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Deep learning models often face challenges when handling real-world image corruptions. In response, researchers have developed image corruption datasets to evaluate the performance of deep neural networks in handling such corruptions.…

With the ever-growing demand for cybersecurity, static key encryption mechanisms are increasingly vulnerable to adversarial attacks due to their deterministic and non-adaptive nature. Brute-force attacks, key compromise, and unauthorized…

Fraudulent activities are rapidly evolving, employing increasingly diverse and sophisticated methods that pose serious threats to individuals, organizations, and society. This paper proposes the FIST Framework (Fraud Incident Structured…

In recent years we have seen an explosion in the usage of low-cost, low-power microcontrollers (MCUs) in embedded devices around us due to the popularity of Internet of Things (IoT) devices. Although this is good from an economics…

Although Aligned Large Language Models (LLMs) are trained to refuse harmful requests, they remain vulnerable to jailbreak attacks. Unfortunately, existing methods often focus on surface-level patterns, overlooking the deeper attack…

Data-Flow Integrity (DFI) is a well-known approach to effectively detecting a wide range of software attacks. However, its real-world application has been quite limited so far because of the prohibitive performance overhead it incurs.…

This paper presents a novel three-stage framework for real-time foreign object intrusion (FOI) detection and tracking in power transmission systems. The framework integrates: (1) a YOLOv7 segmentation model for fast and robust object…

Traditional rule-based cybersecurity systems have proven highly effective against known malware threats. However, they face challenges in detecting novel threats. To address this issue, emerging cybersecurity systems are incorporating AI…

Foundation Models (FMs) display exceptional performance in tasks such as natural language processing and are being applied across a growing range of disciplines. Although typically trained on large public datasets, FMs are often fine-tuned…

Fault diagnosis of mechanical equipment involves data collection, feature extraction, and pattern recognition but is often hindered by the imbalanced nature of industrial data, introducing significant uncertainty and reducing diagnostic…

The evolution of ransomware requires the development of more sophisticated detection methodologies capable of identifying malicious behaviors beyond traditional signature-based and heuristic techniques. The proposed Hierarchical…

Adversarial attacks for machine learning models have become a highly studied topic both in academia and industry. These attacks, along with traditional security threats, can compromise confidentiality, integrity, and availability of…

Physically Unclonable Function (PUF) offers a secure and lightweight alternative to traditional cryptography for authentication due to their unique device fingerprint. However, their dependence on specialized hardware hinders their adoption…

Universal Serial Bus (USB) Flash Drives are nowadays one of the most convenient and diffused means to transfer files, especially when no Internet connection is available. However, USB flash drives are also one of the most common attack…

The rising complexity of cyber threats calls for a comprehensive reassessment of current security frameworks in business environments. This research focuses on Stealth Data Exfiltration, a significant cyber threat characterized by covert…

Fault injection (FI) is a powerful attack methodology allowing an adversary to entirely break the security of a target device. As finite-state machines (FSMs) are fundamental hardware building blocks responsible for controlling systems,…

Security evaluations inherently depend on stable identifiers. Any finding, audit, or regulatory decision must remain attached to the specific artifact it pertains to. Continuously updated artificial intelligence systems violate this core…

Embedded devices face an ever-expanding threat landscape: vulnerabilities in application software, operating system kernels, and peripherals threaten the embedded device integrity. Existing computer-architectural defenses fully consider at…

As machine learning (ML) systems expand in both scale and functionality, the security landscape has become increasingly complex, with a proliferation of attacks and defenses. However, existing studies largely treat these threats in…

Fundamental processes in digital forensic investigation, such as disk imaging, were developed when digital investigation was relatively young. As digital forensic processes and procedures matured, these fundamental tools, that are the…