EtherBee: a Global Dataset of Ethereum Node Performance Measurements Coupled with Honeypot Interactions and Full Network Sessions

We introduce EtherBee, a global dataset integrating detailed Ethereum node metrics, network traffic metadata, and honeypot interaction logs collected from ten geographically diverse vantage points over three months. By correlating node data with granular network sessions and security events,...

Sec5GLoc: Securing 5G Indoor Localization Via Adversary-Resilient Deep Learning Architecture

Emerging 5G millimeter-wave and sub-6 GHz networks enable high-accuracy indoor localization, but security and privacy vulnerabilities pose serious challenges. In this paper, we identify and address threats including location spoofing and adversarial signal manipulation against 5G-based indoor...

CVE-2021-26702

EPrints 3.4.2 exposes a reflected XSS opportunity in the dataset parameter to the cgi/datasetdictionary URI...

CVE-2021-37839

Apache Superset up to 1.5.1 allowed for authenticated users to access metadata information related to datasets they have no permission on. This metadata included the dataset name, columns and metrics...

MAL-2025-4148 Malicious code in @confluent-cfet-medusa/dataset-designer (npm)

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Malicious code in @confluent-cfet-medusa/dataset-designer (npm)

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A Scalable Hierarchical Intrusion Detection System for Internet of Vehicles

Due to its nature of dynamic, mobility, and wireless data transfer, the Internet of Vehicles IoV is prone to various cyber threats, ranging from spoofing and Distributed Denial of Services DDoS attacks to malware. To safeguard the IoV ecosystem from intrusions, malicious activities, policy...

Unsupervised Network Anomaly Detection with Autoencoders and Traffic Images

Due to the recent increase in the number of connected devices, the need to promptly detect security issues is emerging. Moreover, the high number of communication flows creates the necessity of processing huge amounts of data. Furthermore, the connected devices are heterogeneous in nature, having...

ReCopilot: Reverse Engineering Copilot in Binary Analysis

Binary analysis plays a pivotal role in security domains such as malware detection and vulnerability discovery, yet it remains labor-intensive and heavily reliant on expert knowledge. General-purpose large language models LLMs perform well in programming analysis on source code, while...

Hybrid Audio Detection Using Fine-Tuned Audio Spectrogram Transformers: a Dataset-Driven Evaluation of Mixed AI-Human Speech

The rapid advancement of artificial intelligence AI has enabled sophisticated audio generation and voice cloning technologies, posing significant security risks for applications reliant on voice authentication. While existing datasets and models primarily focus on distinguishing between human and...

FragFake: a Dataset for Fine-Grained Detection of Edited Images with Vision Language Models

Fine-grained edited image detection of localized edits in images is crucial for assessing content authenticity, especially given that modern diffusion models and image editing methods can produce highly realistic manipulations. However, this domain faces three challenges: 1 Binary classifiers yie...

Evaluating the Efficacy of LLM Safety Solutions : the Palit Benchmark Dataset

Large Language Models LLMs are increasingly integrated into critical systems in industries like healthcare and finance. Users can often submit queries to LLM-enabled chatbots, some of which can enrich responses with information retrieved from internal databases storing sensitive data. This gives...

FedGraM: Defending against Untargeted Attacks in Federated Learning Via Embedding Gram Matrix

Federated Learning FL enables geographically distributed clients to collaboratively train machine learning models by sharing only their local models, ensuring data privacy. However, FL is vulnerable to untargeted attacks that aim to degrade the global model's performance on the underlying data...

Adaptive Pruning of Deep Neural Networks for Resource-Aware Embedded Intrusion Detection on the Edge

Artificial neural network pruning is a method in which artificial neural network sizes can be reduced while attempting to preserve the predicting capabilities of the network. This is done to make the model smaller or faster during inference time. In this work we analyze the ability of a selection...

Think Twice Before You Act: Enhancing Agent Behavioral Safety with Thought Correction

LLM-based autonomous agents possess capabilities such as reasoning, tool invocation, and environment interaction, enabling the execution of complex multi-step tasks. The internal reasoning process, i.e., thought, of behavioral trajectory significantly influences tool usage and subsequent actions...

Improving LLM Outputs against Jailbreak Attacks with Expert Model Integration

Using LLMs in a production environment presents security challenges that include vulnerabilities to jailbreaks and prompt injections, which can result in harmful outputs for humans or the enterprise. The challenge is amplified when working within a specific domain, as topics generally accepted fo...

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense against High-Ratio Malicious Clients

Federated learning FL is gaining increasing attention as an emerging collaborative machine learning approach, particularly in the context of large-scale computing and data systems. However, the fundamental algorithm of FL, Federated Averaging FedAvg, is susceptible to backdoor attacks. Although...

MalVis: a Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification

As technology advances, Android malware continues to pose significant threats to devices and sensitive data. The open-source nature of the Android OS and the availability of its SDK contribute to this rapid growth. Traditional malware detection techniques, such as signature-based, static, and...

GenoArmory: a Unified Evaluation Framework for Adversarial Attacks on Genomic Foundation Models

We propose the first unified adversarial attack benchmark for Genomic Foundation Models GFMs, named GenoArmory. Unlike existing GFM benchmarks, GenoArmory offers the first comprehensive evaluation framework to systematically assess the vulnerability of GFMs to adversarial attacks. Methodologicall...

SecReEvalBench: a Multi-Turned Security Resilience Evaluation Benchmark for Large Language Models

The increasing deployment of large language models in security-sensitive domains necessitates rigorous evaluation of their resilience against adversarial prompt-based attacks. While previous benchmarks have focused on security evaluations with limited and predefined attack domains, such as...