In 2023, nearly 45.6 percent of all e-mails worldwide were identified as spam, down from almost 49 percent in 2022. While remaining a big part of the e-mail traffic, since 2011, the share of spam e-mails has decreased significantly. In 2023, the highest volume of spam e-mails was registered in May, approximately 50 percent of e-mail traffic worldwide.

Spam messages accounted for over **** percent of e-mail traffic in December 2023. Russia generated the largest share of unsolicited spam e-mails in 2022, with **** percent of global spam e-mails originating from the country. Spam worldwide It is almost impossible to think about e-mail without considering the issue of spam, which usually includes billions of promotional e-mails marketers send daily. As of January 2023, the United States had the highest number of spam e-mails sent daily. While many e-mail users believe such content belongs in their spam folder, marketing e-mails are generally harmless if annoying to the user. Malicious spam Phishing e-mails remain one of the primary attack vectors for cybercriminals. On average, around ** percent of businesses worldwide experience four to six successful cyber attacks in one year. Another ** percent said they became victims of more than ** bulk phishing attacks. More than half of the companies said these phishing attacks resulted in consumer or client data breaches.

It started like any other Tuesday morning. A mid-level finance manager at a US-based logistics firm opened what looked like an urgent request from their CEO. The subject line? “Quarterly Financial Review Needed Immediately.” The logo looked legit. The tone felt familiar. Within two minutes, confidential files were shared, and...

Email, text, and call spam remain major threats nowadays. Nearly half of all daily emails are unwanted, with users worldwide encountering boosted volumes of phishing and scam content. In retail and financial services, spam disrupts customer trust and inflates cybersecurity budgets. Meanwhile, call-based scams cost consumers time and mental strain...

This is a collection of text data from 160 emails. For each email, we have included the subject, text, and type of phishing email. The four types of emails included in the dataset are fraud, false positives (legitimate emails), phishing, and commercial spam. 40 of each type of email are in the dataset. This type of data can be used to help build a more complex email spam blocker and could have applications in cybersecurity.

The dataset consists of a CSV file containing of 300 generated email spam messages. Each row in the file represents a separate email message, its title and text. The dataset aims to facilitate the analysis and detection of spam emails. The dataset can be used for various purposes, such as training machine learning algorithms to classify and filter spam emails, studying spam email patterns, or analyzing text-based features of spam messages.

How to Access:

To access this dataset, please contact Francisco Janez via email at francisco.janez@unileon.es. Access will be granted based on specific requests.

Purpose:The PerSentSE corpus was developed to study persuasive techniques in spam emails. It includes 130 emails randomly selected from the SpamArchive2122 dataset, which contains over 20,000 spam emails in English.

Methodology:

Segmentation: Emails were divided into sentences using the NLTK library.

Annotation: Eight persuasive techniques, along with a "non-persuasion" class, were identified. Two expert annotators labeled an initial subset of emails to measure inter-annotator agreement, achieving a final acceptable level (γ = 0.63).

Corpus Statistics:

Total sentences: 1,075

Persuasive sentences: 216 (20.1%)

Persuasion Distribution by Email Sections (Table 7):

Subject lines: 35.59% persuasive, with an average of 1.62 techniques.

Greeting section: 54.17% persuasive, averaging 1.46 techniques.

Email body: 82.46% persuasive, with 5.51 techniques on average.

Farewell section: 31.43% persuasive, averaging 1.45 techniques.

Co-occurrence of Techniques (Figure 2):Some persuasive techniques frequently appeared together:

Appeal to Fear/Prejudice with Loaded Language: 25 instances.

Exaggeration/Minimization with Loaded Language: 24 instances.

Appeal to Fear/Prejudice with Exaggeration/Minimization: 20 instances.

Findings:The body section of emails concentrates the highest number of persuasive elements, contrary to earlier studies focusing on subject lines alone. This suggests that spam emails rely heavily on persuasive content in their main text.

In 2020, healthcare-related spam e-mails accounted for nearly 33 percent of total spam volume. Spam e-mails with adult content were the second-most common category, around 27 percent. Dating-related junk mail generated approximately 10 percent of spam messages in the same period.

