A Lightweight Cryptography Technique with Random Pattern Generation

Thangamani, Narmadha; Meenakshi, M.

doi:10.1007/s11277-018-6092-8

Cited by 14 publications

(6 citation statements)

References 27 publications

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“…At present, Deep learning and machine learningbased techniques have become popular in network security disciplines with the advancement of artificial intelligence technologies. Just as defense technologies against device-based DDoS attacks, the main research direction is to detect the occurrence of DDoS attacks by using machine learning [104] or reinforcement learning [105]. However, these learning methods may overfit network traffic, so relevant researchers need to consider the real network scenarios and select appropriate machine learning algorithms to detect security threats in edge computing systems.…”

Section: Future Research Directionsmentioning

confidence: 99%

Review on Security Defense Technology Research in Edge Computing Environment

Shang,

He,

Zhang

2024

Chinese J. Elect.

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Edge computing, which achieves quick data processing by sinking data computing and storage to the network edge, has grown rapidly along with the Internet of things. The new network architecture of edge computing brings new security challenges. Based on this, this paper investigates the edge computing security literature published in recent years and summarizes and analyzes research work on edge computing security from different attack surfaces. We start with the definition and architecture of edge computing. From the attack surface between device and edge server, as well as on edge servers, the research describes the security threats and defense methods of edge computing. In addition, the cause of the attack and the pros and cons of defense methods is introduced. The challenges and future research directions of edge computing are given.

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Section: Future Research Directionsmentioning

confidence: 99%

Review on Security Defense Technology Research in Edge Computing Environment

Shang,

He,

Zhang

2024

Chinese J. Elect.

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“…Key size, block size, structure type, and the number of rounds is the primary considerations to evaluate a lightweight block cipher. In [58] writers recommend that a lightweight algorithm must focus on the three challenges such as a minimal memory, low power intake, sufficient security level. A Lightweight algorithm should have a small block size of 32-64 bits in comparison with the conventional 64 and 128 bits block size [59].…”

Section: Lightweight Block Ciphermentioning

confidence: 99%

“…PRINCE, QARMA, and modified QARMA have the lowest latency, which indicates they are suitable for resource constrained devices. However, according to the author [58], further modification is needed for QARMA algorithm. AFN, SAT_Jo, AES and Piccolo are some of the block ciphers with higher latency that makes them vulnerable to several IoT attacks.…”

Section: Number Of Roundsmentioning

confidence: 99%

Lightweight cryptography in IoT networks: A survey

Rana

Mamun

Islam

2022

Future Generation Computer Systems

113

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“…A random secret key generation scheme that integrates differential logical pattern method is presented in [43]. In particular, an input message is split into a number of blocks for pattern extraction.…”

Section: Application-dependent Secret Key Generationmentioning

confidence: 99%

Design and Implementation of Secret Key Agreement for Platoon-based Vehicular Cyber-physical Systems

Li¹,

Emami³

et al. 2019

ACM Trans. Cyber-Phys. Syst.

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In platoon-based vehicular cyber-physical system (PVCPS), a lead vehicle that is responsible for managing the platoon's moving directions and velocity periodically disseminates control messages to the vehicles that follow. Securing wireless transmissions of the messages between the vehicles is critical for privacy and confidentiality of platoon's driving pattern. However, due to the broadcast nature of radio channels, the transmissions are vulnerable to eavesdropping. In this paper, we propose a cooperative secret key agreement (CoopKey) scheme for encrypting/decrypting the control messages, where the vehicles in PVCPS generate a unified secret key based on the quantized fading channel randomness. Channel quantization intervals are optimized by dynamic programming to minimize the mismatch of keys. A platooning testbed is built with autonomous robotic vehicles, where a TelosB wireless node is used for onboard data processing and multi-hop dissemination. Extensive real-world experiments demonstrate that CoopKey achieves significantly low secret bit mismatch rate in a variety of settings. Moreover, the standard NIST test suite is employed to verify randomness of the generated keys, where the p-values of our CoopKey pass all the randomness tests. We also evaluate CoopKey with an extended platoon size via simulations to investigate the effect of system scalability on performance.

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A Lightweight Cryptography Technique with Random Pattern Generation

Cited by 14 publications

References 27 publications

Review on Security Defense Technology Research in Edge Computing Environment

Review on Security Defense Technology Research in Edge Computing Environment

Lightweight cryptography in IoT networks: A survey

Design and Implementation of Secret Key Agreement for Platoon-based Vehicular Cyber-physical Systems

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