DEBIAN-CVE-2025-64331

Suricata is a network IDS, IPS and NSM engine developed by the OISF Open Information Security Foundation and the Suricata community. Prior to versions 7.0.13 and 8.0.2, a stack overflow can occur on large HTTP file transfers if the user has increased the HTTP response body limit and enabled the...

A Novel and Practical Universal Adversarial Perturbations against Deep Reinforcement Learning Based Intrusion Detection Systems

Intrusion Detection Systems IDS play a vital role in defending modern cyber physical systems against increasingly sophisticated cyber threats. Deep Reinforcement Learning-based IDS, have shown promise due to their adaptive and generalization capabilities. However, recent studies reveal their...

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...

Adaptive Intrusion Detection for Evolving RPL IoT Attacks Using Incremental Learning

The routing protocol for low-power and lossy networks RPL has become the de facto routing standard for resource-constrained IoT systems, but its lightweight design exposes critical vulnerabilities to a wide range of routing-layer attacks such as hello flood, decreased rank, and version number...

GraphFaaS: Serverless GNN Inference for Burst-Resilient, Real-Time Intrusion Detection

Provenance-based intrusion detection is an increasingly popular application of graphical machine learning in cybersecurity, where system activities are modeled as provenance graphs to capture causality and correlations among potentially malicious actions. Graph Neural Networks GNNs have...

Toward an Intrusion Detection System for a Virtualization Framework in Edge Computing

Edge computing pushes computation closer to data sources, but it also expands the attack surface on resource-constrained devices. This work explores the deployment of the Lightweight Deep Anomaly Detection for Network Traffic LDPI integrated as an isolated service within a virtualization framewor...

An Explainable Recursive Feature Elimination to Detect Advanced Persistent Threats Using Random Forest Classifier

Intrusion Detection Systems IDS play a vital role in modern cybersecurity frameworks by providing a primary defense mechanism against sophisticated threat actors. In this paper, we propose an explainable intrusion detection framework that integrates Recursive Feature Elimination RFE with Random...

EulerOS 2.0 SP10 : aide (EulerOS-SA-2025-2405)

According to the versions of the aide package installed, the EulerOS installation on the remote host is affected by the following vulnerabilities : AIDE is an advanced intrusion detection environment. Prior to version 0.19.2, there is an improper output neutralization vulnerability in AIDE. An...

Toward Autonomous and Efficient Cybersecurity: A Multi-Objective AutoML-Based Intrusion Detection System

With increasingly sophisticated cybersecurity threats and rising demand for network automation, autonomous cybersecurity mechanisms are becoming critical for securing modern networks. The rapid expansion of Internet of Things IoT systems amplifies these challenges, as resource-constrained IoT...

Binary and Multiclass Cyberattack Classification on GeNIS Dataset

The integration of Artificial Intelligence AI in Network Intrusion Detection Systems NIDS is a promising approach to tackle the increasing sophistication of cyberattacks. However, since Machine Learning ML and Deep Learning DL models rely heavily on the quality of their training data, the lack of...

Enhancing Adversarial Robustness of IoT Intrusion Detection Via SHAP-Based Attribution Fingerprinting

The rapid proliferation of Internet of Things IoT devices has transformed numerous industries by enabling seamless connectivity and data-driven automation. However, this expansion has also exposed IoT networks to increasingly sophisticated security threats, including adversarial attacks targeting...

Large Language Models for Cyber Security

This paper studies the integration off Large Language Models into cybersecurity tools and protocols. The main issue discussed in this paper is how traditional rule-based and signature based security systems are not enough to deal with modern AI powered cyber threats. Cybersecurity industry is...

Temporal Analysis Framework for Intrusion Detection Systems: A Novel Taxonomy for Time-Aware Cybersecurity

Most intrusion detection systems still identify attacks only after significant damage has occurred, detecting late-stage tactics rather than early indicators of compromise. This paper introduces a temporal analysis framework and taxonomy for time-aware network intrusion detection. Through a...

Towards Ultra-Low Latency: Binarized Neural Network Architectures for In-Vehicle Network Intrusion Detection

The Control Area Network CAN protocol is essential for in-vehicle communication, facilitating high-speed data exchange among Electronic Control Units ECUs. However, its inherent design lacks robust security features, rendering vehicles susceptible to cyberattacks. While recent research has...

Attention Augmented GNN RNN-Attention Models for Advanced Cybersecurity Intrusion Detection

In this paper, we propose a novel hybrid deep learning architecture that synergistically combines Graph Neural Networks GNNs, Recurrent Neural Networks RNNs, and multi-head attention mechanisms to significantly enhance cybersecurity intrusion detection capabilities. By leveraging the comprehensiv...

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...

cybersec-ids

cybersec-ids Full-stack AI-driven Web App Intrusion Detection...

Actionable Cybersecurity Notifications for Smart Homes: A User Study on the Role of Length and Complexity

The proliferation of smart home devices has increased convenience but also introduced cybersecurity risks for everyday users, as many devices lack robust security features. Intrusion Detection Systems are a prominent approach to detecting cybersecurity threats. However, their alerts often use...

Exploring the Effect of DNN Depth on Adversarial Attacks in Network Intrusion Detection Systems

Adversarial attacks pose significant challenges to Machine Learning ML systems and especially Deep Neural Networks DNNs by subtly manipulating inputs to induce incorrect predictions. This paper investigates whether increasing the layer depth of deep neural networks affects their robustness agains...

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...