In 2023, half of the social engineering attacks worldwide were scams, making it the most common type of cyberattack in this category. Phishing ranked second, with **** percent of the attacks, while business e-mail compromise (BEC) made up nearly ** percent of the total spear-phishing attacks.

Introduction

Social Engineering Statistics: Social Engineering refers to the use of psychological tricks by perpetrators to obtain critical and sensitive information from the victims. Although the methods have changed with technology, the purpose of deceit to obtain information remains a significant threat to the general population.

In this blog, we will review social engineering statistics to provide a holistic overview of the dangerous aspects of negative factors involving social engineering. By venturing into this topic, one can avoid getting compromised and be aware of measures to prevent cybersecurity attacks.

******** and*************************************n was the most common social engineering method used by cybercriminals in Poland in 2023.

Social Engineering Attack Scenarios collected from various sources : News papers, Government advisories, Police education materials and interviews.

The Social Engineering Audit market is experiencing robust growth, driven by the increasing sophistication of cyberattacks and the rising awareness of social engineering vulnerabilities among organizations. The market, estimated at $500 million in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 15% between 2025 and 2033, reaching approximately $1.8 billion by 2033. This expansion is fueled by several key factors, including mandatory compliance regulations (GDPR, CCPA, etc.) pushing organizations to strengthen their security posture and increasing reliance on remote work models, making organizations more susceptible to social engineering attacks. The market is segmented by service type (vulnerability assessments, penetration testing, employee training), industry (financial services, healthcare, technology), and region. North America currently holds a significant market share, but the Asia-Pacific region is expected to show the fastest growth due to rising digitalization and a growing number of cyber security awareness programs. The competitive landscape is fragmented, with numerous specialized firms and consulting companies offering social engineering audit services. Key players like Sofistic Cybersecurity, Vaadata, and others are competing based on their expertise, service offerings, and geographic reach. However, the market also faces certain restraints, including the high cost of audits and a shortage of skilled professionals capable of conducting these assessments. Despite this, the ongoing evolution of social engineering tactics, coupled with the ever-increasing severity and financial impact of successful attacks, will continue to drive strong demand for sophisticated social engineering audit services in the coming years. The market is poised for continued growth, particularly as organizations increasingly prioritize proactive security measures to mitigate the risks associated with human error in cybersecurity. This requires a multi-pronged approach encompassing robust technical security measures combined with comprehensive employee training and awareness programs.

The social media security market is experiencing robust growth, driven by the increasing reliance on social media platforms for both personal and professional communication, alongside a corresponding surge in cyber threats targeting these channels. The market's expansion is fueled by several key factors. Firstly, the escalating sophistication and frequency of social engineering attacks, data breaches, and brand impersonation necessitate robust security solutions. Secondly, growing government regulations and data privacy concerns (like GDPR and CCPA) are pushing organizations to invest heavily in preventative measures and incident response capabilities. Thirdly, the rise of remote work and the integration of social media into business workflows have expanded the attack surface, further emphasizing the need for comprehensive security strategies. The market is segmented by application (Professional Services and Managed Services) and by security type (Web Security, Application Security, Endpoint Security, Network Security, and Cloud Security). The high adoption rate of cloud-based services across various sectors, from finance to healthcare, is driving significant growth in cloud security solutions specifically. While the overall market shows significant promise, certain challenges remain. High implementation and maintenance costs associated with sophisticated security tools can hinder adoption, particularly for smaller businesses. Furthermore, the constant evolution of cyber threats requires continuous updates and adaptation of security solutions, posing an ongoing operational expense. Despite these restraints, the market is poised for considerable expansion in the coming years, driven by the sustained growth of social media usage and the increasing awareness of associated risks. This growth will be seen across all regions, but North America and Europe are likely to maintain significant market shares due to their high technological adoption and advanced security infrastructure. Key players in the market, such as Sophos, Trend Micro, and Symantec, are constantly innovating and investing in research and development to stay ahead of evolving threats and maintain their competitive advantage. The market's dynamic nature demands continuous monitoring and strategic adaptation to remain competitive.

In today's digitally-driven world, the Social Engineering Attack Defense Solution market is rapidly emerging as a vital component of cybersecurity strategies across various industries. Social engineering attacks, which exploit human psychology rather than technical vulnerabilities, have become increasingly sophistic

This dataset contains 624 English-language messages for training and evaluating phishing detection models using Natural Language Processing (NLP). Each message is labeled with one of six classes related to cybersecurity threats or benign content.

The file is in .xlsx format with two columns:

• Corpus: Message content (email or SMS-like)
• Label: Message category (Phishing, Malware, Scareware, Baiting, Pretexting, or NOT-Malicious)

This dataset supports multi-class classification and is ideal for machine learning applications in cybersecurity, especially in detecting social engineering attacks.

All messages are anonymized and do not contain any personal data.

