Intrusion Detection
100 papers with code • 4 benchmarks • 7 datasets
Intrusion Detection is the process of dynamically monitoring events occurring in a computer system or network, analyzing them for signs of possible incidents and often interdicting the unauthorized access. This is typically accomplished by automatically collecting information from a variety of systems and network sources, and then analyzing the information for possible security problems.
Benchmarks
These leaderboards are used to track progress in Intrusion Detection
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Libraries
Use these libraries to find Intrusion Detection models and implementationsDatasets
UNSW-NB15
20NewsGroups
IoT Network Intrusion Dataset
IoT ENVIRONMENT DATASET
Latest papers with no code
Privacy-Preserving Intrusion Detection using Convolutional Neural Networks
A common service provision involves the input data from the client and the model on the analyst's side.
An incremental hybrid adaptive network-based IDS in Software Defined Networks to detect stealth attacks
It can detect known and unknown attacks.
A Transformer-Based Framework for Payload Malware Detection and Classification
Techniques such as Deep Packet Inspection (DPI) have been introduced to allow IDSs analyze the content of network packets, providing more context for identifying potential threats.
Dealing with Imbalanced Classes in Bot-IoT Dataset
To evaluate the robustness of the NIDS in the IoT network, the existing work proposed a realistic botnet dataset in the IoT network (Bot-IoT dataset) and applied it to machine learning-based anomaly detection.
Expectations Versus Reality: Evaluating Intrusion Detection Systems in Practice
Our paper provides empirical comparisons between recent IDSs to provide an objective comparison between them to help users choose the most appropriate solution based on their requirements.
EG-ConMix: An Intrusion Detection Method based on Graph Contrastive Learning
As the number of IoT devices increases, security concerns become more prominent.
Multiple-Input Auto-Encoder Guided Feature Selection for IoT Intrusion Detection Systems
The MIAE model is trained in an unsupervised learning mode to transform the heterogeneous inputs into lower-dimensional representation, which helps classifiers distinguish between normal behaviour and different types of attacks.
Problem space structural adversarial attacks for Network Intrusion Detection Systems based on Graph Neural Networks
Machine Learning (ML) algorithms have become increasingly popular for supporting Network Intrusion Detection Systems (NIDS).
usfAD Based Effective Unknown Attack Detection Focused IDS Framework
To address this challenge, we put forth two strategies for semi-supervised learning based IDS where training samples of attacks are not required: 1) training a supervised machine learning model using randomly and uniformly dispersed synthetic attack samples; 2) building a One Class Classification (OCC) model that is trained exclusively on benign network traffic.
Hierarchical Classification for Intrusion Detection System: Effective Design and Empirical Analysis
With the increased use of network technologies like Internet of Things (IoT) in many real-world applications, new types of cyberattacks have been emerging.
