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All Journal Indonesian Journal of Electrical Engineering and Computer Science Journal of Geology and Exploration
Taha, Ahmed
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Earth & Planetary Sciences Engineering

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Tailoring AES for resource-constrained IoT devices Saleh, Shaimaa S.; Al-Awamry, Amr A.; Taha, Ahmed
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp290-301

Abstract

The internet of things (IoT) is a network of interconnected hardware, software, and many infrastructures that require cryptography solutions to provide security. IoT security is a critical concern, and it can be settled by using cryptographic algorithms such as advanced encryption standard (AES) for encryption and authentication. A fundamental component within the AES algorithm is the substitution box (S-box), which generates confusion and nonlinearity between plaintext and ciphertext, strengthening the process of security. This paper introduces a comparative analysis to offer valuable knowledge of the factors related to different S-box modifications, which will ultimately affect the design of cryptographic systems that use the AES algorithm. Then, a tailored AES algorithm is proposed for resource-constrained IoT devices by changing the standard S-box with another S-box. The new S-box reduces the rounds number and the time needed for the AES algorithm’s encryption, decryption, and key expansion. The performance of the proposed AES is assessed through various experiments. Therefore, our tailored AES with the new S-box is more secure and efficient than AES with a standard S-box.
Distinguishing Algorith for Gold Deposit Types Mahmoud, Abdelhalim; Abdelsamad, Mansour; Taha, Ahmed; Mansour , Ahmed; Nassif, Mariam; Abdelfatah, Sara; Saleeb, Mera; Khaled, Rabee
Journal of Geology and Exploration Vol. 3 No. 2 (2024): Journal of Geology and Exploration, December 2024
Publisher : CV Insight Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58227/jge.v3i2.184

Abstract

The determination of gold deposit type holds a great economic significance since each gold deposit type displays its own grade and tonnage and consequently requires different exploration and exploitation strategies. The considerable diversity of gold deposits, combined with the distinctive features inherent to each type and the notable overlap among many deposits, renders the accurate classification of these deposits a complex endeavor. To differentiate between these deposit types, we collected geological, mineralogical, and geochemical characteristics, as well as ore-forming parameters, for 12 gold deposit types. A detailed classification scheme is utilized, covering four specific categories of gold deposits, namely orogenic, including greenstone-hosted, banded iron formation-hosted, and turbidite-hosted; reduced intrusion-related deposits; and oxidized intrusion-related gold deposits, which encompass Au-Cu-porphyry, Au-skarn, and high-sulfidation epithermal deposits, with a fourth class incorporating other deposit types, such as low-sulfidation epithermal, Carlin-type, and Au-volcanic massive sulfide deposits. The tabulated distinctive characteristics were used to construct a series of decision trees for gold deposit type identification. The distinguishing algorithm is formulated in the form of a Java computer application. Three decision trees are implemented for the purpose of ascertaining the type of gold deposit. If two decision trees yield a consensus on a particular type, the ore type identification is made accordingly. To validate the outcome, the user is prompted to respond to a series of questions pertaining to the identified type, with the accuracy rate of the responses must exceed 90%. Failure to meet this criterion will result in the decision tree being revisited, and the accurate data will need to be re-entered.
Co-Authors Abdelfatah, Sara Abdelsamad, Mansour Al-Awamry, Amr A. Khaled, Rabee Mahmoud, Abdelhalim Mansour , Ahmed Nassif, Mariam Saleeb, Mera Saleh, Shaimaa S.