Contact: jessica.testa@eng.it

Citation: Engineering Ingegneria Inforamtica Spa (2025). Multiclass NLP Dataset for Phishing and Social Engineering Threat Detection [Data set]. Zenodo. DOI:10.5281/zenodo.15235123

The Social Engineering Testing Services market is experiencing robust growth, driven by the increasing sophistication of cyberattacks and the rising awareness of social engineering vulnerabilities among organizations. The market's expansion is fueled by the need for proactive security measures to protect against increasingly prevalent phishing scams, pretexting, and other social engineering techniques. While precise market sizing requires proprietary data, a reasonable estimation based on industry reports and the listed companies suggests a current market value (2025) in the range of $300-$500 million USD. Considering a plausible CAGR of 15-20% (a conservative estimate considering the cybersecurity landscape), the market is projected to reach a value between $700 million and $1.5 billion by 2033. Key market drivers include the growing adoption of cloud-based services, the increasing prevalence of remote work, and stringent regulatory compliance requirements. Trends indicate a shift towards automated social engineering testing tools and a heightened focus on employee training and awareness programs to bolster defenses against these attacks. Despite this growth, restraints include the high cost of professional penetration testing services and the need for specialized expertise. The market is segmented by service type (e.g., phishing simulations, pretexting tests, vulnerability assessments), deployment mode (cloud, on-premise), industry vertical (finance, healthcare, government), and region, providing opportunities for specialized service providers. The competitive landscape is characterized by a mix of established cybersecurity firms like Tarlogic and ScienceSoft, along with smaller specialized vendors and niche players. The success of these firms hinges on their ability to offer innovative testing methodologies, comprehensive reporting, and tailored solutions that address specific client needs. Future growth will be influenced by technological advancements in AI-powered threat detection, the development of more sophisticated social engineering tactics, and the continued evolution of regulatory frameworks. A key factor in market penetration will be the effectiveness of service providers in clearly demonstrating the return on investment (ROI) from social engineering testing, emphasizing quantifiable risk reduction and improved security posture. The ongoing evolution of social engineering techniques necessitates constant innovation and adaptation within the industry to maintain effectiveness.

In 2023, business e-mail compromise (BEC) scams were the most common type of social engineering attacks using Gmail.com. Roughly **** percent of such cyberattacks detected on Gmail.com were identified as BEC scams. General scamming ranked second, with over ** percent, and phishing was identified in *** percent of social engineering attacks abusing Gmail.com.

The Social Engineering Attack Defense Solution market is experiencing robust growth, driven by the escalating sophistication and frequency of social engineering attacks targeting businesses and individuals. The increasing reliance on digital technologies and remote work environments has expanded the attack surface, making organizations more vulnerable. While precise market sizing data isn't provided, considering a plausible CAGR of 15% (a reasonable estimate given the cybersecurity market's overall growth) and a 2025 market value of $5 billion (a conservative estimation considering the importance of this security segment), the market is projected to reach approximately $10 billion by 2033. Key drivers include the rising adoption of cloud-based security solutions, increased regulatory compliance requirements (such as GDPR and CCPA), and a growing awareness of the financial and reputational damage caused by successful social engineering attacks. The market is segmented by solution type (e.g., security awareness training, phishing simulation, deception technology), deployment model (cloud, on-premise), and end-user industry (finance, healthcare, government). Competitive forces are strong, with established players like Imperva and Datto competing with newer entrants focusing on specialized solutions. However, challenges remain, including the ongoing evolution of social engineering techniques, the difficulty in detecting highly targeted attacks, and the persistent skills gap in cybersecurity professionals. The market's growth trajectory is further influenced by several trends, including the increasing use of artificial intelligence and machine learning to enhance threat detection and response capabilities. The integration of social engineering defense solutions with existing security infrastructure, such as SIEM (Security Information and Event Management) systems, is also gaining traction. Furthermore, the rise of phishing-as-a-service (PhaaS) platforms is prompting a corresponding increase in demand for sophisticated countermeasures. Despite the strong growth projections, market restraints include the high cost of implementation and maintenance for comprehensive solutions, the need for ongoing training and updates to keep pace with evolving threats, and the complexity of integrating these solutions into existing IT infrastructures. The market's future success depends on the ability of vendors to develop and deliver innovative, cost-effective, and user-friendly solutions that address these challenges and keep pace with the ever-changing landscape of social engineering attacks.

The Social Engineering Penetration Testing market plays a crucial role in today's cybersecurity landscape, where organizations increasingly recognize the significance of human factors in their security protocols. Social engineering attacks exploit psychological manipulation to trick individuals into divulging confid

The Social Engineering Audit market has emerged as a crucial element in safeguarding organizations against sophisticated cyber threats. In an era where human behaviors can significantly influence cybersecurity, a comprehensive social engineering audit assesses vulnerabilities created by employees and organizational

Between November 2022 and October 2023, the education saw 860 data breach cases caused by system intrusion. Basic web application attacks resulted in 161 data breaches in the finance sector. Social engineering attacks caused 158 data breaches in the construction sector.

