Think Fast: Real-Time IoT Intrusion Reasoning Using IDS and LLMs at the Edge Gateway

As the number of connected IoT devices continues to grow, securing these systems against cyber threats remains a major challenge, especially in environments with limited computational and energy resources. This paper presents an edge-centric Intrusion Detection System IDS framework that integrate...

Systematically Deconstructing APVD Steganography and Its Payload with a Unified Deep Learning Paradigm

In the era of digital communication, steganography allows covert embedding of data within media files. Adaptive Pixel Value Differencing APVD is a steganographic method valued for its high embedding capacity and invisibility, posing challenges for traditional steganalysis. This paper proposes a...

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...

Network Intrusion Detection: Evolution from Conventional Approaches to LLM Collaboration and Emerging Risks

This survey systematizes the evolution of network intrusion detection systems NIDS, from conventional methods such as signature-based and neural network NN-based approaches to recent integrations with large language models LLMs. It clearly and concisely summarizes the current status, strengths, a...

Cyberattack Detection in Critical Infrastructure and Supply Chains

Cyberattack detection in Critical Infrastructure and Supply Chains has become challenging in Industry 4.0. Intrusion Detection Systems IDS are deployed to counter the cyberattacks. However, an IDS effectively detects attacks based on the known signatures and patterns, Zero-day attacks go...

RoBCtrl: Attacking GNN-Based Social Bot Detectors Via Reinforced Manipulation of Bots Control Interaction

Social networks have become a crucial source of real-time information for individuals. The influence of social bots within these platforms has garnered considerable attention from researchers, leading to the development of numerous detection technologies. However, the vulnerability and robustness...

ShuffleV: A Microarchitectural Defense Strategy against Electromagnetic Side-Channel Attacks in Microprocessors

The run-time electromagnetic EM emanation of microprocessors presents a side-channel that leaks the confidentiality of the applications running on them. Many recent works have demonstrated successful attacks leveraging such side-channels to extract the confidentiality of diverse applications, suc...

Lightweight CNN-Based Wi-Fi Intrusion Detection Using 2D Traffic Representations

Wi-Fi networks are ubiquitous in both home and enterprise environments, serving as a primary medium for Internet access and forming the backbone of modern IoT ecosystems. However, their inherent vulnerabilities, combined with widespread adoption, create opportunities for malicious actors to gain...

EUVD-2019-0092

Malware in sbrugna...

Adversarial-Resilient RF Fingerprinting: A CNN-GAN Framework for Rogue Transmitter Detection

Radio Frequency Fingerprinting RFF has evolved as an effective solution for authenticating devices by leveraging the unique imperfections in hardware components involved in the signal generation process. In this work, we propose a Convolutional Neural Network CNN based framework for detecting rog...

EUVD-2023-0183

Malicious code in bioql PyPI...

EUVD-2025-30894

Malicious code in bioql PyPI...

EUVD-2022-30644

Malicious code in bioql PyPI...

EvoMail: Self-Evolving Cognitive Agents for Adaptive Spam and Phishing Email Defense

Modern email spam and phishing attacks have evolved far beyond keyword blacklists or simple heuristics. Adversaries now craft multi-modal campaigns that combine natural-language text with obfuscated URLs, forged headers, and malicious attachments, adapting their strategies within days to bypass...

Defending against Stegomalware in Deep Neural Networks with Permutation Symmetry

Deep neural networks are being utilized in a growing number of applications, both in production systems and for personal use. Network checkpoints are as a consequence often shared and distributed on various platforms to ease the development process. This work considers the threat of neural networ...

Collaborative P4-SDN DDoS Detection and Mitigation with Early-Exit Neural Networks

Distributed Denial of Service DDoS attacks pose a persistent threat to network security, requiring timely and scalable mitigation strategies. In this paper, we propose a novel collaborative architecture that integrates a P4-programmable data plane with an SDN control plane to enable real-time DDo...

Robust DDoS-Attack Classification with 3D CNNs against Adversarial Methods

Distributed Denial-of-Service DDoS attacks remain a serious threat to online infrastructure, often bypassing detection by altering traffic in subtle ways. We present a method using hive-plot sequences of network data and a 3D convolutional neural network 3D CNN to classify DDoS traffic with high...

Machine Learning-Based AES Key Recovery Via Side-Channel Analysis on the ASCAD Dataset

Cryptographic algorithms like AES and RSA are widely used and they are mathematically robust and almost unbreakable but its implementation on physical devices often leak information through side channels, such as electromagnetic EM emissions, potentially compromising said theoretically secure...

Developing a Transferable Federated Network Intrusion Detection System

Intrusion Detection Systems IDS are a vital part of a network-connected device. In this paper, we develop a deep learning based intrusion detection system that is deployed in a distributed setup across devices connected to a network. Our aim is to better equip deep learning models against unknown...

BadBlocks: Low-Cost and Stealthy Backdoor Attacks Tailored for Text-To-Image Diffusion Models

In recent years,Diffusion models have achieved remarkable progress in the field of image generation.However,recent studies have shown that diffusion models are susceptible to backdoor attacks,in which attackers can manipulate the output by injecting covert triggers such as specific visual pattern...