8941 matches found
Interpretable Anomaly Detection in Encrypted Traffic Using SHAP with Machine Learning Models
The widespread adoption of encrypted communication protocols such as HTTPS and TLS has enhanced data privacy but also rendered traditional anomaly detection techniques less effective, as they often rely on inspecting unencrypted payloads. This study aims to develop an interpretable machine...
Password Strength Detection Via Machine Learning: Analysis, Modeling, and Evaluation
As network security issues continue gaining prominence, password security has become crucial in safeguarding personal information and network systems. This study first introduces various methods for system password cracking, outlines password defense strategies, and discusses the application of...
CoTSRF: Utilize Chain of Thought As Stealthy and Robust Fingerprint of Large Language Models
Despite providing superior performance, open-source large language models LLMs are vulnerable to abusive usage. To address this issue, recent works propose LLM fingerprinting methods to identify the specific source LLMs behind suspect applications. However, these methods fail to provide stealthy...
CVE-2005-4338
announcement.pl in Blackboard Learning and Community Portal System in Academic Suite 6.3.1.424, 6.2.3.23, and other versions before 6 allows remote attackers to gain administrator privileges by setting the context parameter to "admin"...
CVE-2005-4339
Cross-site scripting XSS vulnerability in Blackboard Learning and Community Portal System in Academic Suite 6.3.1.424, 6.2.3.23, and other versions before 6 allows remote attackers to inject arbitrary web script or HTML via the context parameter to announcement.pl, which is reflected in the...
CVE-2005-4341
Blackboard Learning and Community Portal System in Academic Suite 6.3.1.424, 6.2.3.23, and other versions before 6 allows remote attackers to list all available categories via a blank categoryid parameter to category.pl. NOTE: it is not clear whether this information is sensitive or not, so this...
CVE-2005-4337
The login page in Blackboard Learning and Community Portal System in Academic Suite 6.3.1.424, 6.2.3.23, and other versions before 6 allows remote attackers to bypass authentication and gain privileges as other users via a modified userid parameter and a "/" in the encodedpw parameter...
Reliable Disentanglement Multi-View Learning against View Adversarial Attacks
Trustworthy multi-view learning has attracted extensive attention because evidence learning can provide reliable uncertainty estimation to enhance the credibility of multi-view predictions. Existing trusted multi-view learning methods implicitly assume that multi-view data is secure. However, in...
Federated Learning-Enhanced Blockchain Framework for Privacy-Preserving Intrusion Detection in Industrial IoT
Industrial Internet of Things IIoT systems have become integral to smart manufacturing, yet their growing connectivity has also exposed them to significant cybersecurity threats. Traditional intrusion detection systems IDS often rely on centralized architectures that raise concerns over data...
A Survey on Secure Machine Learning
In this survey, we will explore the interaction between secure multiparty computation and the area of machine learning. Recent advances in secure multiparty computation MPC have significantly improved its applicability in the realm of machine learning ML, offering robust solutions for...
PT-2025-53456
Уязвимость виртуальной обучающей среды Moodle связана с непринятием мер по защите структуры веб-страницы. Эксплуатация уязвимости может позволить нарушителю, действующему удаленно, провести атаку межсайтового скриптинга XSS...
EC-LDA : Label Distribution Inference Attack against Federated Graph Learning with Embedding Compression
Graph Neural Networks GNNs have been widely used for graph analysis. Federated Graph Learning FGL is an emerging learning framework to collaboratively train graph data from various clients. However, since clients are required to upload model parameters to the server in each round, this provides t...
AI-Driven Dynamic Firewall Optimization Using Reinforcement Learning for Anomaly Detection and Prevention
The growing complexity of cyber threats has rendered static firewalls increasingly ineffective for dynamic, real-time intrusion prevention. This paper proposes a novel AI-driven dynamic firewall optimization framework that leverages deep reinforcement learning DRL to autonomously adapt and update...
Covert Attacks on Machine Learning Training in Passively Secure MPC
Secure multiparty computation MPC allows data owners to train machine learning models on combined data while keeping the underlying training data private. The MPC threat model either considers an adversary who passively corrupts some parties without affecting their overall behavior, or an adversa...
Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption
Federated Learning FL is susceptible to privacy attacks, such as data reconstruction attacks, in which a semi-honest server or a malicious client infers information about other clients' datasets from their model updates or gradients. To enhance the privacy of FL, recent studies combined Multi-Key...
CSAGC-IDS: a Dual-Module Deep Learning Network Intrusion Detection Model for Complex and Imbalanced Data
As computer networks proliferate, the gravity of network intrusions has escalated, emphasizing the criticality of network intrusion detection systems for safeguarding security. While deep learning models have exhibited promising results in intrusion detection, they face challenges in managing...
Vulnerability of Transfer-Learned Neural Networks to Data Reconstruction Attacks in Small-Data Regime
Training data reconstruction attacks enable adversaries to recover portions of a released model's training data. We consider the attacks where a reconstructor neural network learns to invert the random mapping between training data and model weights. Prior work has shown that an informed adversar...
Neuromorphic Mimicry Attacks Exploiting Brain-Inspired Computing for Covert Cyber Intrusions
Neuromorphic computing, inspired by the human brain's neural architecture, is revolutionizing artificial intelligence and edge computing with its low-power, adaptive, and event-driven designs. However, these unique characteristics introduce novel cybersecurity risks. This paper proposes...
WordPress Plugin Masteriyo – LMS security vulnerabilities
WordPress and WordPress plugin are both products of the WordPress Foundation.WordPress is a blogging platform developed using the PHP language. The platform supports setting up personal blog sites on servers with PHP and MySQL.WordPress plugin is an application plug-in. A security vulnerability...