Biblio

Physical Unclonable Functions (PUFs) are vulnerable to various modelling attacks. The chaotic behaviour of oscillating systems can be leveraged to improve their security against these attacks. We have integrated an Arbiter PUF implemented on a FPGA with Chua's oscillator circuit to obtain robust final responses. These responses are tested against conventional Machine Learning and Deep Learning attacks for verifying security of the design. It has been found that such a design is robust with prediction accuracy of nearly 50%. Moreover, the quality of the PUF architecture is evaluated for uniformity and uniqueness metrics and Monte Carlo analysis at varying temperatures is performed for determining reliability.

Silicon Physical Unclonable Function (PUF) is arguably the most promising hardware security primitive. In particular, PUFs that are capable of generating a large amount of challenge response pairs (CRPs) can be used in many security applications. However, these CRPs can also be exploited by machine learning attacks to model the PUF and predict its response. In this paper, we first show that, based on data in the public domain, two popular PUFs that can generate CRPs (i.e., arbiter PUF and reconfigurable ring oscillator (RO) PUF) can be broken by simple logistic regression (LR) attack with about 99% accuracy. We then propose a feedback structure to XOR the PUF response with the challenge and challenge the PUF again to generate the response. Results show that this successfully reduces LR's learning accuracy to the lower 50%, but artificial neural network (ANN) learning attack still has an 80% success rate. Therefore, we propose a configurable ring oscillator based dual-mode PUF which works with both odd number of inverters (like the reconfigurable RO PUF) and even number of inverters (like a bistable ring (BR) PUF). Since currently there are no known attacks that can model both RO PUF and BR PUF, the dual-mode PUF will be resistant to modeling attacks as long as we can hide its working mode from the attackers, which we achieve with two practical methods. Finally, we implement the proposed dual-mode PUF on Nexys 4 FPGA boards and collect real measurement to show that it reduces the learning accuracy of LR and ANN to the mid-50% and low 60%, respectively. In addition, it meets the PUF requirements of uniqueness, randomness, and robustness.

In modern cyber-physical systems and wireless sensor networks the complexity of crisis management processes is caused by a variety of software/hardware assets and communication protocols, the necessity of their collaborative function, possible inconsistency of data flows between particular devices and increased requirements to cyber-physical security. A crisis management oriented model of a communicational mobile network is constructed. A general architecture of network nodes by the use of XBee circuits, Arduino microcontrollers and connecting equipment are developed. An analysis of possible cyber-physical security events on the base of existing intruder models is performed. A series of experiments on modeling attacks on network nodes is conducted. Possible ways for attack revelations by means of components for security event collection and data correlation is discussed.

This paper aims to address the security challenges on physical unclonable functions (PUFs) raised by modeling attacks and denial of service (DoS) attacks. We develop a hardware isolation-based secure architecture extension, namely PUFSec, to protect the target PUF from security compromises without modifying the internal PUF design. PUFSec achieves the security protection by physically isolating the PUF hardware and data from the attack surfaces accessible by the adversaries. Furthermore, we deploy strictly enforced security policies within PUFSec, which authenticate the incoming PUF challenges and prevent attackers from collecting sufficient PUF responses to issue modeling attacks or interfering with the PUF workflow to launch DoS attacks. We implement our PUFSec framework on a Xilinx SoC equipped with ARM processor. Our experimental results on the real hardware prove the enhanced security and the low performance and power overhead brought by PUFSec.

Smart Grids (SGs) are Critical Infrastructures (CI), which are responsible for controlling and maintaining the distribution of electricity. To manage this task, modern SGs integrate an Information and Communication Infrastructure (ICT) beside the electrical power grid. Aside from the benefits derived from the increasing control and management capabilities offered by the ICT, unfortunately the introduction of this cyber layer provides an attractive attack surface for hackers. As a consequence, security becomes a fundamental prerequisite to be fulfilled. In this context, the adoption of Systems Engineering (SE) tools combined with Modeling and Simulation (M&S) techniques represent a promising solution to support the evaluation process of a SG during early design stages. In particular, the paper investigates on the identification, modeling and assessment of attacks in SG environments, by proposing a model for representing attack scenarios as a combination of attack types, attack schema and their temporal occurrence. Simulation techniques are exploited to enable the execution of such attack combinations in the SG domain. Specifically, a simulator, which allows to assess the SG behaviour to identify possible flaws and provide preventive actions before its realization, is developed on the basis of the proposed model and exemplified through a case study.

In this paper, machine learning attacks are performed on a novel hybrid delay based Arbiter Ring Oscillator PUF (AROPUF). The AROPUF exhibits improved results when compared to traditional Arbiter Physical Unclonable Function (APUF). The challenge-response pairs (CRPs) from both PUFs are fed to the multilayered perceptron model (MLP) with one hidden layer. The results show that the CRPs generated from the proposed AROPUF has more training and prediction errors when compared to the APUF, thus making it more difficult for the adversary to predict the CRPs.