3137 matches found
Automated and Explainable Denial of Service Analysis for AI-Driven Intrusion Detection Systems
With the increasing frequency and sophistication of Distributed Denial of Service DDoS attacks, it has become critical to develop more efficient and interpretable detection methods. Traditional detection systems often struggle with scalability and transparency, hindering real-time response and...
SHIELD: Securing Healthcare IoT with Efficient Machine Learning Techniques for Anomaly Detection
The integration of IoT devices in healthcare introduces significant security and reliability challenges, increasing susceptibility to cyber threats and operational anomalies. This study proposes a machine learning-driven framework for 1 detecting malicious cyberattacks and 2 identifying faulty...
WordPress OOPSpam Anti-Spam plugin IP Header Forgery Vulnerability
WordPress OOPSpam Anti-Spam plugin is an anti-spam plugin designed for WordPress that protects forms and comments from spam through AI and machine learning techniques without the use of CAPTCHA validation. The WordPress OOPSpam Anti-Spam plugin suffers from an IP header forgery vulnerability that...
Detecting Vulnerabilities from Issue Reports for Internet-Of-Things
Timely identification of issue reports reflecting software vulnerabilities is crucial, particularly for Internet-of-Things IoT where analysis is slower than non-IoT systems. While Machine Learning ML and Large Language Models LLMs detect vulnerability-indicating issues in non-IoT systems, their I...
Machine and Deep Learning for Indoor UWB Jammer Localization
Ultra-wideband UWB localization delivers centimeter-scale accuracy but is vulnerable to jamming attacks, creating security risks for asset tracking and intrusion detection in smart buildings. Although machine learning ML and deep learning DL methods have improved tag localization, localizing...
Android Malware Detection: A Machine Learning Approach
This study examines machine learning techniques like Decision Trees, Support Vector Machines, Logistic Regression, Neural Networks, and ensemble methods to detect Android malware. The study evaluates these models on a dataset of Android applications and analyzes their accuracy, efficiency, and...
MH-1M: A 1.34 Million-Sample Comprehensive Multi-Feature Android Malware Dataset for Machine Learning, Deep Learning, Large Language Models, and Threat Intelligence Research
We present MH-1M, one of the most comprehensive and up-to-date datasets for advanced Android malware research. The dataset comprises 1,340,515 applications, encompassing a wide range of features and extensive metadata. To ensure accurate malware classification, we employ the VirusTotal API,...
EUVD-2025-36706
MLflow Weak Password Requirements Authentication Bypass Vulnerability. This vulnerability allows remote attackers to bypass authentication on affected installations of MLflow. Authentication is not required to exploit this vulnerability. The specific flaw exists within the handling of passwords...
An In-Depth Analysis of Cyber Attacks in Secured Platforms
There is an increase in global malware threats. To address this, an encryption-type ransomware has been introduced on the Android operating system. The challenges associated with malicious threats in phone use have become a pressing issue in mobile communication, disrupting user experiences and...
The Attribution Story of WhisperGate: An Academic Perspective
This paper explores the challenges of cyberattack attribution, specifically APTs, applying the case study approach for the WhisperGate cyber operation of January 2022 executed by the Russian military intelligence service GRU and targeting Ukrainian government entities. The study provides a detail...
A Hard-Label Black-Box Evasion Attack against ML-Based Malicious Traffic Detection Systems
Machine Learning ML-based malicious traffic detection is a promising security paradigm. It outperforms rule-based traditional detection by identifying various advanced attacks. However, the robustness of these ML models is largely unexplored, thereby allowing attackers to craft adversarial traffi...
Injection, Attack and Erasure: Revocable Backdoor Attacks Via Machine Unlearning
Backdoor attacks pose a persistent security risk to deep neural networks DNNs due to their stealth and durability. While recent research has explored leveraging model unlearning mechanisms to enhance backdoor concealment, existing attack strategies still leave persistent traces that may be detect...
New Machine Learning Approaches for Intrusion Detection in ADS-B
With the growing reliance on the vulnerable Automatic Dependent Surveillance-Broadcast ADS-B protocol in air traffic management ATM, ensuring security is critical. This study investigates emerging machine learning models and training strategies to improve AI-based intrusion detection systems IDS...
A Demonstration of Self-Adaptive Jamming Attack Detection in AI/ML Integrated O-RAN
The open radio access network O-RAN enables modular, intelligent, and programmable 5G network architectures through the adoption of software-defined networking, network function virtualization, and implementation of standardized open interfaces. However, one of the security concerns for O-RAN,...
EUVD-2021-0376
Malware in sbrugna...
EUVD-2021-0398
Malware in sbrugna...
EUVD-2021-0391
Malware in sbrugna...
EUVD-2021-0364
Malware in sbrugna...