User Profile Builder < 3.11.8 - File Upload

The User Profile Builder WordPress plugin before 3.11.8 does not have proper authorisation, allowing unauthenticated users to upload media files via the async upload functionality of WP. id: CVE-2024-6366 info: name: User Profile Builder 3.11.8 - File Upload author: s4e-io severity: high...

network-intrusion-detector

network-intrusion-detector A Python tool that analyses web se...

vehicle-subsystem-security-assessment

🚗 End-to-end security assessment of vehicle subsystems ! Me...

Semantic Multi-Agent Intrusion Detection for IoT:Zero-Day and Adversarial Threats with Risk-Aware Reasoning

The rapid proliferation of Internet of Things IoT devices has enabled unprecedented automation and connectivity, but it has also substantially increased the attack surface, exposing networks to sophisticated cyber threats, including zero-day and adversarial intrusions. Traditional Intrusion...

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...

Rethinking IoT Intrusion Detection: Augmenting Routing Metrics with Radio Features

Machine learning-based intrusion detection systems IDS for RPL-based IoT networks often rely solely on routing layer features, which provide only a partial view of network behaviour. In this work, we investigate whether incorporating Transmit TX and Receive RX radio features alongside the standar...

An Improved CNN-LSTM Based Intrusion Detection System for IoT Networks

With the rapid proliferation of IoT devices, security concerns have dramatically escalated and intrusion detection systems have become critical for protecting networked environments. This paper presents an improved CNN-LSTM based intrusion detection model that combines multi-class classification,...

Explainable AI-Driven Cyber Risk Analytics and Model Reliability Assessment for Intelligent Governance of U.S. Critical Infrastructure: An XGBoost and SHAP-Based Intrusion Detection Framework

The increasing penetrations of the critical infrastructure sector in the United States with intelligent digital technologies have greatly increased exposure to advanced cyber adversaries and operational vulnerabilities. AI-powered governance and automated decision-making systems are becoming a ke...

GenTI: Benchmarking LLMs for Autonomous IDPS Rule Generation for Unseen Attacks

Rule-based Intrusion Detection and Prevention Systems IDPS offer precise attack detection as well as mitigation, however their manually crafted, signature-driven rules limit adaptability to emerging and zero-day threats. Additionally, existing public datasets e.g., CICIDS2017, UNSW-NB15 focus on...

Towards Intrusion Detection Systems for RPL-Based IoT Networks Using Foundation Models

AI-based intrusion detection systems IDS have shown promise in detecting attacks on IoT systems. In this work, we explore the use of foundation models to detect and identify attacks, with a specific focus on RPL-based IoT networks. We study multiple attack types, attack variations, and network...

FlowGuard: Flow Matching for Identity-Independent Detection of Data-Free Model Stealing Attacks on Energy System Intrusion Detection Systems

Artificial Intelligence AI-based Intrusion Detection Systems IDS deployed in energy infrastructure are vulnerable to model theft attacks, which allow adversaries to create evasive traffic offline. Current defences against model extraction rely either on identity-bound query monitoring, which is...

On the Evaluation of Spiking Neural Network Configurations for Network Intrusion Detection

Network intrusion detection is a core component of modern cybersecurity infrastructure, yet the deep learning models that dominate the field are computationally demanding, motivating interest in lightweight alternatives suited to edge and neuromorphic deployment. Spiking Neural Networks SNNs are...

Improving IoT Intrusion Detection through SMOTE-Based Oversampling and Extended Multi-Model Evaluation on Side-Channel Power Data

The detection of intrusions in IoT-based networks poses challenges that cannot be overcome using traditional machine learning methods. Perhaps the biggest of them is related to the presence of a class imbalance in the side-channel dataset, where the number of samples in the normal class compared ...

Web-Based-Honeypot-for-Intrusion-Detection

Web-Based-Honeypot-for-Intrusion-Detection A Web-Based Honeypo...

Meta-Quantum Ensemble Framework for Robust Network Intrusion Detection

Intrusion Detection Systems IDSs must maintain high detection sensitivity while operating under strict false-positive constraints, a challenge intensified by class imbalance and heterogeneous IoT traffic. This work investigates whether heterogeneous quantum learners can provide useful and...

"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...

CALIBURN: A Regime-Sensitivity Study of Operationally Calibrated Streaming Intrusion Detection

Streaming network intrusion detection systems must process flows continuously while keeping memory bounded, but most current methods leave alerting threshold selection as a post-hoc tuning problem poorly suited to production. Operators need alerting behaviour specifiable before deployment using...

Cybersecurity of Electric Vehicle Charging Infrastructure: Recent Advances, Open Challenges, and Future Directions

Electric Vehicles EVs have emerged as significant disruptors in the transportation sector over the past decade. Their growing popularity and adoption are accompanied by capital expenditures to deploy charging infrastructure. EV charging infrastructure sits at the intersection of the power grid, t...

FALCON-C: Flow-Based Analysis and Labeling for Connected Vehicular Network Cybersecurity

Along with the recent rise in popularity of Electric Vehicles EVs, Electric Vehicle Supply Equipment EVSE has emerged as a new target for cyber attacks. Therefore, ensuring the security and integrity of network communication between EVSE components and vehicular clients is a significant challenge...

Stabilising Explainability Fragility in Cybersecurity AI: The Impact and Mitigation of Multicollinearity in Public Benchmark Datasets

This paper investigates a unexplored yet impactful vulnerability in AI explainability used in intrusion detection IDS: multicollinearity-induced instability. Despite extensive reliance on post-hoc explainability tools such as SHAP or LIME, the impact of correlated features on explanation robustne...