Related papers: Kernel-level Rootkit Detection, Prevention and Beh…
Large Language Models (LLMs) suffer from a range of vulnerabilities that allow malicious users to solicit undesirable responses through manipulation of the input text. These so-called jailbreak prompts are designed to trick the LLM into…
This draft addresses issues of detecting stealthy integrity cyber-attacks on automatic control systems in the unified control and detection framework. A general form of integrity cyber-attacks that cannot be detected using the…
Ransomware's escalating sophistication necessitates tamper-resistant, off-host detection solutions that capture deep disk activity beyond the reach of a compromised operating system. Existing detection systems use host/kernel signals or…
Adversaries are often able to penetrate networks and compromise systems by exploiting vulnerabilities in people and systems. The key to the success of these attacks is information that adversaries collect throughout the phases of the cyber…
The development of quantum computers has been advancing rapidly in recent years. As quantum computers become more widely accessible, potentially malicious users could try to execute their code on the machines to leak information from other…
Modern malware poses a severe threat to cybersecurity, continually evolving in sophistication. To combat this threat, researchers and security professionals continuously explore advanced techniques for malware detection and analysis.…
Quantum computing introduces unfamiliar security vulnerabilities demanding customized threat models. Hardware and software Trojans pose serious concerns needing rethinking from classical paradigms. This paper develops the first structured…
Applying machine learning to biological sequences - DNA, RNA and protein - has enormous potential to advance human health, environmental sustainability, and fundamental biological understanding. However, many existing machine learning…
The rapid evolution of malware attacks calls for the development of innovative detection methods, especially in resource-constrained edge computing. Traditional detection techniques struggle to keep up with modern malware's sophistication…
While machine learning (ML) models are being increasingly trusted to make decisions in different and varying areas, the safety of systems using such models has become an increasing concern. In particular, ML models are often trained on data…
Security threats such as jamming and route manipulation can have significant consequences on the performance of modern wireless networks. To increase the efficacy and stealthiness of such threats, a number of extremely challenging,…
Detecting covert channels among legitimate traffic represents a severe challenge due to the high heterogeneity of networks. Therefore, we propose an effective covert channel detection method, based on the analysis of DNS network data…
Ransomware constitutes a significant threat to the Android operating system. It can either lock or encrypt the target devices, and victims are forced to pay ransoms to restore their data. Hence, the prompt detection of such attacks has a…
Docker offers an ecosystem that offers a platform for application packaging, distributing, and managing within containers. However, the Docker platform has not yet matured. Presently, Docker is less secured than virtual machines (VM) and…
It is becoming more common that adversary attacks consist of more than a standalone executable or script. Often, evidence of an attack includes conspicuous process heritage that may be ignored by traditional static machine learning models.…
Ransomware has been an ongoing issue since the early 1990s. In recent times ransomware has spread from traditional computational resources to cyber-physical systems and industrial controls. We devised a series of experiments in which…
Interactive theorem proving software is typically designed around a trusted proof-checking kernel, the sole system component capable of authenticating theorems. Untrusted automation procedures reside outside of the kernel, and drive it to…
Backdoors and poisoning attacks are a major threat to the security of machine-learning and vision systems. Often, however, these attacks leave visible artifacts in the images that can be visually detected and weaken the efficacy of the…
Anomaly detection is generally acknowledged as an important problem that has already drawn attention to various domains and research areas, such as, network security. For such "classic" application domains a wide range of surveys and…
Email tracking allows email senders to collect fine-grained behavior and location data on email recipients, who are uniquely identifiable via their email address. Such tracking invades user privacy in that email tracking techniques gather…