5107 matches found
Empowering IoT Security: On-Device Intrusion Detection in Resource Constrained Devices
IoT devices particularly microcontrollers are challenged by their inherent limitations in processing capabilities, memory capacity, and energy conservation. Securing communication within IoT networks is further complicated by the heterogeneity of devices and the myriad of potential security...
Enhancing Adversarial Robustness in Network Intrusion Detection: A Layer-Wise Adaptive Regularization Approach
The new wave of adversarial attacks that utilize gradient-related vulnerabilities in neural network-based classifiers makes Network Intrusion Detection Systems more open to such threats. Although state-of-the-art adversarial training methods have shown promising results in producing more robust...
A Novel Byte-Level Flow-To-Image Encoding Method for Network Intrusion Detection Systems
Network-based Intrusion Detection Systems IDS are predominantly trained on tabular flow records, whose one-dimensional representations limit convolutional architectures from exploiting inter-feature spatial correlations. This paper presents a novel byte-level flow-to-image encoding method that...
AoI-Guided Client Selection for Robust and Timely Federated Intrusion Detection in Cloud-Edge Security Analytics
Federated learning FL is attractive for cloud-edge intrusion detection because it enables collaborative training over distributed telemetry without centralizing raw logs. In production security analytics pipelines, however, only a subset of clients participates in each round, and heterogeneous...
LiteShield: Hybrid Feature Selection-Driven Lightweight Intrusion Detection for Resource-Constrained IoT Networks
The rapid expansion of Internet of Things IoT deployments has enlarged the attack surface of modern digital infrastructure while exposing a key security mismatch: many intrusion detection systems IDSs remain too computationally expensive for constrained IoT environments. This paper presents...
Zero Day Attacks: Novel Behaviour or Novel Vulnerability?
Zero-day attacks pose severe cybersecurity risks due to their high success rates and stealth. Because signature-based approaches struggle to detect such attacks, building Intrusion Detection Systems IDSs for detecting zero-day attacks is essential. We contend that for an IDS to be effective it mu...
Evaluating Tabular Representation Learning for Network Intrusion Detection
Classic Network Intrusion Detection Systems NIDS often rely on manual feature engineering to extract meaningful patterns from network traffic data. However, this approach requires domain expertise and runs counter to the widely adopted principle of modern machine learning and neural networks: tha...
FIRCE: A Framework for Intrusion Response and Conformal Evaluation
Machine learning-based intrusion detection systems deployed in real-world environments frequently suffer from model degradation due to concept drift, where changes in traffic patterns invalidate training assumptions. To address this, we present FIRCE, a Framework for Intrusion Response and...
A Comparative Analysis of Machine Learning Models for Intrusion Detection in Intelligent Transport Systems
AI-powered edge computing security is moving Intelligent Transportation Systems ITS from passive, rule-based protections to proactive, smart, zero-touch, self-sufficient safeguards that neutralize threats in milliseconds. As transportation becomes more connected with edge computing, massive IoT,...
Large Language Models As Explainable Cyberattack Detectors for Energy Industrial Control Systems
In modern energy systems, industrial control systems ICS and power-system SCADA require intrusion detection that is not only accurate but also auditable by operators. The ICS intrusion-detection landscape is currently dominated by established supervised detectors. In this paper, we study whether ...
A-THENA: Early Intrusion Detection for IoT with Time-Aware Hybrid Encoding and Network-Specific Augmentation
The proliferation of Internet of Things IoT devices has significantly expanded attack surfaces, making IoT ecosystems particularly susceptible to sophisticated cyber threats. To address this challenge, this work introduces A-THENA, a lightweight early intrusion detection system EIDS that...
On the Challenges of Holistic Intrusion Detection in ICS
Past attacks against industrial control systems ICS show that adversaries often target both the ICS network and the physical process to achieve potential catastrophic impact. To secure ICS, intrusion detection systems promise timely uncovering of such adversaries. However, as these detection...
SDNGuardStack: An Explainable Ensemble Learning Framework for High-Accuracy Intrusion Detection in Software-Defined Networks
Software-Defined Networking SDN is another technology that has been developing in the last few years as a relevant technique to improve network programmability and administration. Nonetheless, its centralized design presents a major security issue, which requires effective intrusion detection...
ExAI5G: A Logic-Based Explainable AI Framework for Intrusion Detection in 5G Networks
Intrusion detection systems IDSs for 5G networks must handle complex, high-volume traffic. Although opaque "black-box" models can achieve high accuracy, their lack of transparency hinders trust and effective operational response. We propose ExAI5G, a framework that prioritizes interpretability by...
SoK: Reshaping Research on Network Intrusion Detection Systems
Network Intrusion Detection Systems NIDS have been studied for decades. Hundreds of papers have, e.g., proposed ways to enhance, harden or bypass NIDS. However, the findings of prior literature are hardly reflected in real-world operational contexts. Such a disconnection is problematic for resear...
n-days-poc-benchmark-and-dataset
ICS N-Day Vulnerability PoC Benchmark Suite A structured coll...
MLDAS: Machine Learning Dynamic Algorithm Selection for Software-Defined Networking Security
Network security is a critical concern in the digital landscape of today, with users demanding secure browsing experiences and protection of their personal data. This study explores the dynamic integration of Machine Learning ML algorithms with Software-Defined Networking SDN controllers to enhan...
Robustness Analysis of Machine Learning Models for IoT Intrusion Detection under Data Poisoning Attacks
Ensuring the reliability of machine learning-based intrusion detection systems remains a critical challenge in Internet of Things IoT environments, particularly as data poisoning attacks increasingly threaten the integrity of model training pipelines. This study evaluates the susceptibility of fo...