Data security using random dynamic salting and AES based on master-slave keys for Iraqi dam management system

Ali, Hussam J.; Jawad, Talib M.; Zuhair, Hiba

doi:10.11591/ijeecs.v23.i2.pp1018-1029

Cited by 7 publications

(8 citation statements)

References 27 publications

(31 reference statements)

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“…Fortunately, our protocol generates ElGamal random keys and converts them into small sizes of length 256, which are suitable for encryption in AES and e-commerce environments. Ali et al [15] utilized several built-in methods such as PBKDF2, RNG and slave key to generate shared keys. However, their protocol is complex and increases implementation running time costs.…”

Section: Results Comparison and Analysismentioning

confidence: 99%

“…Ali et al [15] presented a suggestion about the design of a relational database that covered the creation of a multi-tier secure architecture for integrating the dam management framework with its tasks. They employed the AES technique and generated keys to encrypt and decode the dam data via the password-based key derivation function 2 (PBKDF2), random numbers generator (RNG) sequences generator and the slave key for salting security.…”

Section: Related Work Of E-commerce Securitymentioning

confidence: 99%

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Utilizing Trusted Lightweight Ciphers to Support Electronic-Commerce Transaction Cryptography

Shyaa,

Al-Zubaidie

2023

Applied Sciences

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Electronic-commerce (e-commerce) has become a provider of distinctive services to individuals and companies due to the speed and flexibility of transferring orders and completing commercial deals across far and different places. However, due to the increasing attacks on penetrating transaction information or tampering with e-commerce requests, the interest in protecting this information and hiding it from tamperers has become extremely important. In addition, hacking these deals can cause a huge waste of money and resources. Moreover, large numbers of connected and disconnected networks can cause significant disruption to the built-in security measures. In this paper, we propose to design a protocol to protect transaction information based on ElGamal, advanced encryption standard (AES) and Chinese remainder theorem (CRT) techniques. In addition, our protocol ensures providing scalability with high-performance security measures. We combine these algorithms with a robust methodology that supports the balance of performance and security of the proposed protocol. An analysis of our results proves that our protocol is superior to existing security protocols.

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Section: Results Comparison and Analysismentioning

confidence: 99%

Section: Related Work Of E-commerce Securitymentioning

confidence: 99%

Utilizing Trusted Lightweight Ciphers to Support Electronic-Commerce Transaction Cryptography

Shyaa,

Al-Zubaidie

2023

Applied Sciences

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“…If you need to achieve a higher level of security, you can use a CPU-consuming hash algorithm to combat brute force cracking, such as password-based key derivation function 2 (PBKDF2). Ali et al [ 121 ] discussed encryption and decryption of the dam data using the AES algorithm with derived keys via the PBKDF2 and RNG sequences generator and slave key for salting protection. They propose a derived key based on the AES algorithm plus 256 bits.…”

Section: Applicationsmentioning

confidence: 99%

Entropy Sources Based on Silicon Chips: True Random Number Generator and Physical Unclonable Function

Cao

Liu

Lan

et al. 2022

Entropy

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Entropy is a measure of uncertainty or randomness. It is the foundation for almost all cryptographic systems. True random number generators (TRNGs) and physical unclonable functions (PUFs) are the silicon primitives to respectively harvest dynamic and static entropy to generate random bit streams. In this survey paper, we present a systematic and comprehensive review of different state-of-the-art methods to harvest entropy from silicon-based devices, including the implementations, applications, and the security of the designs. Furthermore, we conclude the trends of the entropy source design to point out the current spots of entropy harvesting.

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“…Due to an oversized variety of released android applications alongside the daily emergence of malicious applications, the manual investigation, and analysis of the latest apps became a cumbersome task. An important task in exploring malware streams is clustering 7,8 . In addition to the limited memory and other constraints, the nature of the malware stream includes the following points for clustering: 9,10 There is no hypothesis regarding the total cluster's numbers. No commitment exist to discover demanded shape clusters. There is no assumption about outliers data. …”

Section: Introductionmentioning

confidence: 99%

“…An important task in exploring malware streams is clustering. 7,8 In addition to the limited memory and other constraints, the nature of the malware stream includes the following points for clustering: 9,10 1. There is no hypothesis regarding the total cluster's numbers.…”

Section: Introductionmentioning

confidence: 99%

RAPSAMS: Robust affinity propagation clustering on static android malware stream

Katebi

Rezakhani

Joudaki

et al. 2022

Concurrency and Computation

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This article proposed RAPSAMS, extending affinity propagation (AP) clustering to be robust in malware steaming. We use AP, which has been suggested as an approach for clustering a set of samples that by passing messages in different clusters, represents malware stream clustering. Then, by generating and adding adversarial examples, a method has been proposed to attack this clustering algorithm and try to make a robust algorithm against the proposed attack. Malware clustering has become an active research area in mobile security, in which the knowledge is being exploited in large quantities of the generated malware stream. Different procedures have been used that apply clustering to the malware's static features. This article focuses on selecting the most appropriate examples as centers for Android malware clusters and tries to add some perturbation to fool the clustering algorithm. There are two important challenges in this regard: (i) How to find the best representations for clustering.(ii) How to manage extracted patterns that include important data flow characteristics with different distributions. Malware stream clustering poses many challenges to solve this problem, including dealing with malware that is imported online, being able to process malware quickly and incrementally, dealing appropriately with time constraints, and the way that can manage important features patterns of malware stream.To examine the adaptability of the proposed methods of defense and attack, analyses were performed. The proposed approaches are tested on a couple of malware Android dataset benchmarks, namely, Contagio, Genome, and Drebin. We use permission, API, and intent features of these datasets. The obtained results from false positive rate (FPR) recognize that the amounts of the known algorithm for clustering, AP, increases to more than 50% afterward an offense occurs in a number of instances (like: Genome dataset in permission and intent features). Furthermore, by applying the label flipping attack (LFA) the accuracy measures decrease lower than 85% in all instances. Also, the proposed defense method decreased the FPR value by less than 30%, and the accuracy increased by more than 91%, in all cases. Consequently, the AP clustering will be robust against LFAs.

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Data security using random dynamic salting and AES based on master-slave keys for Iraqi dam management system

Cited by 7 publications

References 27 publications

Utilizing Trusted Lightweight Ciphers to Support Electronic-Commerce Transaction Cryptography

Utilizing Trusted Lightweight Ciphers to Support Electronic-Commerce Transaction Cryptography

Entropy Sources Based on Silicon Chips: True Random Number Generator and Physical Unclonable Function

RAPSAMS: Robust affinity propagation clustering on static android malware stream

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