Channel-PUFs: AI-Assisted Channel Estimation for Enhanced Wireless Network Security


Next Generation Mobile Networks (NGMNs) are entering existing and future (industrial) wireless networks, associated with advantages such as higher data throughput, low latency, operation in almost real-time, and the microcell approach. However, this development also comes with drawbacks in the form of new attack vectors and security threats.

The PhySec Thing: About Trust and Security in Industrial IoT Systems


The developments of the fourth industrial revolution with the fusion of technologies to Cyber-Physical Production System (CPPS), accompanied by the growing (wireless) interconnection of a multitude of different components (sensors, actuators, and machines, for example) opens up a multitude of risks, attack vectors and security threats. The Physical Layer Security (PhySec) approach proposed in this paper enables an efficient and resource-saving yet sound and secure safeguarding of industrial networks. The elaborated results of the Secret Key Generation (SKG) algorithms to establish Symmetric Key Cryptography (SKC) in wireless networks on the one hand and the SRAM-based Physically Unclonable Functions (PUFs) to derive hardware-intrinsic cryptographic credentials, on the other hand, demonstrate the appropriateness of the PhySec principle.

An Automated, Disruption-Tolerant Device Authentication and Key Management Framework for Critical Systems


Key management is critical to secure operation. Distributed control systems, such as Supervisory Control and Data Acquisition (SCADA) systems, have unique operational requirements that make conventional key management solutions less effectiveand burdensome. This paper pres-ents a novel Kerberos-based framework for automated, disruption-tolerant key management for control system environments. Experimental tests and their results are presented to quantify the expected performance overhead of this approach. Additionally, Zeek sensor analytics are presented to aid in monitoring the health and security of the key management framework operation.

Biometric vs. Password Authentication: A User’s Perspective


This study investigates the main factors that affect adoption of biometric authentication. A purposive sample of 85 network users from the Philadelphia area was used for this study. A laboratory experiment was also carried out to assess false reject and false accept rates. The study found that a large majority (84%) of people would prefer biometric authentication. Privacy, cost, accuracy, and the perception of biometric technology are the main concerns that hinder adoption of this technology. False accept rate was found not to be high enough to cause concerns. Finally, the many benefits of using biometric authentication greatly outweigh those of password authentication.

Software Implementation using Hardware-Based Verification for Secure Content Delivery


This paper presents a novel method for secure message transmission – the Software Implementation using Hardware-Based Verification for Secure Content Delivery (SIHBVSCD) method.  This method incorporates a two-tier security protocol which allows messages to be verified at both the user level (coming from a particular user) and hardware level (originating from a particular machine) providing protection from espionage and/or clandestine manipulation of information.  SIHBVSCD securely sets up a one-time symmetric key used for transmission, offering advantages over both the high theft/loss likelihood of smartcards and the inability of hardware-based verification for machines that do not contain hardware capable of remote attestation.

Introduction of Random Forest Classifier to ZigBee Device Network Authentication Using RF-DNA Fingerprinting


The decentralized architecture of ZigBee ad-hoc networks creates unique security challenges to ensure only authentic devices are granted network access. Non-parametric Random Forest (RndF) and Multi-Class AdaBoost (MCA) ensemble classifiers were introduced with RF-Distinct Native Attribute (RF-DNA) fingerprinting to enhance device authentication performance. Correct classification (%C) performance is improved up to 24% over other classifiers, with 10% improvement at the lowest SNR = 0.0 dB. Network intrusion tests correctly rejected 31/36 rogue devices vs. 25/36 and 28/36 with previously used classifiers. The key benefit of ensemble method processing is improved rogue rejection in noisy environments–gains of up to Gs = 18.0 dB are realized over other classifiers. Collectively considering demonstrated %C and rogue rejection capability, the use of ensemble methods improves ZigBee network authentication and enhances anti-spoofing protection afforded by RF-DNA fingerprinting.

Journal of Information Warfare

The definitive publication for the best and latest research and analysis on information warfare, information operations, and cyber crime. Available in traditional hard copy or online.












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The definitive publication for the best and latest research and analysis on information warfare, information operations, and cyber crime. Available in traditional hard copy or online.


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