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

Context Aware Routing Management Architecture for Airborne Networks

Military environments require highly dynamic mobile ad hoc networks (MANETs) to meet operational mission requirements. Decision makers rely on the timely delivery of critical battlefield information to make informed determinations quickly and as accurately as possible. However, traditional MANET routing protocols do not provide quality of service (QoS). Furthermore, they do not implement active controls to minimise the impact of network congestion. This study proposes the use of the information embedded in an air tasking order (ATO) during the planning phase of military missions to optimise the network performance. The trajectories of relevant nodes (airborne platforms) participating in the MANET can be forecasted by parsing key information contained in the ATO. Using this idea it is possible to optimise network routes to minimise edge overutilisation and increase network throughput. In onesimulated test case, there was a 25% improvement of network throughput, and 23% reduction on dropped packets. Using this technique, the authors can selectively preserve the QoS by establishing network controls that drop low-priority packets when necessary. The algorithm improves the overall MANET throughput while minimising the packets dropped due to network congestion

Securing ZigBee Commercial Communications Using Constellation Based Distinct Native Attribute Fingerprinting

This work provides development of Constellation Based DNA (CB-DNA) Fingerprinting for use in systems employing quadrature modulations and includes network protection demonstrations for ZigBee offset quadrature phase shift keying modulation. Results are based on 120 unique networks comprised of seven authorized ZigBee RZSUBSTICK devices, with three additional like-model devices serving as unauthorized rogue devices. Authorized network device fingerprints are used to train a Multiple Discriminant Analysis (MDA) classifier and Rogue Rejection Rate (RRR) estimated for 2520 attacks involving rogue devices presenting themselves as authorized devices. With MDA training thresholds set to achieve a True Verification Rate (TVR) of TVR = 95% for authorized network devices, the collective rogue device detection results for SNR ≥ 12 dB include average burst-by-burst RRR ≈ 94% across all 2520 attack scenarios with individual rogue device attack performance spanning 83.32% \u3c RRR \u3c 99.81%

Extending Critical Infrastructure Element Longevity using Constellation-based ID Verification

This work supports a technical cradle-to-grave protection strategy aimed at extending the useful lifespan of Critical Infrastructure (CI) elements. This is done by improving mid-life operational protection measures through integration of reliable physical (PHY) layer security mechanisms. The goal is to improve existing protection that is heavily reliant on higher-layer mechanisms that are commonly targeted by cyberattack. Relative to prior device ID discrimination works, results herein reinforce the exploitability of constellation-based PHY layer features and the ability for those features to be practically implemented to enhance CI security. Prior work is extended by formalizing a device ID verification process that enables rogue device detection demonstration under physical access attack conditions that include unauthorized devices mimicking bit-level credentials of authorized network devices. The work transitions from distance-based to probability-based measures of similarity derived from empirical Multivariate Normal Probability Density Function (MVNPDF) statistics of multiple discriminant analysis radio frequency fingerprint projections. Demonstration results for Constellation-Based Distinct Native Attribute (CB-DNA) fingerprinting of WirelessHART adapters from two manufacturers includes 1) average cross-class percent correct classification of %C \u3e 90% across 28 different networks comprised of six authorized devices, and 2) average rogue rejection rate of 83.4% ≤ RRR ≤ 99.9% based on two held-out devices serving as attacking rogue devices for each network (a total of 120 individual rogue attacks). Using the MVNPDF measure proved most effective and yielded nearly 12% RRR improvement over a Euclidean distance measure

Adaptive-hybrid Redundancy with Error Injection

Adaptive-Hybrid Redundancy (AHR) shows promise as a method to allow flexibility when selecting between processing speed and energy efficiency while maintaining a level of error mitigation in space radiation environments. Whereas previous work demonstrated AHR’s feasibility in an error free environment, this work analyzes AHR performance in the presence of errors. Errors are deliberately injected into AHR at specific times in the processing chain to demonstrate best and worst case performance impacts. This analysis demonstrates that AHR provides flexibility in processing speed and energy efficiency in the presence of error

Securing ZigBee Commercial Communications Using Constellation Based Distinct Native Attribute Fingerprinting

This work provides development of Constellation Based DNA (CB-DNA) Fingerprinting for use in systems employing quadrature modulations and includes network protection demonstrations for ZigBee offset quadrature phase shift keying modulation. Results are based on 120 unique networks comprised of seven authorized ZigBee RZSUBSTICK devices, with three additional like-model devices serving as unauthorized rogue devices. Authorized network device fingerprints are used to train a Multiple Discriminant Analysis (MDA) classifier and Rogue Rejection Rate (RRR) estimated for 2520 attacks involving rogue devices presenting themselves as authorized devices. With MDA training thresholds set to achieve a True Verification Rate (TVR) of TVR = 95% for authorized network devices, the collective rogue device detection results for SNR ≥ 12 dB include average burst-by-burst RRR ≈ 94% across all 2520 attack scenarios with individual rogue device attack performance spanning 83.32% < RRR < 99.81%