Dimensionality reduction

Pure mathematics

Layer (electronics)

Dimension (graph theory)

Anomaly detection

Data mining

Computer science

Anomaly (physics)

Computer network

Physics

Organic chemistry

Pattern recognition (psychology)

Mathematics

Condensed matter physics

Artificial intelligence

Geometry

Intrusion detection system

Chemistry

Reduction (mathematics)

IEEE Transactions on Emerging Topics in Computing

With increasing reliance on Internet of Things (IoT) devices and services, the capability to detect intrusions and malicious activities within IoT networks is critical for resilience of the network infrastructure. In this paper, we present a novel model for intrusion detection based on two-layer dimension reduction and two-tier classification module, designed to detect malicious activities such as User to Root (U2R) and Remote to Local (R2L) attacks. The proposed model is using component analysis and linear discriminate analysis of dimension reduction module to spate the high dimensional dataset to a lower one with lesser features. We then apply a two-tier classification module utilizing Naive Bayes and Certainty Factor version of K-Nearest Neighbor to identify suspicious behaviors. The experiment results using NSL-KDD dataset shows that our model outperforms previous models designed to detect U2R and R2L attacks.

A Two-Layer Dimension Reduction and Two-Tier Classification Model for Anomaly-Based Intrusion Detection in IoT Backbone Networks