Related papers: Phishing Detection System: An Ensemble Approach Us…

In 2024, the Anti-Phishing Work Group identified over one million phishing pages. Phishers achieve this scale by using phishing kits -- ready-to-deploy phishing websites -- to rapidly deploy phishing campaigns with specific data…

Malware detection in modern computing environments demands models that are not only accurate but also interpretable and robust to evasive techniques. Graph neural networks (GNNs) have shown promise in this domain by modeling rich structural…

Security issues are threatened in various types of networks, especially in the Internet of Things (IoT) environment that requires early detection. IoT is the network of real-time devices like home automation systems and can be controlled by…

Radio Frequency Fingerprinting (RFF) has evolved as an effective solution for authenticating devices by leveraging the unique imperfections in hardware components involved in the signal generation process. In this work, we propose a…

Image spam emails are often used to evade text-based spam filters that detect spam emails with their frequently used keywords. In this paper, we propose a new image spam email detection tool called DeepCapture using a convolutional neural…

In the realm of construction safety, the detection of personal protective equipment, such as helmets, plays a critical role in preventing workplace injuries. This paper details the development and evaluation of convolutional neural networks…

Background: Explainability in phishing detection model can support a further solution of phishing attack mitigation by increasing trust and understanding how phishing can be detected. Objective: The aims of this study to determine and best…

The escalating complexity and volume of cyberattacks demand proactive detection strategies that go beyond traditional rule-based systems. This paper presents a phase-aware, multi-model machine learning framework that emulates adversarial…

Traditional machine learning models often prioritize predictive accuracy, often at the expense of model transparency and interpretability. The lack of transparency makes it difficult for organizations to comply with regulatory requirements…

This paper presents a novel convolutional neural network (CNN)-based detector for faster-than-Nyquist (FTN) signaling, introducing structured fixed kernel layers with domain-informed masking to effectively mitigate intersymbol interference…

We present a federated, multi-modal phishing website detector that supports URL, HTML, and IMAGE inputs without binding clients to a fixed modality at inference: any client can invoke any modality head trained elsewhere. Methodologically,…

Keylogger detection involves monitoring for unusual system behaviors such as delays between typing and character display, analyzing network traffic patterns for data exfiltration. In this study, we provide a comprehensive analysis for…

With the rapid growth of e-commerce, online payment fraud has become increasingly complex, posing serious threats to financial security and consumer trust. Traditional detection methods often struggle to capture the intricate relational…

Customers represent their satisfactions of consuming products by sharing their experiences through the utilization of online reviews. Several machine learning-based approaches can automatically detect deceptive and fake reviews. Recently,…

Phishing is a type of social engineering attack with an intention to steal user data, including login credentials and credit card numbers, leading to financial losses for both organisations and individuals. It occurs when an attacker,…

Phishing detectors built on engineered website features attain near-perfect accuracy under i.i.d.\ evaluation, yet deployment security depends on robustness to post-deployment feature manipulation. We study this gap through a cost-aware…

Phishing email is a serious cyber threat that tries to deceive users by sending false emails with the intention of stealing confidential information or causing financial harm. Attackers, often posing as trustworthy entities, exploit…

Advances in computer vision have brought us to the point where we have the ability to synthesise realistic fake content. Such approaches are seen as a source of disinformation and mistrust, and pose serious concerns to governments around…

This paper examines the vulnerabilities of convolutional neural networks (CNNs) to adversarial attacks and explores a method for their safeguarding. In this study, CNNs were implemented on four of the most common image datasets, namely…

The hypothesis here states that neural network algorithms such as Multi-layer Perceptron (MLP) have higher accuracy in differentiating malicious and semi-structured phishing URLs. Compared to classical machine learning algorithms such as…