Intrusion Detection System

Intrusion Detection Systems (IDS) aim to identify malicious activities within computer networks, primarily by analyzing network traffic patterns to distinguish between normal and anomalous behavior. Current research emphasizes improving IDS accuracy and reliability through advanced machine learning techniques, including deep learning models like convolutional neural networks (CNNs), recurrent neural networks (RNNs, such as LSTMs), and graph neural networks (GNNs), as well as ensemble methods and federated learning for enhanced privacy and scalability. The development of more robust, explainable, and efficient IDSs is crucial for securing increasingly complex and interconnected systems, impacting both cybersecurity practices and the broader field of machine learning.

Papers

May 23, 2022

FedSA: Accelerating Intrusion Detection in Collaborative Environments with Federated Simulated Annealing
Helio N. Cunha Neto, Ivana Dusparic, Diogo M. F. Mattos, Natalia C. Fernandes
Intrusion Detection Intrusion Detection System Attack Detection Collaborative Environment Federated Automatic Differentiation

May 3, 2022

CANShield: Deep Learning-Based Intrusion Detection Framework for Controller Area Networks at the Signal-Level
Md Hasan Shahriar, Yang Xiao, Pablo Moriano, Wenjing Lou, Y. Thomas Hou
Intrusion Detection System Attack Detection Learning Based Intrusion Detection Controller Area Network

April 26, 2022

A review of Federated Learning in Intrusion Detection Systems for IoT
Aitor Belenguer, Javier Navaridas, Jose A. Pascual
Narrative Review Intrusion Detection Detection Model Intrusion Detection System IOT Technology Federated Learning Approach

April 23, 2022

STC-IDS: Spatial-Temporal Correlation Feature Analyzing based Intrusion Detection System for Intelligent Connected Vehicles
Pengzhou Cheng, Mu Han, Aoxue Li, Fengwei Zhang
Intrusion Detection System Temporal Correlation Intelligent Vehicle Space Time Attention

April 1, 2022

Effect of Balancing Data Using Synthetic Data on the Performance of Machine Learning Classifiers for Intrusion Detection in Computer Networks
Ayesha S. Dina, A. B. Siddique, D. Manivannan
Machine Learning Synthetic Data Intrusion Detection Intrusion Detection System Imbalanced Data Machine Learning Classifier Computer Network Data Balancing NSL KDD

February 19, 2022

Survey of Machine Learning Based Intrusion Detection Methods for Internet of Medical Things
Ayoub Si-Ahmed, Mohammed Ali Al-Garadi, Narhimene Boustia
Timely Survey Intrusion Detection System Internet of Medical Thing

February 16, 2022

An Intrusion Response System utilizing Deep Q-Networks and System Partitions
Valeria Cardellini, Emiliano Casalicchio, Stefano Iannucci, Matteo Lucantonio, Sudip Mittal, Damodar Panigrahi, Andrea Silvi
Reinforcement Learning Deep Q Network Intrusion Detection System Magic Cube Partition

January 31, 2022

Unsupervised Network Intrusion Detection System for AVTP in Automotive Ethernet Networks
Natasha Alkhatib, Maria Mushtaq, Hadi Ghauch, Jean-Luc Danger
Anomaly Detection Network Intrusion Detection Intrusion Detection System

January 27, 2022

December 3, 2021

Two-stage Deep Stacked Autoencoder with Shallow Learning for Network Intrusion Detection System
Nasreen Fathima, Akshara Pramod, Yash Srivastava, Anusha Maria Thomas, Syed Ibrahim S P, Chandran K R
Deep Learning Intrusion Detection System Network Intrusion Detection Network Traffic Shallow Learning Sparse Event

November 19, 2021

A Hybrid Approach for an Interpretable and Explainable Intrusion Detection System
Tiago Dias, Nuno Oliveira, Norberto Sousa, Isabel Praça, Orlando Sousa
Decision Tree Intrusion Detection System Cybersecurity Perspective Hybrid Approach

Intrusion Detection System

Papers

FedSA: Accelerating Intrusion Detection in Collaborative Environments with Federated Simulated Annealing

CANShield: Deep Learning-Based Intrusion Detection Framework for Controller Area Networks at the Signal-Level

A review of Federated Learning in Intrusion Detection Systems for IoT

STC-IDS: Spatial-Temporal Correlation Feature Analyzing based Intrusion Detection System for Intelligent Connected Vehicles

Effect of Balancing Data Using Synthetic Data on the Performance of Machine Learning Classifiers for Intrusion Detection in Computer Networks

Survey of Machine Learning Based Intrusion Detection Methods for Internet of Medical Things

An Intrusion Response System utilizing Deep Q-Networks and System Partitions

Unsupervised Network Intrusion Detection System for AVTP in Automotive Ethernet Networks

A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of Vehicles

Early Detection of Network Attacks Using Deep Learning

Two-stage Deep Stacked Autoencoder with Shallow Learning for Network Intrusion Detection System

A Hybrid Approach for an Interpretable and Explainable Intrusion Detection System