Machine Learning Techniques for Enhancing Quantum Key Distribution

Quantum Key Distribution QKD offers theoretically unbreakable security by leveraging quantum mechanics. However, practical implementation is challenged by environmental vulnerabilities, noise, and hardware imperfections. Recently, Machine Learning ML has emerged as a powerful tool to address thes...

Unknown Attack Detection in IoT Networks Using Large Language Models: A Robust, Data-Efficient Approach

The rapid evolution of cyberattacks continues to drive the emergence of unknown zero-day threats, posing significant challenges for network intrusion detection systems in Internet of Things IoT networks. Existing machine learning and deep learning approaches typically rely on large labeled...

SQL-Injection-Detector-and-Prevention

SQL-Injection-...

PrivFly: A Privacy-Preserving Self-Supervised Framework for Rare Attack Detection in IoFT

The Internet of Flying Things IoFT plays a vital role in modern applications such as aerial surveillance and smart mobility. However, it remains highly vulnerable to cyberattacks that threaten the confidentiality, integrity, and availability of sensitive data. Developing effective intrusion...

ai_bouncer

AiBouncer AI-powered HTTP request classification for Ruby on...

CVE-2026-21876

The OWASP core rule set CRS is a set of generic attack detection rules for use with compatible web application firewalls. Prior to versions 4.22.0 and 3.3.8, the current rule 922110 has a bug when processing multipart requests with multiple parts. When the first rule in a chain iterates over a...

tcpdump 4.99.6

tcpdump allows you to dump the traffic on a network. It can be used to print out the headers and/or contents of packets on a network interface that matches a given expression. You can use this tool to track down network problems, to detect many attacks, or to monitor the network activities...

Introducing Nylon Face Mask Attacks: A Dataset for Evaluating Generalised Face Presentation Attack Detection

Face recognition systems are increasingly deployed across a wide range of applications, including smartphone authentication, access control, and border security. However, these systems remain vulnerable to presentation attacks PAs, which can significantly compromise their reliability. In this wor...

Injecting Falsehoods: Adversarial Man-In-The-Middle Attacks Undermining Factual Recall in LLMs

LLMs are now an integral part of information retrieval. As such, their role as question answering chatbots raises significant concerns due to their shown vulnerability to adversarial man-in-the-middle MitM attacks. Here, we propose the first principled attack evaluation on LLM factual memory unde...

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...

Penetrating the Hostile: Detecting DeFi Protocol Exploits through Cross-Contract Analysis

Decentralized finance DeFi protocols are crypto projects developed on the blockchain to manage digital assets. Attacks on DeFi have been frequent and have resulted in losses exceeding $80 billion. Current tools detect and locate possible vulnerabilities in contracts by analyzing the state changes...

Coordinated Position Falsification Attacks and Countermeasures for Location-Based Services

With the rise of location-based service LBS applications that rely on terrestrial and satellite infrastructures e.g., GNSS and crowd-sourced Wi-Fi, Bluetooth, cellular, and IP databases for positioning, ensuring their integrity and security is paramount. However, we demonstrate that these...

LLM-Based Multi-Class Attack Analysis and Mitigation Framework in IoT/IIoT Networks

The Internet of Things has expanded rapidly, transforming communication and operations across industries but also increasing the attack surface and security breaches. Artificial Intelligence plays a key role in securing IoT, enabling attack detection, attack behavior analysis, and mitigation...

From ECU to VSOC: UDS Security Monitoring Strategies

Increasing complexity and connectivity of modern vehicles have heightened their vulnerability to cyberattacks. This paper addresses security challenges associated with the Unified Diagnostic Services UDS protocol, a critical communication framework for vehicle diagnostics in the automotive...

DRsam: Detection of Fault-Based Microarchitectural Side-Channel Attacks in RISC-V Using Statistical Preprocessing and Association Rule Mining

RISC-V processors are becoming ubiquitous in critical applications, but their susceptibility to microarchitectural side-channel attacks is a serious concern. Detection of microarchitectural attacks in RISC-V is an emerging research topic that is relatively underexplored, compared to x86 and ARM...

PoTS: Proof-Of-Training-Steps for Backdoor Detection in Large Language Models

As Large Language Models LLMs gain traction across critical domains, ensuring secure and trustworthy training processes has become a major concern. Backdoor attacks, where malicious actors inject hidden triggers into training data, are particularly insidious and difficult to detect. Existing...

EUVD-2007-3695

Malware in sbrugna...

EUVD-2020-23220

Malware in sbrugna...

CVE-2025-38512

In the Linux kernel, the following vulnerability has been resolved: wifi: prevent A-MSDU attacks in mesh networks This patch is a mitigation to prevent the A-MSDU spoofing vulnerability for mesh networks. The initial update to the IEEE 802.11 standard, in response to the FragAttacks, missed this...

A Hierarchical IDS for Zero-Day Attack Detection in Internet of Medical Things Networks

The Internet of Medical Things IoMT is driving a healthcare revolution but remains vulnerable to cyberattacks such as denial of service, ransomware, data hijacking, and spoofing. These networks comprise resource constrained, heterogeneous devices e.g., wearable sensors, smart pills, implantables,...