Jev-IDS: System One Models for Network Intrusion Detection

Machine-learning Network Intrusion Detection Systems IDS depend on substantial labeled datasets and task-specific training, whereas Large Language Models LLMs detection can analyze flow records directly but incurs higher inference cost and latency, with less constrained outputs. This paper presen...

How It's Made: Uncovering Detection Engineering Processes for Network Intrusion Detection Rules

Many Security Operations Centers rely on signature-based Network Intrusion Detection Systems like Suricata, yet detection rule engineering remains understudied. We investigate this process by introducing SuriCap, a platform for rule engineering exercises, and hosting CTF-style workshops where 60...

Linux Distros Unpatched Vulnerability : CVE-2026-45768

The Linux/Unix host has one or more packages installed that are impacted by a vulnerability without a vendor supplied patch available. - Suricata is a network Intrusion Detection System, Intrusion Prevention System and Network Security Monitoring engine. Starting in version 8.0.0 and prior to...

Few-Shot Learning for Network Intrusion Detection: Methods, Datasets, and Performance

Anomaly-based network intrusion detection systems NIDS are an important first line of defense. However, training NIDS for new attack types is challenging, because labeled attack data are rarely available. Few-shot learning FSL addresses this problem by learning from few samples. However, the...

Navigating the Latent Manifold: Proactive Concept Drift Adaptation for Resilient NIDS

Network intrusion detection systems NIDS are critical for cybersecurity, safeguarding services and data from potential attacks. However, existing AI-based NIDS often assume static data distributions and fail to handle concept drift, leading to degraded performance and increased false positives in...

Candidate Comparability Before Promotion: Conditional Validation in Adaptive Network Intrusion Detection

Adaptive network intrusion detection systems retrain classifiers after drift alarms, but an alarm detects change; it does not establish that a challenger should replace the deployed incumbent. Promotion is security-relevant because it changes the model responsible for subsequent attack detection,...

Evaluating ML-Based Intrusion Detection Systems: The Illusion of Model Efficacy

Intrusion Detection has been revolutionized due to the integration of Machine Learning. Improved detection rates, reduced false alarms, and optimized algorithms contribute to the perception of improved systems with optimal accuracy and near-perfect performance, the illusion of model efficacy...

Defending Network Intrusion Detection Systems Based on Graph Neural Networks against Structural Adversarial Attacks

Graph Neural Networks GNNs represent a promising solution for Machine Learning ML based Network Intrusion Detection Systems NIDS, thanks to their ability to leverage both network flow features and topological patterns. While GNN classifiers demonstrate superior robustness against feature-based...

Slips: Behavioral Evidence Aggregation for Network Security

Network intrusion detection systems often analyze individual packets or flows, although malicious behavior may develop across many connections and over time. This may limit their ability to combine isolated detections into a coherent assessment of host behavior. Packet-level features may also be...

[SECURITY] Fedora 44 Update: suricata-8.0.6-1.fc44

The Suricata Engine is an Open Source Next Generation Intrusion Detection and Prevention Engine. This engine is not intended to just replace or emulate the existing tools in the industry, but will bring new ideas and technologies to the field. This new Engine supports Multi-threading, Automatic...

Fedora 43 : suricata (2026-d2a3477900)

The remote Fedora 43 host has a package installed that is affected by a vulnerability as referenced in the FEDORA-2026-d2a3477900 advisory. Upstream security/bugfix release Tenable has extracted the preceding description block directly from the Fedora security advisory. Note that Nessus has not...

Cybersecurity and the Gap Between Skill and Ability

Last week, national security agencies from the Five Eyes--that's the rich, English-language-speaking countries club--jointly released a statement warning of the increasing cyber risks of AI models: in particular, their ability to autonomously hack into systems and networks. The statement was more...

Beyond the Syntax: Do Security Experts Trust LLMs for NIDS Rule Engineering?

As network threats evolve, manual NIDS rule engineering has become a critical operational bottleneck. While Large Language Models LLMs show promise for automating this process, their ability to produce production-ready rules remains unvalidated. This paper presents a human-centered investigation...

A Hybrid Intrusion Detection System for Electric Vehicle Charging Infrastructure

The integration of Electric Vehicle Charging Stations EVCSs into the smart grid necessitates sophisticated digital infrastructure for their management and coordination, which expands the attack surface and makes both the power grid and EVCSs vulnerable to cyberattacks. This research addresses...

Categorical Robustness Assessment for Machine Learning Based Network Intrusion Detection Systems

Network Intrusion Detection Systems NIDS heavily utlize Machine Learning ML but ML models can be manipulated via adversarial attacks. These attacks add carefully crafted perturbations to network traffic data that leads to misclassifications. While prior work has demonstrated adversarial...

NCMD: Benign-Anchored Feature Selection for Imbalanced Network Intrusion Detection

Feature selection is critical for network intrusion detection systems NIDS operating under high-dimensional, highly imbalanced traffic, as found in operational and defense networks. Traditional filter methods rank features using global statistics computed symmetrically across classes and thus fai...

A Protocol-Language Model for Network Intrusion (Without Deep Packet Inspection)

Modern network intrusion detection systems NIDS are caught in a structural contradiction: the protocols carrying the highest threat intelligence are precisely those encrypted under TLS 1.3 and QUIC, where payload inspection yields nothing. We ask a simpler question -- what if the attack signature...

"What Is the Problem Space?" Defining Host-Space Adversarial Perturbations against Network Intrusion Detection Systems

Network Intrusion Detection Systems NIDS are now increasingly leveraging Machine Learning ML techniques to detect malicious network activities. Numerous papers have scrutinized the security of ML-based NIDS ML-NIDS by testing them against various attacks involving adversarial perturbations. The...

A No-Defense Defense against Gradient-Based Adversarial Attacks on ML-NIDS: Is Less More?

Gradient-based adversarial attacks subtly manipulate inputs of Machine Learning ML models to induce incorrect predictions. This paper investigates whether careful architectural choices alone can yield an inherently robust Deep Neural Network DNN-based Network Intrusion Detection Systems NIDS,...

Federated Naive Bayes with Real Mixture of Gaussians and Institutional Governance Regularization for Network Intrusion Detection

Federated learning for intrusion detection rests on a flawed premise: that every participating institution contributes equally to the shared model. In practice, a financial institution with mature security controls and low vulnerability exposure produces fundamentally different data than a...