Related papers: POSTER: A Multi-Signal Model for Detecting Evasive…

The email system is the central battleground against phishing and social engineering attacks, and yet email providers still face key challenges to authenticate incoming emails. As a result, attackers can apply spoofing techniques to…

With its critical role in business and service delivery through mobile devices, SMS (Short Message Service) has long been abused for spamming, which is still on the rise today possibly due to the emergence of A2P bulk messaging. The effort…

Phishing is one of the most effective ways in which cybercriminals get sensitive details such as credentials for online banking, digital wallets, state secrets, and many more from potential victims. They do this by spamming users with…

Phishing attacks remain a critical cybersecurity threat. Attackers constantly refine their methods, making phishing emails harder to detect. Traditional detection methods, including rule-based systems and supervised machine learning models,…

Spear Phishing is a harmful cyber-attack facing business and individuals worldwide. Considerable research has been conducted recently into the use of Machine Learning (ML) techniques to detect spear-phishing emails. ML-based solutions may…

Phishing attacks threaten online users, often leading to data breaches, financial losses, and identity theft. Traditional phishing detection systems struggle with high false positive rates and are usually limited by the types of attacks…

Phishing remains one of the most prevalent online threats, exploiting human trust to harvest sensitive credentials. Existing URL- and HTML-based detection systems struggle against obfuscation and visual deception. This paper presents…

This paper explores the use of Large Language Models (LLMs) in spear phishing message generation and evaluates their performance compared to human-authored counterparts. Our pilot study examines the effectiveness of smishing (SMS phishing)…

Developments in artificial intelligence (AI) are likely to affect social engineering and change cyber defense operations. The broad and sweeping nature of AI impact means that many aspects of social engineering could be automated,…

In this paper, we present findings from a large-scale and long-term phishing experiment that we conducted in collaboration with a partner company. Our experiment ran for 15 months during which time more than 14,000 study participants…

Phishing sites continue to grow in volume and sophistication. Recent work leverages large language models (LLMs) to analyze URLs, HTML, and rendered content to decide whether a website is a phishing site. While these approaches are…

The increase in global cellphone usage has led to a spike in instant messaging scams, causing extensive socio-economic damage with yearly losses exceeding half a trillion US dollars. These scams pose a challenge to the integrity of justice…

Spam is commonly known as unsolicited or unwanted email messages in the Internet causing potential threat to Internet Security. Users spend a valuable amount of time deleting spam emails. More importantly, ever increasing spam emails occupy…

The rise in phishing attacks via e-mail and short message service (SMS) has not slowed down at all. The first thing we need to do to combat the ever-increasing number of phishing attacks is to collect and characterize more phishing cases…

Phishing attacks are among emerging security issues that recently draws significant attention in the cyber security community. There are numerous existing approaches for phishing URL detection. However, malicious URL detection is still a…

Phishing websites remain a persistent cybersecurity threat by mimicking legitimate sites to steal sensitive user information. Existing machine learning-based detection methods often rely on supervised learning with labeled data, which not…

Model merging is a widespread technology in large language models (LLMs) that integrates multiple task-specific LLMs into a unified one, enabling the merged model to inherit the specialized capabilities of these LLMs. Most task-specific…

Phishing has become a prominent risk in modern cybersecurity, often used to bypass technological defences by exploiting predictable human behaviour. Warning dialogues are a standard mitigation measure, but the lack of explanatory clarity…

Phishing email detection faces significant challenges due to evolving adversarial tactics and heterogeneous attack patterns. Traditional approaches, such as rule-based filters and denylists, often struggle to keep pace, leading to missed…

The rapid adoption of open-source Large Language Models (LLMs) in offline and enterprise environments has introduced a largely unexamined security risk like susceptibility to adversarial phishing prompts under static safety configurations.…