On the Entropy of Oscillator-Based True Random Number Generators under Ionizing Radiation

Martín, Honorio; Martín-Holgado, Pedro; Peris-López, Pedro; Morilla, Yolanda; Entrena, Luis

doi:10.3390/e20070513

Cited by 14 publications

(4 citation statements)

References 19 publications

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“…Since devices were operating within their specifications, these biasing effects could be considered out-of-band attacks which operate at limited distances. This is in contrast to, for example, Martín et al's work which investigates the entropy of TRNGs in response to ionizing radiation [144]. However, remote conducted attacks on TRNGs are possible using local voltage drops [123].…”

Section: Acoustic Emanationsmentioning

confidence: 87%

Taxonomy and Challenges of Out-of-Band Signal Injection Attacks and Defenses

Giechaskiel

Rasmussen

2020

IEEE Commun. Surv. Tutorials

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Recent research has shown that the integrity of sensor measurements can be violated through out-of-band signal injection attacks. These attacks target the conversion process from a physical quantity to an analog property-a process that fundamentally cannot be authenticated. Out-of-band signal injection attacks thus pose previously-unexplored security risks by exploiting hardware imperfections in the sensors themselves, or in their interfaces to microcontrollers. In response to the growingyet-disjointed literature in the subject, this article presents the first survey of out-of-band signal injection attacks. It focuses on unifying their terminology and identifying commonalities in their causes and effects through a chronological, evolutionary, and thematic taxonomy of attacks. By highlighting cross-influences between different types of out-of-band signal injections, this paper underscores the need for a common language irrespective of the attack method. By placing attack and defense mechanisms in the wider context of their dual counterparts of side-channel leakage and electromagnetic interference, this study identifies common threads and gaps that can help guide and inform future research. Overall, the ever-increasing reliance on sensors embedded in everyday commodity devices necessitates that a stronger focus be placed on improving the security of such systems against out-of-band signal injection attacks.

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Section: Acoustic Emanationsmentioning

confidence: 87%

Taxonomy and Challenges of Out-of-Band Signal Injection Attacks and Defenses

Giechaskiel

Rasmussen

2020

IEEE Commun. Surv. Tutorials

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“…This paper uses statistics-based measurement methods to speed up the entropy estimate; these methods are the most common values, collision tests, Markov tests, and compression tests [ 24 ]. The most common value estimate obtains entropy by using the probability that a value will appear frequently in the input data set, shown as Equation (2).…”

Section: The Proposed Detection Methodologymentioning

confidence: 99%

Effective Ransomware Detection Using Entropy Estimation of Files for Cloud Services

Lee

Yim

2023

Sensors

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A variety of data-based services such as cloud services and big data-based services have emerged in recent times. These services store data and derive the value of the data. The reliability and integrity of the data must be ensured. Unfortunately, attackers have taken valuable data as hostage for money in attacks called ransomware. It is difficult to recover original data from files in systems infected by ransomware because they are encrypted and cannot be accessed without keys. There are cloud services to backup data; however, encrypted files are synchronized with the cloud service. Therefore, the original file cannot be restored even from the cloud when the victim systems are infected. Therefore, in this paper, we propose a method to effectively detect ransomware for cloud services. The proposed method detects infected files by estimating the entropy to synchronize files based on uniformity, one of the characteristics of encrypted files. For the experiment, files containing sensitive user information and system files for system operation were selected. In this study, we detected 100% of the infected files in all file formats, with no false positives or false negatives. We demonstrate that our proposed ransomware detection method was very effective compared to other existing methods. Based on the results of this paper, we expect that this detection method will not synchronize with a cloud server by detecting infected files even if the victim systems are infected with ransomware. In addition, we expect to restore the original files by backing up the files stored on the cloud server.

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“…According to the measurement methods used, these can be classified into a statistic-based measurement method and a predictor-based measurement method [22]. In this paper, we use statistic-based measurement methods to speed up the measurement: these are the most common value estimate, the collision test estimate, the Markov test estimate, and the compression test estimate [23]. The methods measure entropy based on frequently occurring probabilities, random repetitive patterns, and dependencies between successive values.…”

Section: A Entropy Measurement Methodsmentioning

confidence: 99%

Machine Learning Based File Entropy Analysis for Ransomware Detection in Backup Systems

Lee

Yim

2019

IEEE Access

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With the advent of big data and cloud services, user data has become an important issue. Although a variety of detection and prevention technologies are used to protect user data, ransomware that demands money in exchange for one's data has emerged. In order to detect and prevent ransomware, file-and behavior-based detection methods have been investigated. Nevertheless, we are still facing from ransomware threats, as it is difficult to detect and prevent ransomware containing unknown malicious codes. In particular, these methods are limited in that they cannot detect ransomware for backup systems such as cloud services. For instance, if files infected with ransomware are synchronized with the backup systems, the infected files will not be able to be restored through the backed-up files. In this paper, we utilize an entropy technique to measure a characteristic of the encrypted file (i.e., uniformity). Machine learning is applied for classifying infected files based file entropy analysis. The proposed method can recover the original file from the backup system by detecting ransomware infected files that have been synchronized to the backup system, even if the user system is infected by ransomware. Conducted analysis results confirm that the proposed method provides a high detection rate with low false positive and false negative rates compared with the existing detection methods.

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On the Entropy of Oscillator-Based True Random Number Generators under Ionizing Radiation

Cited by 14 publications

References 19 publications

Taxonomy and Challenges of Out-of-Band Signal Injection Attacks and Defenses

Taxonomy and Challenges of Out-of-Band Signal Injection Attacks and Defenses

Effective Ransomware Detection Using Entropy Estimation of Files for Cloud Services

Machine Learning Based File Entropy Analysis for Ransomware Detection in Backup Systems

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