Publications 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) A Feature Extraction Approach for the Detection of Phishing Websites Using Machine Learning
S.C. Gundla, M.K. Praveen, M.J.K. Reddy, G. Gourav, A. Pankaj, Z. Stamenkovic, S.P. Raja
Journal of Circuits, Systems, and Computers (JCSC) 33(2), 2450031 (2024)
DOI: 10.1142/S0218126624500312, (BB-KI-Chips)
In this growing world of the internet, most of our daily routine tasks are somehow connected to the internet, from smartphones to IoT devices to cloud networks. Internet users are growing rapidly, and the internet is accessible to everyone from anywhere. Data phishing is a cyber security attack that uses deception to trick internet users to get their content and information. In these some malicious users try to steal personal data such as bank details, login credentials, credit card details, and health care information of others on the internet. They exploit users’ sensitive information using vulnerabilities. Information stealers are known as Phishers. Phishers use different techniques for phishing. One of the most common methods is to direct users to some false website to enter their login credentials and their details on these phishing sites. They look the same as the original websites. Phishers use these details to get access to the user’s accounts and hijack the user account for monetary purposes. Many internet users get into this trap of phishing sites and share their personal and sensitive details. In this paper, we will analyze and implement machine-learning techniques to detect phishing attacks. There are different methods to identify phishing attacks, one of them is by checking the URL address using Machine learning. ML is used as a way to teach a machine to differentiate between phishing and original site URLs. There are many different techniques to overcome this attack. This research paper aims to provide accurate and true phishing detection with less time complexity.

(3) 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.

(4) Real-Time Wideband Video Synchronization via an Analog QPSK Costas Loop in a Laboratory Demonstration of an E-Band Satellite Downlink
J. Wörmann, L. Manoliu, S. Haussmann, M. Krstic, I. Kallfass
Proc. IEEE Space Hardware and Radio Conference (SHaRC 2024), 23 (2024)
DOI: 10.1109/SHaRC59908.2024.10438501

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