PT-2025-51640

Name of the Vulnerable Software and Affected Versions Linux kernel affected versions not specified Description A flaw exists in the Linux kernel’s MPTCP implementation related to protocol fallback detection with BPF. The issue arises when a server has MPTCP enabled, but a client sends a TCP SYN...

Intrusion Detection in Internet of Vehicles Using Machine Learning

The Internet of Vehicles IoV has evolved modern transportation through enhanced connectivity and intelligent systems. However, this increased connectivity introduces critical vulnerabilities, making vehicles susceptible to cyber-attacks such Denial-ofService DoS and message spoofing. This project...

Linux kernel 安全漏洞

Linux kernel is the kernel used by Linux, the open source operating system of the Linux Foundation in the United States. A security vulnerability exists in the Linux kernel that stems from improper serial device detection and could lead to null pointer dereferencing...

Security and Detectability Analysis of Unicode Text Watermarking Methods against Large Language Models

Securing digital text is becoming increasingly relevant due to the widespread use of large language models. Individuals' fear of losing control over data when it is being used to train such machine learning models or when distinguishing model-generated output from text written by humans. Digital...

Behavior-Aware and Generalizable Defense against Black-Box Adversarial Attacks for ML-Based IDS

Machine learning based intrusion detection systems are increasingly targeted by black box adversarial attacks, where attackers craft evasive inputs using indirect feedback such as binary outputs or behavioral signals like response time and resource usage. While several defenses have been proposed...

Hyperparameter Tuning-Based Optimized Performance Analysis of Machine Learning Algorithms for Network Intrusion Detection

Network Intrusion Detection Systems NIDS are essential for securing networks by identifying and mitigating unauthorized activities indicative of cyberattacks. As cyber threats grow increasingly sophisticated, NIDS must evolve to detect both emerging threats and deviations from normal behavior. Th...

FiD-QAE: A Fidelity-Driven Quantum Autoencoder for Credit Card Fraud Detection

Credit card fraud detection is a critical task in financial security, as fraudulent transactions are rare, highly imbalanced, and often resemble legitimate ones. A wide range of classical machine learning methods, as well as more recent quantum machine learning approaches, have been investigated ...

Detecting Malicious Entra OAuth Apps with LLM-Based Permission Risk Scoring

This project presents a unified detection framework that constructs a complete corpus of Microsoft Graph permissions, generates consistent LLM-based risk scores, and integrates them into a real-time detection engine to identify malicious OAuth consent activity...

Taint-Based Code Slicing for LLMs-Based Malicious NPM Package Detection

The increasing sophistication of malware attacks in the npm ecosystem, characterized by obfuscation and complex logic, necessitates advanced detection methods. Recently, researchers have turned their attention from traditional detection approaches to Large Language Models LLMs due to their strong...

Diverse LLMs Vs. Vulnerabilities: Who Detects and Fixes Them Better?

Large Language Models LLMs are increasingly being studied for Software Vulnerability Detection SVD and Repair SVR. Individual LLMs have demonstrated code understanding abilities, but they frequently struggle when identifying complex vulnerabilities and generating fixes. This study presents...

EUVD-2023-32746

Malwarebytes 1.0.14 for Linux doesn't properly compute signatures in some scenarios. This allows a bypass of detection...

CVE-2023-29144

Malwarebytes 1.0.14 for Linux doesn't properly compute signatures in some scenarios. This allows a bypass of detection...

CVE-2025-66516: Detecting and Defending Against Apache Tika XXE Attack

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HackTheBox-Penetration-Testing-Methodology

HackTheBox Penetration Testing Methodology by 9mmpterodacty...

Smartbedded Meteobridge Web Detection

Binary data smartbeddedmeteobridgewebdetect.nbin...

PHANTOM: Progressive High-Fidelity Adversarial Network for Threat Object Modeling

The scarcity of cyberattack data hinders the development of robust intrusion detection systems. This paper introduces PHANTOM, a novel adversarial variational framework for generating high-fidelity synthetic attack data. Its innovations include progressive training, a dual-path VAE-GAN...

CVE-2023-29144

Malwarebytes 1.0.14 for Linux doesn't properly compute signatures in some scenarios. This allows a bypass of detection...

Quantum-Augmented AI/ML for O-RAN: Hierarchical Threat Detection with Synergistic Intelligence and Interpretability (Technical Report)

Open Radio Access Networks O-RAN enhance modularity and telemetry granularity but also widen the cybersecurity attack surface across disaggregated control, user and management planes. We propose a hierarchical defense framework with three coordinated layers-anomaly detection, intrusion...

CVE-2023-29144

Affected software: Malwarebytes 1.0.14 for Linux. Vulnerability: does not properly compute signatures in some scenarios, allowing a bypass of malware detection. Impact: local bypass of detection is implied by the description. Root cause: incorrect calculation of signatures. Exploitation status: n...

PT-2025-50956

Name of the Vulnerable Software and Affected Versions Malwarebytes version 1.0.14 Description The software does not correctly calculate signatures in certain situations, leading to a potential bypass of malware detection. Recommendations Update to a newer version that contains a fix for this...