Cyber security guidance

Detecting Deception with Emotion: The Role of Emotional AI in Combating Social Engineering AttacksAuthor: Stephen Mikah MakoshiDate: April 12, 2025Type: Copyrighted ManuscriptDetailed Description:This manuscript provides a comprehensive investigation into the integration of Emotional Artificial Intelligence (Emotional AI) into modern cybersecurity practices, with a focus on its potential to combat social engineering attacks. Unlike traditional cyber threats that exploit system vulnerabilities, social engineering targets the human psyche—manipulating users through tactics based on fear, trust, urgency, authority, and curiosity.Authored by Stephen Mikah Makoshi, the paper begins by unpacking the psychology behind social engineering and detailing the most prevalent attack techniques, including phishing, grooming, pretexting, and baiting. It emphasizes that human error continues to be the weakest link in cybersecurity despite the advancement of technical defenses, thereby underscoring the need for emotionally aware systems that can interpret and respond to human behavior.The core of the study introduces Emotional AI as an emerging, multidisciplinary approach that combines Natural Language Processing (NLP), computer vision, behavioral biometrics, and machine learning to assess emotional cues in real time. These cues—extracted from text, voice tone, facial expressions, and user interaction patterns—can be used to detect signs of manipulation or distress that precede a social engineering exploit.The paper outlines how Emotional AI can be applied across various domains within cybersecurity, including:Phishing email detection through sentiment and tone analysis.Voice and chatbot security by analyzing stress and urgency in conversations.Insider threat monitoring through behavioral and emotional baselining.Continuous real-time monitoring to flag deviations in user behavior tied to emotional shifts.Beyond its practical applications, the manuscript also explores the ethical, legal, and social implications of deploying Emotional AI. Issues such as emotional data privacy, informed consent, algorithmic bias, and compliance with global data protection regulations (e.g., GDPR, CCPA) are critically examined.To support real-world adoption, the paper presents a detailed implementation framework covering:Data collection and multimodal input analysisMachine learning model training and customizationIntegration with SIEM and behavioral analytics toolsEthical governance and compliance safeguardsPilot testing and continuous system improvementFinally, the manuscript concludes that Emotional AI is not a replacement for traditional cybersecurity systems, but a strategic augmentation—a tool that strengthens the "human firewall" by equipping systems with the emotional awareness necessary to detect and disrupt socially engineered cyber threats.

# Description of contents

This repository contains the codebook, dataset and analysis scripts in R used for the following publication: Pavlo Burda, Luca Allodi, Nicola Zannone. "Cognition in Social Engineering Empirical Research: a Systematic Literature Review". ACM Transactions on Computer-Human Interaction (TOCHI).
The repository consists of the following files:

## dataset_and_codebook.xlsx contains the dataset, the codebook and a detailed description of contents.

## scripts/ contains the R scripts used for the analysis

## readme.txt contains this readme

# Dataset and codebook

The dataset_and_codebook.xlsx contains the following sheets:

## Codebook
Contains a detailed description of the dataset (tables, columns, fields, etc.).
The codebook describes the concepts and variables that are present in the dataset. This includes explanations on meaning, numerical values, classification schemes and labels.

## Hypotheses table
Contains the hypotheses for all analyzed papers and is used in the results of the paper.
Each row is a hypothesis of an included paper, cells contain one or more values (e.g., value1,value2,...) with or without sub values (e.g., value1,value2(sub-value1, ...)). Any content that is in square brackets [] is ignored in the analysis. Empty cells mean that there is no applicable value for that column.

## Papers table
Contains the analyzed papers and is used in the results of the paper. It is also used in the overview table (Table 6) in Appendix C.
Each row is an included paper, cells contain up to two values (e.g., value1, value2) or the word 'multiple' in case of more than two values. Empty cells mean that there is no applicable value for that column.

## Values table
Contains the description of cell values of 'Hypotheses' and 'Papers' and sub-values (specific variables in a study) that belong to a value.
It has a hierarchical structure from left to right where each field on the right column falls under the first non-empty field on the immediate left-top.

# Reproducing results with scripts/ (generate figures)
To run the R scripts included in the 'scripts' directory it is sufficient to follow the instructions in and run the 'RUN_ALL_SCRIPTS.R' in 'scripts' directory.
The scripts use the TSV (Tab Separated Values) format of the dataset, which is the exact copy of the 'Papers' and 'Hypotheses' tables in the 'dataset_and_codebook.xlsx' file.
The resulting figures are stored in the 'scripts/results' directory.

Case study's original data in Dissecting the Process of Manipulating People: A State Transition-based Risk Assessment Approach for Social Engineering Attacks.

According to surveys of working adults and IT professionals conducted in 2023, almost ***** in ** respondents reported having encountered vishing attacks. This represents a slight decrease from ** percent in the year prior. Vishing attacks are a type of social engineering attacks performed over phone calls or voice messages for phishing.