Literature reviews for 2 topics feb 23

Objective Summary 1

Introduction

Ahmed et al. (2018) are the researchers who wanted to reveal the kind of cyberattacks happening in various cyber systems. The six authors of the work are qualified since they presented their work through a conference paper with a good global reputation. Ahmed et al. (2018) conducted the research in two areas comprising water treatment and distribution facilities, SWaT and WADI. The authors failed to mention the location of the study. The study shows that the noise fingerprint can uniquely identify many sensors with an accuracy greater than 90%. The vulnerability of the systems to cyberattacks depends on the system's understanding and the profile of noise it detects.

Fingerprint sensors and noise detection are two technologies that help identify attacks on technological devices. Ahmed et al. (2018) researched two sample testbeds that dealt with water. The SWaT testbed helped the authors to develop a model with the right components and physics, using concepts of first principles (Ahmed et al., 2018). The authors easily established a model used in the second testbed. According to the findings, the suggested approach can identify zero-alarm assaults, whereas statistical reference methods cannot. Furthermore, scientists have demonstrated that sensors may be recognized uniquely with more than 90% accuracy. The authors need more accuracy, which is possible through the distinction between the sensor's noise and the process.

Conclusion

Biometrics improves online security and protects users from potential data breaches. These technological systems operate while producing data for both the fingerprint and the noise. This study assesses NoisePrint using testbeds for water delivery and treatment. The authors performed not one but two tests of the NoisePrint system, each on a separate testbed. The often-used industrial sensors are analyzed, but the research is broadly relevant to other industrial applications. This research presents better and more secure ways of accessing user data in the face of current technological risks.

Objective Summary 2

Introduction

Ingale et al. (2020) are the researchers who worked on developing new opportunities for previous research concerning biometrics. These authors perform research on various topics that are relevant under the theme of biometrics. The researchers were from San Jose State University, and some were qualified members of the IEEE. The authors did not mention the timespan when they conducted the research or the location of the experiment.

Summary

           The study design was an experimental setup where the authors engaged several ECG databases. Five of these databases were on-the-person, while the remaining one was in-house off-the-person. The researchers use the model databases to analyze several metrics pertaining to the topic of discussion, to validate the performance of the model. The authors further used fivefold cross-validation of performance to assess the effectiveness of the model. Ingale et al. (2020) revealed that manufacturers could achieve 100% accuracy on fixed window segmentation with FIR when they combine 1.86% FAR. Also, the study assesses 1,694 subjects and found an EER of 2.11%. The study shows that the ECG biometric authentication model surpasses the contemporary models currently in use but has insufficient abilities to filter, segment, match, and extract features.

Conclusion

           The authors of this paper came up with several datasets that pertain to biometric safety. They do an exemplary job of presenting both the pros and cons of the field of biometric safety. The authors, through this work, present a new approach that improves the performance of biometric security measures.

Objective Summary 3

Introduction

               This paper highlights the concept of modern type of computing and the influence of storage on the performance of edge computing. Edge computing has been quite revolutionary in several areas of current computing and technology in that field, including elements like artificial intelligence (AI). Mahadevappa et al. (2021) researched identifying an attack and isolating data that has been infected without interfering with the other nodes nearby. The authors of this paper should have mentioned the research date, and the study area was also included.

Summary

               This research had the methodology of separating various data so that modern safety of computers becomes optimum for users and other stakeholders. The study focuses on the possible ways to separate infected data from the rest of the ones that have not suffered interference. The edge nodes for data acquisition detected the intruders and quarantined other devices suspected by dimensionality reduction. The quarantine stage of the concept creates reputation scores that identify and sanitize false alarms on devices. The preliminary investigation that the authors performed in the research was effective in reducing obstacles to improve the efficiency of biometric security. The LDA helps increase the accuracy of quarantine by 72.83% in 0.9 seconds of training time.

Conclusion

               The study above helped to show that the biometric technology can be modified to help reduce the possible attacks on computer systems that companies use often. The safety of biometrics and computer systems is dependent on several factors as the authors present in their study. The authors provide a solution for data quarantine in edge computing. The model uses lightweight dimensionality reduction to quarantine data that has encountered intrusion. These researchers propose a future model to validate the findings and improve estimation efficiency.

References

Ahmed, C. M., Ochoa, M., Zhou, J., Mathur, A. P., Qadeer, R., Murguia, C., & Ruths, J. (2018, May). Noiseprint: Attack detection using sensor and process noise fingerprint in cyber physical systems. In  Proceedings of the 2018 on Asia Conference on Computer and Communications Security (pp. 483-497). https://doi.org/10.1145/3196494.3196532

Ingale, M., Cordeiro, R., Thentu, S., Park, Y., & Karimian, N. (2020). ECG biometric authentication: A comparative analysis. IEEE Access, 8, 117853-117866. Retrieved from https://ieeexplore.ieee.org/iel7/6287639/8948470/09123339.pdf

Mahadevappa, P., & Murugesan, R. K. (2021). A data quarantine model to secure data in edge computing. arXiv preprint arXiv:2111.07672. Retrieved from https://arxiv.org/pdf/2111.07672