CVE-2025-54409 AIDE null pointer dereference when reading incorrectly encoded xattr attributes from database (local DoS)

AIDE is an advanced intrusion detection environment. From versions 0.13 to 0.19.1, there is a null pointer dereference vulnerability in AIDE. An attacker can crash the program during report printing or database listing after setting extended file attributes with an empty attribute value or with a...

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,...

Ashlar-Vellum Cobalt, Xenon, Argon, Lithium, Cobalt Share

RISK EVALUATION Successful exploitation of these vulnerabilities could allow an attacker to disclose information and execute arbitrary code. 2. RECOMMENDED PRACTICES CISA recommends users take defensive measures to minimize the risk of exploitation of these vulnerabilities, such as: Minimize...

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...

FetFIDS: a Feature Embedding Attention Based Federated Network Intrusion Detection Algorithm

Intrusion Detection Systems IDS have an increasingly important role in preventing exploitation of network vulnerabilities by malicious actors. Recent deep learning based developments have resulted in significant improvements in the performance of IDS systems. In this paper, we present FetFIDS,...

EG4 Electronics EG4 Inverters (Update B)

RISK EVALUATION Successful exploitation of these vulnerabilities could allow an attacker to intercept and manipulate critical data, install malicious firmware, hijack device access, and gain unauthorized control over the system. 2. RECOMMENDED PRACTICES CISA recommends users take defensive...

Optimizing IoT Threat Detection with Kolmogorov-Arnold Networks (KANs)

The exponential growth of the Internet of Things IoT has led to the emergence of substantial security concerns, with IoT networks becoming the primary target for cyberattacks. This study examines the potential of Kolmogorov-Arnold Networks KANs as an alternative to conventional machine learning...

Leveraging Large Language Models for SQL Behavior-Based Database Intrusion Detection

Database systems are extensively used to store critical data across various domains. However, the frequency of abnormal database access behaviors, such as database intrusion by internal and external attacks, continues to rise. Internal masqueraders often have greater organizational knowledge,...

Moderate: Red Hat Security Advisory: mod_security security update

An update for modsecurity is now available for Red Hat Enterprise Linux 9. Red Hat Product Security has rated this update as having a security impact of Moderate. A Common Vulnerability Scoring System CVSS base score, which gives a detailed severity rating, is available for each vulnerability fro...

Intrusion Detection in Heterogeneous Networks with Domain-Adaptive Multi-Modal Learning

Network Intrusion Detection Systems NIDS play a crucial role in safeguarding network infrastructure against cyberattacks. As the prevalence and sophistication of these attacks increase, machine learning and deep neural network approaches have emerged as effective tools for enhancing NIDS...

RLSA-2025:8844 Important: mod_security security update

ModSecurity is an open source intrusion detection and prevention engine for web applications. Security Fixes: modsecurity: ModSecurity Has Possible DoS Vulnerability CVE-2025-47947 For more details about the security issues, including the impact, a CVSS score, acknowledgments, and other related...

Enhancing IoT Intrusion Detection Systems through Adversarial Training

The augmentation of Internet of Things IoT devices transformed both automation and connectivity but revealed major security vulnerabilities in networks. We address these challenges by designing a robust intrusion detection system IDS to detect complex attacks by learning patterns from the...

The vulnerability of the Suricata intrusion detection and prevention system, due to the unlimited distribution of resources, allows an intruder to trigger a service failure.

The vulnerability of the Suricata intrusion detection and prevention system is related to the unlimited distribution of resources. Exploiting this vulnerability could allow a malicious actor, operating remotely, to cause service failures...

CANDoSA: a Hardware Performance Counter-Based Intrusion Detection System for DoS Attacks on Automotive CAN Bus

The Controller Area Network CAN protocol, essential for automotive embedded systems, lacks inherent security features, making it vulnerable to cyber threats, especially with the rise of autonomous vehicles. Traditional security measures offer limited protection, such as payload encryption and...

How to Mitigate and Defend against DDoS Attacks in IoT Devices

Distributed Denial of Service DDoS attacks have become increasingly prevalent and dangerous in the context of Internet of Things IoT networks, primarily due to the low-security configurations of many connected devices. This paper analyzes the nature and impact of DDoS attacks such as those launch...

REAL-IoT: Characterizing GNN Intrusion Detection Robustness under Practical Adversarial Attack

Graph Neural Network GNN-based network intrusion detection systems NIDS are often evaluated on single datasets, limiting their ability to generalize under distribution drift. Furthermore, their adversarial robustness is typically assessed using synthetic perturbations that lack realism. This...

Contrastive-KAN: a Semi-Supervised Intrusion Detection Framework for Cybersecurity with Scarce Labeled Data

In the era of the Fourth Industrial Revolution, cybersecurity and intrusion detection systems are vital for the secure and reliable operation of IoT and IIoT environments. A key challenge in this domain is the scarcity of labeled cyber-attack data, as most industrial systems operate under normal...

Spectral Feature Extraction for Robust Network Intrusion Detection Using MFCCs

The rapid expansion of Internet of Things IoT networks has led to a surge in security vulnerabilities, emphasizing the critical need for robust anomaly detection and classification techniques. In this work, we propose a novel approach for identifying anomalies in IoT network traffic by leveraging...

Hybrid LLM-Enhanced Intrusion Detection for Zero-Day Threats in IoT Networks

This paper presents a novel approach to intrusion detection by integrating traditional signature-based methods with the contextual understanding capabilities of the GPT-2 Large Language Model LLM. As cyber threats become increasingly sophisticated, particularly in distributed, heterogeneous, and...

PROTEAN: Federated Intrusion Detection in Non-IID Environments through Prototype-Based Knowledge Sharing

In distributed networks, participants often face diverse and fast-evolving cyberattacks. This makes techniques based on Federated Learning FL a promising mitigation strategy. By only exchanging model updates, FL participants can collaboratively build detection models without revealing sensitive...