Publikationen 2024

Script list Publications

(1) Advantages of Unsupervised Learning Analysis Methods in Single-Trace SCA Attacks
M. Aftowicz, I. Kabin, Z. Dyka, P. Langendörfer
Microprocessors and Microsystems 105, 104994 (2024)
DOI: 10.1016/j.micpro.2023.104994, (Total Resilience)
Machine learning techniques are commonly employed in the context of Side Channel Analysis attacks. The clustering algorithms can be successfully used as classifiers in single execution attacks against implementations of Elliptic Curve point multiplication known as kP operation. They can distinguish between the processing of ‘ones’ and ‘zeros’ during secret scalar processing in the binary kP algorithm. The successful SCA performed by designers can aid in recognizing the leakage sources in cryptographic designs and lead to improvement of the cryptographic implementations. In this work we investigate the influence of the hamming weight of scalar k on the success rate of the single-trace attack. We used the clustering method K-means and the statistical method the comparison to the mean. We analysed simulated power traces and power traces of an FPGA implementation to conclude that K-means, unlike the comparison to the mean, was able to deal with extracting the scalar even when it is consisted of less than 30% of ‘ones’ and more than 70% of ‘ones’.

(2) Non-Profiled Unsupervised Horizontal Iterative Attack Against Hardware Elliptic Curve Scalar Multiplication using Machine Learning
M. Aftowicz, I. Kabin, Z. Dyka, P. Langendörfer
Future Internet (MDPI) 16(2), 45 (2024)
DOI: 10.3390/fi16020045
While IoT technology makes industries, cities, and homes smarter, it also opens the door to security risks. With the right equipment and physical access to the devices, the attacker can leverage side-channel information, like timing, power consumption or electromagnetic emanation to compromise cryptographic operations and extract the secret key. This work presents a side-channel analysis of a cryptographic hardware accelerator for Elliptic Curve Scalar Multiplication operation, implemented in a Field Programmable Gate Array and as an Application Specific Integrated Circuit. The presented framework consists in initial key extraction using a state-of-the-art statistical horizontal attack and is followed by regularized Artificial Neural Networks, which take as input the partially incorrect key guesses from the horizontal attack and correct them iteratively. The initial correctness of the horizontal attack, measured as the fraction of correctly extracted bits of the secret key, was improved from 75% to 98% by applying the iterative learning.

(3) Investigating the Security of OpenPLC: Vulnerabilities, Attacks, and Mitigation Solutions
W. Alsabbagh, C. Kim, P. Langendörfer
IEEE Access 12, 11561 (2024)
DOI: 10.1109/ACCESS.2024.3356051
Open-source Programmable Logic Controller (OpenPLC) has recently gained substantial interest within both the research and industrial communities. It presents an affordable and practical alternative solution for the high-cost of real hardware PLCs. But the project has not fulfilled the security level that is required for Industrial Control Systems (ICSs). Therefore, it becomes imperative to scrutinize the project's security landscape, identifying vulnerabilities that could expose OpenPLC and its related systems to threats. This article focuses precisely on this objective, undertaking intensive investigations into OpenPLC. To substantiate our findings, we conduct a sophisticated control logic injection attack, compromising the user authentication and modifying the program executed by the OpenPLC. Additionally, based on our results, we introduce a security-enhanced OpenPLC software called OpenPLC Aqua. Our developed software is equipped with a set of security solutions designed specifically to address the vulnerabilities to which current OpenPLC versions are prone.

(4) A Survey on Sensor- and Communication-based Issues of Autonomous UAVs
P. Mykytyn, M. Brzozowski, Z. Dyka, P. Langendörfer
Computer Modeling in Engineering & Sciences 138(2), 1019 (2024)
DOI: 10.32604/cmes.2023.029075, (iCampus II)
The application field for Unmanned Aerial Vehicle (UAV) technology and its adoption rate have been increasing steadily in the past years. Decreasing cost of commercial drones has enabled their use at a scale broader than ever before. However, increasing the complexity of UAVs and decreasing the cost, both contribute to a lack of implemented security measures and raise new security and safety concerns. For instance, the issue of implausible or tampered UAV sensor measurements is barely addressed in the current research literature and thus, requires more attention from the research community. The goal of this survey is to extensively review state-of-the-art literature regarding common sensor- and communication-based vulnerabilities, existing threats, and active or passive cyber-attacks against UAVs, as well as shed light on the research gaps in the literature. In this work, we describe the Unmanned Aerial System (UAS) architecture to point out the origination sources for security and safety issues. We evaluate the coverage and completeness of each related research work in a comprehensive comparison table as well as classify the threats, vulnerabilities and cyber-attacks into sensor-based and communication-based categories. Additionally, for each individual cyber-attack, we describe existing countermeasures or detection mechanisms and provide a list of requirements to ensure UAV’s security and safety. We also address the problem of implausible sensor measurements and introduce the idea of a plausibility check for sensor data. By doing so, we discover additional measures to improve security and safety and report on a research niche that is not well represented in the current research literature.

(5) Employing Optical Beam-Induced Current Measurement in Side-Channel Analysis
D. Petryk, I. Kabin, J. Bělohoubek, P. Fišer, J. Schmidt, M. Krstic, Z. Dyka
Proc. 36. ITG/GMM/GI-Workshop Testmethoden und Zuverlässigkeit von Schaltungen und Systemen (TuZ 2024), 15 (2024)
(Total Resilience)

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