1398 matches found
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...
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...
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...
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...
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...
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...
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...
From LLMs to MLLMs to Agents: a Survey of Emerging Paradigms in Jailbreak Attacks and Defenses within LLM Ecosystem
Large language models LLMs are rapidly evolving from single-modal systems to multimodal LLMs and intelligent agents, significantly expanding their capabilities while introducing increasingly severe security risks. This paper presents a systematic survey of the growing complexity of jailbreak...
LLM-Powered Intent-Based Categorization of Phishing Emails
Phishing attacks remain a significant threat to modern cybersecurity, as they successfully deceive both humans and the defense mechanisms intended to protect them. Traditional detection systems primarily focus on email metadata that users cannot see in their inboxes. Additionally, these systems...
CertDW: Towards Certified Dataset Ownership Verification via Conformal Prediction
Deep neural networks DNNs rely heavily on high-quality open-source datasets e.g., ImageNet for their success, making dataset ownership verification DOV crucial for protecting public dataset copyrights. In this paper, we find existing DOV methods implicitly assume that the verification process is...
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...
Navigating the Deep: Signature Extraction on Deep Neural Networks
Neural network model extraction has emerged in recent years as an important security concern, as adversaries attempt to recover a network's parameters via black-box queries. A key step in this process is signature extraction, which aims to recover the absolute values of the network's weights laye...
Efficient Malware Detection with Optimized Learning on High-Dimensional Features
Malware detection using machine learning requires feature extraction from binary files, as models cannot process raw binaries directly. A common approach involves using LIEF for raw feature extraction and the EMBER vectorizer to generate 2381-dimensional feature vectors. However, the high...