Intrusion Detection Systems

This collection encompasses research and advancements in intrusion detection systems (IDS) focusing on a variety of environments, including networks and the Internet of Things (IoT). The topics range from innovative machine learning techniques, feature selection algorithms, and hybrid models to address challenges like false positives and detection accuracy. The studies highlight the integration of novel approaches, such as ensemble learning and the use of genetic algorithms, to enhance performance in identifying and mitigating cyber threats effectively.

Intrusion Detection System: Spot Hackers Before 2026 | CyberPro Magazine
CYBER ATTACKS ON INTRUSION DETECTION SYSTEM
Anomaly Detection in Network Traffic using Selected Statistical and Entropy-Based Features
Federated deep learning intrusion detection system on software defined-network based internet of things
Enhanced intrusion detection through dual reduction and robust mean
Enhancing intrusion detection in next-generation networks based on a multi-agent game-theoretic framework
Network intrusion detection in big datasets using Spark environment and incremental learning
Network intrusion detection in big datasets using Spark environment and incremental learning
Hybrid software defined network-based deep learning framework for enhancing internet of medical things cybersecurity
Anomaly based intrusion detection using ensemble machine learning and block-chain
IMPROVING INTRUSION DETECTION SYSTEM USING THE COMBINATION OF NEURAL NETWORK AND GENETIC ALGORITHM
A novel framework for analyzing internet of things datasets for machine learning and deep learning-based intrusion detection systems
Progress of Machine Learning in the Field of Intrusion Detection Systems
The Role of Intrusion Detection Systems in Network Security.pdf
The Role of Intrusion Detection Systems in Network Security.pdf
malicious logic, virus, worms, trojan horse, denial of service, intrusion, intrusion detection system and zombies
Extending Network Intrusion Detection with Enhanced Particle Swarm Optimization Techniques
ENHANCE THE DETECTION OF DOS AND BRUTE FORCE ATTACKS WITHIN THE MQTT ENVIRONMENT THROUGH FEATURE ENGINEERING AND EMPLOYING AN ENSEMBLE TECHNIQUE
 
Enhance the Detection of DoS and Brute Force Attacks within the MQTT Environment Through Feature Engineering and Employing an Ensemble Technique
PROGRESS OF MACHINE LEARNING IN THE FIELD OF INTRUSION DETECTION SYSTEMS