Dimensional Reduction Analysis for Constellation-Based DNA Fingerprinting to Improve Industrial IoT Wireless Security

The Industrial Internet of Things (IIoT) market is skyrocketing towards 100 billion deployed devices and cybersecurity remains a top priority. This includes security of ZigBee communication devices that are widely used in industrial control system applications. IIoT device security is addressed using Constellation-Based Distinct Native Attribute (CB-DNA) Fingerprinting to augment conventional bit-level security mechanisms. This work expands upon recent CB-DNA “discovery” activity by identifying reduced dimensional fingerprints that increase the computational efficiency and effectiveness of device discrimination methods. The methods considered include Multiple Discriminant Analysis (MDA) and Random Forest (RndF) classification. RndF deficiencies in classification and post-classification feature selection are highlighted and addressed using a pre-classification feature selection method based on a Wilcoxon Rank Sum (WRS) test. Feature down-selection based on WRS testing proves to very reliable, with reduced feature subsets yielding cross-device discrimination performance consistent with full-dimensional feature sets, while being more computationally efficient

Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Models

In this paper, we propose a flexible notion of characteristic functions defined on graph vertices to describe the distribution of vertex features at multiple scales. We introduce FEATHER, a computationally efficient algorithm to calculate a specific variant of these characteristic functions where the probability weights of the characteristic function are defined as the transition probabilities of random walks. We argue that features extracted by this procedure are useful for node level machine learning tasks. We discuss the pooling of these node representations, resulting in compact descriptors of graphs that can serve as features for graph classification algorithms. We analytically prove that FEATHER describes isomorphic graphs with the same representation and exhibits robustness to data corruption. Using the node feature characteristic functions we define parametric models where evaluation points of the functions are learned parameters of supervised classifiers. Experiments on real world large datasets show that our proposed algorithm creates high quality representations, performs transfer learning efficiently, exhibits robustness to hyperparameter changes, and scales linearly with the input size.Comment: Source code is available at: https://github.com/benedekrozemberczki/FEATHE

Cyber-Physical Security with RF Fingerprint Classification through Distance Measure Extensions of Generalized Relevance Learning Vector Quantization

Radio frequency (RF) fingerprinting extracts fingerprint features from RF signals to protect against masquerade attacks by enabling reliable authentication of communication devices at the “serial number” level. Facilitating the reliable authentication of communication devices are machine learning (ML) algorithms which find meaningful statistical differences between measured data. The Generalized Relevance Learning Vector Quantization-Improved (GRLVQI) classifier is one ML algorithm which has shown efficacy for RF fingerprinting device discrimination. GRLVQI extends the Learning Vector Quantization (LVQ) family of “winner take all” classifiers that develop prototype vectors (PVs) which represent data. In LVQ algorithms, distances are computed between exemplars and PVs, and PVs are iteratively moved to accurately represent the data. GRLVQI extends LVQ with a sigmoidal cost function, relevance learning, and PV update logic improvements. However, both LVQ and GRLVQI are limited due to a reliance on squared Euclidean distance measures and a seemingly complex algorithm structure if changes are made to the underlying distance measure. Herein, the authors (1) develop GRLVQI-D (distance), an extension of GRLVQI to consider alternative distance measures and (2) present the Cosine GRLVQI classifier using this framework. To evaluate this framework, the authors consider experimentally collected Z -wave RF signals and develop RF fingerprints to identify devices. Z -wave devices are low-cost, low-power communication technologies seen increasingly in critical infrastructure. Both classification and verification, claimed identity, and performance comparisons are made with the new Cosine GRLVQI algorithm. The results show more robust performance when using the Cosine GRLVQI algorithm when compared with four algorithms in the literature. Additionally, the methodology used to create Cosine GRLVQI is generalizable to alternative measures