ShielDroid: A Hybrid Approach Integrating Machine and Deep Learning for Android Malware Detection

The rapid advancement of modern technology has led to a significant increase in the use of smart devices, such as smartphones and tablets, resulting in the widespread adoption of mobile applications. Although applications are required to undergo malware screening before being published on officia...

A Lightweight Hybrid MLP-Based Framework for Real-Time Phishing URL Detection Using Structural URL Features

Phishing attacks remain a major cybersecurity threat, exploiting deceptive URLs to steal sensitive user information. Traditional blacklist and rule-based detection approaches are reactive and often fail to identify newly emerging phishing URLs. This paper proposes a lightweight hybrid framework f...

Token-Level Generalization in LoRA Adapter Backdoors: Attack Characterization and Behavioral Detection

We show that LoRA adapters, the dominant distribution format for fine-tuned LLMs, can be reliably backdoored through training data poisoning while preserving baseline task performance. On a Qwen 2.5 1.5B prompt-injection classifier, a small fraction of poisoned examples drives a...

Amazon Linux 2023 : ImageMagick, ImageMagick-c++, ImageMagick-c++-devel (ALAS2023-2026-1478)

It is, therefore, affected by multiple vulnerabilities as referenced in the ALAS2023-2026-1478 advisory. ImageMagick is free and open-source software used for editing and manipulating digital images. Prior to versions 7.1.2-15 and 6.9.13-40, a heap information disclosure vulnerability exists in...

Toward a Multi-Layer ML-Based Security Framework for Industrial IoT

The Industrial Internet of Things IIoT introduces significant security challenges as resource-constrained devices become increasingly integrated into critical industrial processes. Existing security approaches typically address threats at a single network layer, often relying on expensive hardwar...

SUSE-SU-2026:0870-1 Security update for ImageMagick

This update for ImageMagick fixes the following issue: - CVE-2026-24484: denial of service vulnerability via multi-layer nested MVG to SVG conversion bsc1258790...

DKD-KAN: A Lightweight Knowledge-Distilled KAN Intrusion Detection Framework, Based on MLP and KAN

Cyber-security systems often operate in resource-constrained environments, such as edge environments and real-time monitoring systems, where model size and inference time are crucial. A light-weight intrusion detection framework is proposed that utilizes the Kolmogorov-Arnold Network KAN to captu...

OESA-2026-1453 ImageMagick security update

Use ImageMagick to create, edit, compose, or convert bitmap images. It can read and write images in a variety of formats over 200 including PNG, JPEG, GIF, HEIC, TIFF, DPX, EXR, WebP, Postscript, PDF, and SVG. Use ImageMagick to resize, flip, mirror, rotate, distort, shear and transform images,...

SUSE CVE-2026-24484

ImageMagick is free and open-source software used for editing and manipulating digital images. Prior to versions 7.1.2-15 and 6.9.13-40, Magick fails to check for multi-layer nested mvg conversions to svg, leading to DoS. Versions 7.1.2-15 and 6.9.13-40 contain a patch...

Allocation of Resources Without Limits or Throttling

Overview Magick.NET-Q16-OpenMP-arm64 is a Magick.NET allows you can use ImageMagick without having to install ImageMagick on your server or desktop. More information about specific builds see the official docs https://github.com/dlemstra/Magick.NET/tree/main/docs Affected versions of this package...

Smart Surveillance: Identifying IoT Device Behaviours Using ML-Powered Traffic Analysis

The proliferation of Internet of Things IoT devices has grown exponentially in recent years, introducing significant security challenges. Accurate identification of the types of IoT devices and their associated actions through network traffic analysis is essential to mitigate potential threats. B...

Antivirus Software Outage: Is Your Defense Ready?

Your antivirus software is the trusted gatekeeper of your digital world, silently working in the background to block threats. But what happens when that gatekeeper suddenly walks off the job? A widespread antivirus software outage recently showed us the answer, grinding critical industries to a...

EUVD-2004-0244

Malware in sbrugna...

EUVD-2017-3904

Malware in sbrugna...

A Novel Study on Intelligent Methods and Explainable AI for Dynamic Malware Analysis

Deep learning models are one of the security strategies, trained on extensive datasets, and play a critical role in detecting and responding to these threats by recognizing complex patterns in malicious code. However, the opaque nature of these models-often described as "black boxes"-makes their...

Enhancing GraphQL Security by Detecting Malicious Queries Using Large Language Models, Sentence Transformers, and Convolutional Neural Networks

GraphQL's flexibility, while beneficial for efficient data fetching, introduces unique security vulnerabilities that traditional API security mechanisms often fail to address. Malicious GraphQL queries can exploit the language's dynamic nature, leading to denial-of-service attacks, data...

LaSM: Layer-Wise Scaling Mechanism for Defending Pop-Up Attack on GUI Agents

Graphical user interface GUI agents built on multimodal large language models MLLMs have recently demonstrated strong decision-making abilities in screen-based interaction tasks. However, they remain highly vulnerable to pop-up-based environmental injection attacks, where malicious visual element...

EVA-S2PMLP: Secure and Scalable Two-Party MLP via Spatial Transformation

Whitepaper called EVA-S2PMLP: Secure and Scalable Two-Party MLP via Spatial Transformation...

Optimizing DDoS Detection in SDNs through Machine Learning Models

The emergence of Software-Defined Networking SDN has changed the network structure by separating the control plane from the data plane. However, this innovation has also increased susceptibility to DDoS attacks. Existing detection techniques are often ineffective due to data imbalance and accurac...

Performance of Machine Learning Classifiers for Anomaly Detection in Cyber Security Applications

This work empirically evaluates machine learning models on two imbalanced public datasets KDDCUP99 and Credit Card Fraud 2013. The method includes data preparation, model training, and evaluation, using an 80/20 train/test split. Models tested include eXtreme Gradient Boosting XGB, Multi Layer...