by this authorTamila V. Lee, Tamila V. Lee Charlotte OfficeSearch for more papers by this author Charles M. Rice, Charles M. Rice crice@kilpatrickstockton.com Atlanta office of Kilpatrick Stockton LLPSearch for more papers by this authorTamila V. Lee, Tamila V. Lee Charlotte OfficeSearch for more papers by this author First published: 23 April 2002 https://doi.org/10.1002/ert.10031AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Volume29, Issue1Spring 2002Pages 85-93 RelatedInformation

E-mail abuse and corporate policies

In an out-of-court settlement, Norwich Union paid £450 000 to Western Provident Association because of libelous comments on its internal E-mail system about Western Provident Association's alleged financial problems. In another case in 1995, a policeman received ‘substantial’ damages in an out-of-court settlement with Asda when Asda staff posted an internal E-mail alleging that he was involved in refund fraud. These cases highlight the danger of careless use of the E-mail system and serve as a grim reminder that companies can suffer heavily from abuses by their staff.