A Bayesian Incentive Mechanism for Poison-Resilient Federated Learning

Federated learning FL enables collaborative model training across decentralized clients while preserving data privacy. However, its open-participation nature exposes it to data-poisoning attacks, in which malicious actors submit corrupted model updates to degrade the global model. Existing defens...

An Investigation of Ear-EEG Signals for a Novel Biometric Authentication System

This work explores the feasibility of biometric authentication using EEG signals acquired through in-ear devices, commonly referred to as ear-EEG. Traditional EEG-based biometric systems, while secure, often suffer from low usability due to cumbersome scalp-based electrode setups. In this study, ...

The Man behind the Sound: Demystifying Audio Private Attribute Profiling Via Multimodal Large Language Model Agents

Our research uncovers a novel privacy risk associated with multimodal large language models MLLMs: the ability to infer sensitive personal attributes from audio data -- a technique we term audio private attribute profiling. This capability poses a significant threat, as audio can be covertly...

PhreshPhish: a Real-World, High-Quality, Large-Scale Phishing Website Dataset and Benchmark

Phishing remains a pervasive and growing threat, inflicting heavy economic and reputational damage. While machine learning has been effective in real-time detection of phishing attacks, progress is hindered by lack of large, high-quality datasets and benchmarks. In addition to poor-quality due to...

REAL-IoT: Characterizing GNN Intrusion Detection Robustness under Practical Adversarial Attack

Graph Neural Network GNN-based network intrusion detection systems NIDS are often evaluated on single datasets, limiting their ability to generalize under distribution drift. Furthermore, their adversarial robustness is typically assessed using synthetic perturbations that lack realism. This...

Accelerating Automatic Program Repair with Dual Retrieval-Augmented Fine-Tuning and Patch Generation on Large Language Models

Automated Program Repair APR is essential for ensuring software reliability and quality while enhancing efficiency and reducing developers' workload. Although rule-based and learning-based APR methods have demonstrated their effectiveness, their performance was constrained by the defect type of...

Exploit for SQL Injection in Phpgurukul Student_Result_Management_System

PoCVulDb PoC of CVEs 4m3rr0r CVE-2025-7534https://github.c...

DATABench: Evaluating Dataset Auditing in Deep Learning from an Adversarial Perspective

The widespread application of Deep Learning across diverse domains hinges critically on the quality and composition of training datasets. However, the common lack of disclosure regarding their usage raises significant privacy and copyright concerns. Dataset auditing techniques, which aim to...

MalVol-25: a Diverse, Labelled and Detailed Volatile Memory Dataset for Malware Detection and Response Testing and Validation

This paper addresses the critical need for high-quality malware datasets that support advanced analysis techniques, particularly machine learning and agentic AI frameworks. Existing datasets often lack diversity, comprehensive labelling, and the complexity necessary for effective machine learning...

ZKPROV: a Zero-Knowledge Approach to Dataset Provenance for Large Language Models

As the deployment of large language models LLMs grows in sensitive domains, ensuring the integrity of their computational provenance becomes a critical challenge, particularly in regulated sectors such as healthcare, where strict requirements are applied in dataset usage. We introduce ZKPROV, a...

Intelligent ARP Spoofing Detection Using Multi-Layered Machine Learning (ML) Techniques for IoT Networks

Address Resolution Protocol ARP spoofing remains a critical threat to IoT networks, enabling attackers to intercept, modify, or disrupt data transmission by exploiting ARP's lack of authentication. The decentralized and resource-constrained nature of IoT environments amplifies this vulnerability,...

HARPT: a Corpus for Analyzing Consumers' Trust and Privacy Concerns in Mobile Health Apps

We present HARPT, a large-scale annotated corpus of mobile health app store reviews aimed at advancing research in user privacy and trust. The dataset comprises over 480,000 user reviews labeled into seven categories that capture critical aspects of trust in applications, trust in providers and...

SmartHome-Bench: a Comprehensive Benchmark for Video Anomaly Detection in Smart Homes Using Multi-Modal Large Language Models

Video anomaly detection VAD is essential for enhancing safety and security by identifying unusual events across different environments. Existing VAD benchmarks, however, are primarily designed for general-purpose scenarios, neglecting the specific characteristics of smart home applications. To...

FAME: a Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes

The widespread emergence of face-swap Deepfake videos poses growing risks to digital security, privacy, and media integrity, necessitating effective forensic tools for identifying the source of such manipulations. Although most prior research has focused primarily on binary Deepfake detection, th...

Leveraging GPT-4 for Vulnerability-Witnessing Unit Test Generation

In the life-cycle of software development, testing plays a crucial role in quality assurance. Proper testing not only increases code coverage and prevents regressions but it can also ensure that any potential vulnerabilities in the software are identified and effectively fixed. However, creating...

Today'S Cat Is Tomorrow'S Dog: Accounting for Time-Based Changes in the Labels of ML Vulnerability Detection Approaches

Vulnerability datasets used for ML testing implicitly contain retrospective information. When tested on the field, one can only use the labels available at the time of training and testing e.g. seen and assumed negatives. As vulnerabilities are discovered across calendar time, labels change and...

Optimizing Resource Allocation and Energy Efficiency in Federated Fog Computing for IoT

Address Resolution Protocol ARP spoofing attacks severely threaten Internet of Things IoT networks by allowing attackers to intercept, modify, or block communications. Traditional detection methods are insufficient due to high false positives and poor adaptability. This research proposes a...

A Lightweight IDS for Early APT Detection Using a Novel Feature Selection Method

An Advanced Persistent Threat APT is a multistage, highly sophisticated, and covert form of cyber threat that gains unauthorized access to networks to either steal valuable data or disrupt the targeted network. These threats often remain undetected for extended periods, emphasizing the critical...

SoK: Automated Vulnerability Repair: Methods, Tools, and Assessments

The increasing complexity of software has led to the steady growth of vulnerabilities. Vulnerability repair investigates how to fix software vulnerabilities. Manual vulnerability repair is labor-intensive and time-consuming because it relies on human experts, highlighting the importance of...

Secure Energy Transactions Using Blockchain Leveraging AI for Fraud Detection and Energy Market Stability

Peer-to-peer trading and the move to decentralized grids have reshaped the energy markets in the United States. Notwithstanding, such developments lead to new challenges, mainly regarding the safety and authenticity of energy trade. This study aimed to develop and build a secure, intelligent, and...