SPAM E-mail Database

The “spam” concept is diverse: advertisements for products/websites, make money fast schemes, chain letters, pornography… Our collection of spam e-mails came from our postmaster and individuals who had filed spam. Our collection of non-spam e-mails came from filed work and personal e-mails, and hence the word ‘george’ and the area code ‘650’ are indicators of non-spam. These are useful when constructing a personalized spam filter. One would either have to blind such non-spam indicators or get a very wide collection of non-spam to generate a general purpose spam filter.

Attribute Information:

The last column denotes whether the e-mail was considered spam (1) or not (0), i.e. unsolicited commercial e-mail. Most of the attributes indicate whether a particular word or character was frequently occurring in the e-mail. The run-length attributes (55-57) measure the length of sequences of consecutive capital letters.

For the statistical measures of each attribute, see the end of this file. Here are the definitions of the attributes:

48 continuous real [0,100] attributes of type word_freq_WORD = percentage of words in the e-mail that match WORD, i.e. 100 * (number of times the WORD appears in the e-mail) / total number of words in e-mail. A “word” in this case is any string of alphanumeric characters bounded by non-alphanumeric characters or end-of-string.

6 continuous real [0,100] attributes of type char_freq_CHAR = percentage of characters in the e-mail that match CHAR, i.e. 100 * (number of CHAR occurrences) / total characters in e-mail

1 continuous real [1,…] attribute of type capital_run_length_average = average length of uninterrupted sequences of capital letters

1 continuous integer [1,…] attribute of type capital_run_length_longest = length of longest uninterrupted sequence of capital letters

1 continuous integer [1,…] attribute of type capital_run_length_total = sum of length of uninterrupted sequences of capital letters = total number of capital letters in the e-mail

1 nominal {0,1} class attribute of type spam = denotes whether the e-mail was considered spam (1) or not (0), i.e. unsolicited commercial e-mail.

Context

Project description

Files

Preprocessed data derived from the "spam-mails" dataset, containing email messages labeled as spam or ham. Each record includes a unique identifier from the original dataset and an experiment_id indicating its assignment to a specific data split (training, validation, or test) used in this experiment. The email content has been lemmatized and cleaned to remove noise such as punctuation, special characters, and stopwords, ensuring consistent input for embedding and model training. Original data source: https://www.kaggle.com/datasets/venky73/spam-mails-dataset

This is a large corpus of 42,619 preprocessed text messages and emails sent by humans in 43 languages. is_spam=1 means spam and is_spam=0 means ham. 1040 rows of balanced data, consisting of casual conversations and scam emails in ≈10 languages, were manually collected and annotated by me, with some help from ChatGPT.

  Some preprcoessing algorithms

spam_assassin.js, followed by spam_assassin.py enron_spam.py

  Data composition








  Description

To make the text… See the full description on the dataset page: https://huggingface.co/datasets/FredZhang7/all-scam-spam.

Phishing Email Dataset

This dataset on Hugging Face is a direct copy of the 'Phishing Email Detection' dataset from Kaggle, shared under the GNU Lesser General Public License 3.0. The dataset was originally created by the user 'Cyber Cop' on Kaggle. For complete details, including licensing and usage information, please visit the original Kaggle page.

Here, we’ve gathered email marketing benchmarks by industry. You can see how your average email open, click-through, click-to-open, unsubscribe, and spam complaint rates compare against other companies in your industry.

What are the average email marketing results in different countries? Here’s what we’ve found.

Dataset

This dataset was created by Faisal Qureshi

Released under CC0: Public Domain

Contents

It contains the following files:

The statistic shows the global e-mail spam rate from 2012 to 2018. In the most recently observed period, it was found that spam accounted for 55 percent of all e-mail messages, same as during the previous year.

