Security Threats on National Defense ICT based on IoT

Yang, Jum-Hong; Lee, Hojae; Park, Minwoo; Eom, Jung-Ho

doi:10.14257/astl.2015.97.16

Cited by 14 publications

(9 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, cryptography techniques still have great relevance and importance for modern ICT (Information and Communication Technology), and ICT enabled industry to keep them protected from dynamically changing threat scenarios [2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

On Energy Optimization in Cryptographic Hash Functions and Symmetric Key Protocols

Harish

Talluri

2017

Preprint

View full text Add to dashboard Cite

Abstract:In this work we study the energy consumption by various modern secured hash functions (MD2, MD5, SHA-1, and SHA-2) and modern symmetric key encryption protocols (Blowfish, DES, 3DES, and AES) from the algorithmic perspective. We identify various parameters that moderate energy consumption of these hashes and protocols. Our work is directed towards redesigning or modifying these algorithms to make them consume lesser energy. As a first step, we try to determine the applicability of the asymptotic energy complexity model by Roy et. al. on these hashes and protocols. Specifically, we try to observe whether parallelizing the access of blocks of data in these algorithms reduces their energy consumption based on the energy model. Our results confirm the applicability of the energy model on these hashes and protocols. Our work is motivated by the relevance and importance of cryptographic hashes and symmetric key protocols for modern ICT (Information and Communication Technology), and ICT enabled industry to keep them protected from dynamically changing threat scenarios. Hence the design of more energy efficient hashes and protocols will definitely contribute in reducing the ICT energy consumption that is continuously increasing.

show abstract

Section: Introductionmentioning

confidence: 99%

On Energy Optimization in Cryptographic Hash Functions and Symmetric Key Protocols

Harish

Talluri

2017

Preprint

View full text Add to dashboard Cite

show abstract

“…A major component of this stream of literature revolves around challenges caused by cybersecurity issues. The challenges of a smart home's cybersecurity are abundant and the literature is aware of the complexity and inherent risk (Schrammel, Hochleitner, & Tscheligi, 2011;Yang et al, 2015;Weber, 2010;Komninos, Philippou,& Pitsillides, 2014;Hong, Suh, & Kim, 2009). Amongst many of the articles in this field, it is noted that although they intend to present a clear picture of challenges in the future of this technology, the set of issues presented is not exhaustive.…”

Section: Cybersecurity Issuesmentioning

confidence: 99%

“…Threats of IoT attacks have increased the risk of future warfare being conducted through this platform (Yang et al, 2015).…”

Section: Cybersecurity Issuesmentioning

confidence: 99%

“…The importance of cybersecurity in smart home technology goes beyond the acceptance and adoption of the technology by consumers largely because it affects the security of the general public. Studies suggest that without robust cybersecurity measures, the next generation of war tactics will utilize the vulnerability of IoT devices to launch attacks on a network-centric battlefield (Yang, 2015). should not apply due to certain restrictions in the data collection and analysis process.…”

Section: Summary and Key Findingsmentioning

confidence: 99%

“…However, the studies do not include cybersecurity as a factor in the technology adoption process. On the other hand, we observe a separate portion of literature that examined the challenges in the future of smart homes(Komninos et al, 2014;Yang et al, 2015). Identified in this second group of research are a set of cybersecurity challenges that can affect the experience of living in a smart home.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Cybersecurity in Consumer Adoption of Smart Home Technology

Iskandar¹

View full text Add to dashboard Cite

The highly anticipated smart home technology for everyday life has been growing over the past quarter century. Using standard technology adoption models, previous research produced conflicting results that did not reflect the market accurately. I Cybersecurity in Consumer Adoption of Smart Home Technology Raed Iskandar Glossary • Cybersecurity: the organization and collection of resources, processes, and structures used to protect cyberspace and cyberspace-enabled systems from occurrences that misalign de jure from de facto property rights. • Internet of Things (IoT): a dynamic global network infrastructure with self configuring capabilities based on standard and interoperable communication protocols where physical and virtual "things" have identities, physical attributes, and virtual personalities, use intelligent interfaces, and are seamlessly integrated into the information network.• Smart Homes: a residence equipped with computing and information technology which anticipates and responds to the needs of the occupants, working to promote their comfort, convenience, security, and entertainment through the management of technology within the home and connections to the world beyond.

show abstract

Study of the energy deposition of helicon plasmas driven by machine learning algorithms

Cheng

Xia

Xiong

2022

Contributions to Plasma Physics

View full text Add to dashboard Cite

To find a fast and reliable way predicting the energy deposition of helicon plasmas, this work focuses on machine learning algorithms. Data generation model and the distribution property of the source data are studied, then the classical algorithms and deep neural network (DNN) are built, and these algorithms are studied to test the performance on the energy deposition datasets. Both decision tree classifier (DTC) and support vector machine (SVM) find the electron temperature is the noise feature, and when it is included in the feature vector, the performance will degrade. Therefore feature selection needs be done to obtain high accuracy. For DNN, by directly changing the numbers of the hidden layers and units, test accuracy exceeds 0.95 when hidden layer is greater than three. The feature selection is automatically performed and the learning process is simpler in DNN. When the splitting ratio is varied, the generalization performance of DTC and SVM fluctuates, whereas DNN exhibits no evident change. Compared with the classical algorithms, DNN shows better stability when the source data are changed. The calculations suggest machine learning technique is a promising choice to predict the energy deposition of helicon plasmas.

show abstract

Security Threats on National Defense ICT based on IoT

Cited by 14 publications

References 1 publication

On Energy Optimization in Cryptographic Hash Functions and Symmetric Key Protocols

On Energy Optimization in Cryptographic Hash Functions and Symmetric Key Protocols

Cybersecurity in Consumer Adoption of Smart Home Technology

Study of the energy deposition of helicon plasmas driven by machine learning algorithms

Contact Info

Product

Resources

About