Investigation of Strength and Security of Pseudo Random Number Generators

Sathya, K.; Premalatha, J.; Rajasekar, Vani

doi:10.1088/1757-899x/1055/1/012076

Cited by 12 publications

(5 citation statements)

References 78 publications

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“…In the designed PoC, the KDF, Secure Random Generator (SRG) [45][46][47], and HMAC are assumed to be secure, and the channel's password and salt are assumed to be previously exchanged in a secure manner, preferably in-person, and generated using a SRG. Moreover, it is also assumed that the UUID is securely generated and that the used images are either chosen randomly from online galleries or from the user's gallery accordingly to user preference.…”

Section: Discussionmentioning

confidence: 99%

Exploiting Online Services to Enable Anonymous and Confidential Messaging

Sousa

Pinto

2022

JCP

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Messaging services are usually provided within social network platforms and allow these platforms to collect additional information about users, such as what time, for how long, with whom, and where a user communicates. This information allows the identification of users and is available to the messaging service provider even when communication is encrypted end-to-end. Thus, a gap still exists for alternative messaging services that enable anonymous and confidential communication and that are independent of a specific online service. Online services can still be used to support this messaging service, but in a way that enables users to communicate anonymously and without the knowledge and scrutiny of the online services. In this paper, we propose messaging using steganography and online services to support anonymous and confidential communication. In the proposed messaging service, only the sender and the receiver are aware of the existence of the exchanged data, even if the online services used or other third parties have access to the exchanged secret data containers. This work reviews the viability of using existing online services to support the proposed messaging service. Moreover, a proof-of-concept of the proposed message service is implemented and tested using two online services acting as proxies in the exchange of encrypted information disguised within images and links to those images. The obtained results confirm the viability of such a messaging service.

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Section: Discussionmentioning

confidence: 99%

Exploiting Online Services to Enable Anonymous and Confidential Messaging

Sousa

Pinto

2022

JCP

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“…[ 437,438 ] Traditional approaches create seed‐dependent pseudo‐random numbers using software and hardware strategies. [ 439,440 ] A physical entropy source, e.g., variability or noise in memory elements, is required to construct a true random number generator (TRNG). Additionally, for many domains, e.g., deep learning, machine learning, stochastic computing, and data encryption, random number generation using the MM system is important.…”

Section: Mm‐based In‐memory Computingmentioning

confidence: 99%

Nonvolatile Memristive Materials and Physical Modeling for In‐Memory and In‐Sensor Computing

Go,

Lim,

Lee

et al. 2024

Small Science

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Separate memory and processing units are utilized in conventional von Neumann computational architectures. However, regarding the energy and the time, it is costly to shuffle data between the memory and the processing entity, and for data‐intensive applications associated with artificial intelligence, the demand is ever increasing. A paradigm shift in traditional architectures is required, and in‐memory computing is one of the non‐von‐Neumann computing strategies. By harnessing physical signatures of the memory, computing workloads are administered in the same memory element. For in‐memory computing, a wide range of memristive material (MM) systems have been examined. Moreover, developing computing schemes that perform in the same sensory network and that minimize the data shuffle between the processing unit and the sensing element is a requirement, to process large volumes of data efficiently and decrease the energy consumption. In this review, an overview of the switching character and system signature harnessed in three archetypal MM systems is rendered, along with an integrated application survey for developing in‐sensor and in‐memory computing, viz., brain‐inspired or analogue computing, physical unclonable functions, and random number generators. The recent progress in theoretical studies that reveal the structural origin of the fast‐switching ability of the MM system is further summarized.

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“…When a seed value is provided to a PRNG, it uses this value as the initial state, the PRNG applies a mathematical operation to this seed value to produce a new number, which then becomes the input for the next iteration and so on. Seeding a PRNG with a specific number makes its output reproducible [15], if not seeded the PRNG will usually use a value derived from the system clock called timestamp as a seed, (see Fig. 3).…”

Section: B Seed-based Synchronization and Prng Phasementioning

confidence: 99%

Secure IoT Seed-based Matrix Key Generator

AINE,

LEGHRIS

2024

IJACSA

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The rapid evolution of the Internet of Things (IoT) has significantly transformed various aspects of both personal and professional spheres, offering innovative solutions in fields from home automation to industrial manufacturing. This progression is driven by the integration of physical devices with digital networks, facilitating efficient communication and data processing. However, such advancements bring forth critical security challenges, especially regarding data privacy and network integrity. Conventional cryptographic methods often fall short in addressing the unique requirements of IoT environments, such as limited device computational power and the need for efficient energy consumption. This paper introduces a novel approach to IoT security, inspired by the principles of steganographythe art of concealing information within other non-secret data. This method enhances security by embedding secret information within the payload or communication protocols, aligning with the low-power and minimal processing capabilities of IoT devices. We propose a steganographic key generation algorithm, adapted from the Diffie-Hellman key exchange model, tailored for IoT. This approach eliminates the need for explicit parameter exchange, thereby reducing vulnerability to key interception and unauthorized access, prevalent in IoT networks. The algorithm utilizes a pre-shared 2D matrix and a synchronized seed-based approach for covert communication without explicit data exchange. Furthermore, we have rigorously tested our algorithm using the NIST Statistical Test Suite (STS), comparing its execution time with other algorithms. The results underscore our algorithm's superior performance and suitability for IoT applications, highlighting its potential to secure IoT networks effectively without compromising on efficiency and device resource constraints. This paper presents the design, implementation, and potential implications of this algorithm for enhancing IoT security, ensuring the full realization of IoT benefits without compromising user security and privacy.

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Investigation of Strength and Security of Pseudo Random Number Generators

Cited by 12 publications

References 78 publications

Exploiting Online Services to Enable Anonymous and Confidential Messaging

Exploiting Online Services to Enable Anonymous and Confidential Messaging

Nonvolatile Memristive Materials and Physical Modeling for In‐Memory and In‐Sensor Computing

Secure IoT Seed-based Matrix Key Generator

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