Spam messages accounted for 45.1 percent of e-mail traffic in March 2021. During the most recently measured period, Russia generated the largest share of unsolicited spam e-mails with 23.52 percent of global spam volume. Despite its ubiquity, the global e-mail spam rate has actually been decreasing: the global annual spam e-mail rate in 2018 was 55 percent, down from 69 percent in 2012. Spam e-mail It is almost impossible to think about e-mail without considering the issue of spam. In 2019, 293.6 billion e-mails were sent and received on a daily basis. This includes billions of promotional e-mails sent by marketers every day. Whilst many e-mail users believe such content belongs in their spam folder, marketing e-mails are generally harmless, if annoying to the user. In 2018, the spam placement rate of commercial e-mails had declined to nine percent, down from 14 percent in 2017.

Malicious spam Not all spam are benign promotional e-mails though. A significant portion of spam messages are of a more malicious nature, aiming to damage or hijack user systems. The most common variants of malicious spam worldwide include trojans, spyware, and ransomware.

E-Mail Spam Filter Market Outlook

The global market size for e-mail spam filters is poised to grow from approximately USD 2.84 billion in 2023 to an estimated USD 5.97 billion by 2032, with a robust compound annual growth rate (CAGR) of 8.5%. This growth is driven by increasing cyber threats and the rising importance of securing communication channels.

One of the primary growth factors for the e-mail spam filter market is the escalating number of cyberattacks and phishing scams. These attacks often infiltrate via spam emails, making it critical for organizations to implement robust spam filters to protect sensitive information. The sophistication of spam email tactics has evolved, necessitating advanced filtering solutions that can detect and block such threats effectively. Consequently, the demand for dynamic and intelligent spam filtering systems is on the rise.

Furthermore, the growing regulatory demands for data protection and privacy act as significant drivers for this market. Regulations such as GDPR in Europe and CCPA in California mandate stringent measures to protect users' data, including the prevention of spam and phishing emails. Compliance with these regulations often requires the deployment of advanced spam filtering technologies, thereby propelling market growth. Companies are increasingly investing in these solutions to avoid hefty penalties and maintain customer trust.

In addition to spam filters, Email Protection Software plays a crucial role in safeguarding communication channels from a myriad of cyber threats. These software solutions provide comprehensive protection by integrating features such as encryption, data loss prevention, and threat intelligence. With the increasing sophistication of cyberattacks, organizations are turning to email protection software to ensure the confidentiality and integrity of their communications. This software not only helps in blocking spam but also offers advanced threat detection capabilities, making it an indispensable tool for modern businesses aiming to secure their email infrastructure.

Another crucial factor contributing to the market's expansion is the increasing adoption of cloud-based services. Cloud computing offers scalable solutions that can be easily integrated with existing email systems, providing efficient spam filtering capabilities without the need for significant upfront investments in hardware. This flexibility and cost-effectiveness make cloud-based spam filters particularly attractive to small and medium enterprises (SMEs), further driving market growth.

Regionally, North America holds a significant share of the e-mail spam filter market, owing to the high adoption of advanced technologies and the presence of major industry players. The Asia Pacific region is anticipated to exhibit the highest growth rate during the forecast period, driven by the rapid digital transformation and increasing cyber threats in emerging economies like India and China. The stringent regulatory environment in Europe also ensures steady demand for spam filter solutions in this region.

Component Analysis

The e-mail spam filter market can be broadly segmented into two main components: software and services. The software segment encompasses the actual spam filtering applications that can be installed and integrated into email systems. These software solutions range from basic spam filters to advanced machine learning-based systems that can adapt to new threats. The demand for software solutions is driven by their ability to provide real-time protection against spam and phishing attacks, ensuring the security of organizational communication channels.

On the other hand, the services segment includes managed services, consulting, and support services provided by vendors. Managed services are particularly popular among organizations that lack the in-house expertise to manage and update spam filters. These services often include regular updates, monitoring, and management of the spam filtering systems, ensuring optimal performance and protection. Consulting services help organizations choose the right spam filtering solutions and implement them effectively, while support services provide ongoing assistance to address any issues that may arise.

The software segment is anticipated to hold a larger market share due to the increasing preference for advanced spam filtering solutions that can be customize

Dataset

This dataset was created by Johar M. Ashfaque

Contents