Enhancing Automotive Security with a Hybrid Approach Towards Universal Intrusion Detection System

Security measures are essential in the automotive industry to detect intrusions in-vehicle networks. However, developing a one-size-fits-all Intrusion Detection System IDS is challenging because each vehicle has unique data profiles. This is due to the complex and dynamic nature of the data...

EUVD-2024-39192

Malicious code in bioql PyPI...

EUVD-2024-0756

Malicious code in bioql PyPI...

EUVD-2023-2972

Malicious code in bioql PyPI...

EUVD-2024-45841

Malicious code in bioql PyPI...

WAInjectBench: Benchmarking Prompt Injection Detections for Web Agents

Multiple prompt injection attacks have been proposed against web agents. At the same time, various methods have been developed to detect general prompt injection attacks, but none have been systematically evaluated for web agents. In this work, we bridge this gap by presenting the first...

AutoML in Cybersecurity: An Empirical Study

Automated machine learning AutoML has emerged as a promising paradigm for automating machine learning ML pipeline design, broadening AI adoption. Yet its reliability in complex domains such as cybersecurity remains underexplored. This paper systematically evaluates eight open-source AutoML...

EvoMail: Self-Evolving Cognitive Agents for Adaptive Spam and Phishing Email Defense

Modern email spam and phishing attacks have evolved far beyond keyword blacklists or simple heuristics. Adversaries now craft multi-modal campaigns that combine natural-language text with obfuscated URLs, forged headers, and malicious attachments, adapting their strategies within days to bypass...

Ensembling Large Language Models for Code Vulnerability Detection: an Empirical Evaluation

Code vulnerability detection is crucial for ensuring the security and reliability of modern software systems. Recently, Large Language Models LLMs have shown promising capabilities in this domain. However, notable discrepancies in detection results often arise when analyzing identical code segmen...

Flow-Based Detection and Identification of Zero-Day IoT Cameras

The majority of consumer IoT devices lack mechanisms for administrators to monitor and control them, hindering tailored security policies. A key challenge is identifying whether a new device, especially a streaming IoT camera, has joined the network. We present zCamInspector, a system for...

A Framework for Detection and Classification of Attacks on Surveillance Cameras under IoT Networks

The increasing use of Internet of Things IoT devices has led to a rise in security related concerns regarding IoT Networks. The surveillance cameras in IoT networks are vulnerable to security threats such as brute force and zero-day attacks which can lead to unauthorized access by hackers and...

A Survey of Threats against Voice Authentication and Anti-Spoofing Systems

Voice authentication has undergone significant changes from traditional systems that relied on handcrafted acoustic features to deep learning models that can extract robust speaker embeddings. This advancement has expanded its applications across finance, smart devices, law enforcement, and beyon...

When Machine Learning Meets Vulnerability Discovery: Challenges and Lessons Learned

In recent years, machine learning has demonstrated impressive results in various fields, including software vulnerability detection. Nonetheless, using machine learning to identify software vulnerabilities presents new challenges, especially regarding the scale of data involved, which was not a...

Consiglieres in the Shadow: Understanding the Use of Uncensored Large Language Models in Cybercrimes

The advancement of AI technologies, particularly Large Language Models LLMs, has transformed computing while introducing new security and privacy risks. Prior research shows that cybercriminals are increasingly leveraging uncensored LLMs ULLMs as backends for malicious services. Understanding the...

A Robust Cross-Domain IDS Using BiGRU-LSTM-Attention for Medical and Industrial IoT Security

The increased Internet of Medical Things IoMT and the Industrial Internet of Things IIoT interconnectivity has introduced complex cybersecurity challenges, exposing sensitive data, patient safety, and industrial operations to advanced cyber threats. To mitigate these risks, this paper introduces ...

MCPSecBench: a Systematic Security Benchmark and Playground for Testing Model Context Protocols

Large Language Models LLMs are increasingly integrated into real-world applications via the Model Context Protocol MCP, a universal, open standard for connecting AI agents with data sources and external tools. While MCP enhances the capabilities of LLM-based agents, it also introduces new securit...

Causal Graph Profiling Via Structural Divergence for Robust Anomaly Detection in Cyber-Physical Systems

With the growing complexity of cyberattacks targeting critical infrastructures such as water treatment networks, there is a pressing need for robust anomaly detection strategies that account for both system vulnerabilities and evolving attack patterns. Traditional methods -- statistical,...

SVC 2025: the First Multimodal Deception Detection Challenge

Deception detection is a critical task in real-world applications such as security screening, fraud prevention, and credibility assessment. While deep learning methods have shown promise in surpassing human-level performance, their effectiveness often depends on the availability of high-quality a...

Log2Sig: Frequency-Aware Insider Threat Detection Via Multivariate Behavioral Signal Decomposition

Insider threat detection presents a significant challenge due to the deceptive nature of malicious behaviors, which often resemble legitimate user operations. However, existing approaches typically model system logs as flat event sequences, thereby failing to capture the inherent frequency dynami...

A Survey on Data Security in Large Language Models

Large Language Models LLMs, now a foundation in advancing natural language processing, power applications such as text generation, machine translation, and conversational systems. Despite their transformative potential, these models inherently rely on massive amounts of training data, often...