Big Data and Cyber Security: Emerging Approaches and Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (20 November 2024) | Viewed by 3400

Special Issue Editors


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Guest Editor
Department of Computer Science, Slippery Rock University, Slippery Rock, PA 16057, USA
Interests: high-performance computing; big data; cybersecurity; performance; resilience

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Guest Editor
Department of Computer Science and Information Engineering (CSIE), Providence University, Taichung 43301, Taiwan
Interests: parallel and distributed processing; big data; emerging technologies
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Special Issue Information

Dear Colleagues,

With accelerated digitization, the amount of data being generated from various environments is rapidly increasing. With this rapid increase in the generation of diverse data, which exceeds a quintillion bytes per day, there is an extreme need for innovative technological advances to ensure their security. Alternatively, the power and knowledge behind the emergent solutions employed to secure data generated in business and academic environments reduce the time to response, facilitating the mitigation of and defense against existing and evolving cybersecurity threats.

This Special Issue focuses on advanced cybersecurity approaches and applications that address threats to the confidentiality, integrity, and availability of big data; it also discusses pioneering and emergent solutions that utilize the power of the meteoric rise in big data to analyze and resolve unprecedented security and privacy challenges and malicious threats in diverse environments.

We encourage original submissions from academia and industry on the most recent theoretical and applied advances, with a focus on how big data research fits broader cybersecurity objectives, and vice versa.

Topics of interest may include:

  • Security research and threat intelligence;
  • Real-time compliance;
  • Security monitoring;
  • Secure data storage;
  • Data privacy;
  • Big data cryptography;
  • Access control and auditing;
  • Intrusion and anomaly detection;
  • Spamming and spoofing detection;
  • Malware and ransomware detection;
  • Software code security;
  • Frameworks and architectures for big data security;
  • Cloud security;
  • Artificial intelligence (AI) security, machine learning, and big data analytics for cybersecurity.

Technical Committee Member:

Name: Elizabeth Bautista
Email: [email protected]
Homepage: https://www.nersc.gov/about/nersc-staff/operations-technology/elizabeth-bautista/
Affiliation: Lawrence Berkeley National Laboratory
Interests: HPC; monitoring; cybersecurity; big data

Dr. Nitin Sukhija
Prof. Dr. Kuan-Ching Li
Guest Editors

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Keywords

  • cybersecurity
  • big data
  • data science
  • AI
  • machine learning
  • data analytics
  • monitoring
  • privacy

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Published Papers (1 paper)

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Research

21 pages, 1201 KiB  
Article
Parallel Implementation of Lightweight Secure Hash Algorithm on CPU and GPU Environments
by Hojin Choi, SeongJun Choi and SeogChung Seo
Electronics 2024, 13(5), 896; https://doi.org/10.3390/electronics13050896 - 26 Feb 2024
Viewed by 2781
Abstract
Currently, cryptographic hash functions are widely used in various applications, including message authentication codes, cryptographic random generators, digital signatures, key derivation functions, and post-quantum algorithms. Notably, they play a vital role in establishing secure communication between servers and clients. Specifically, servers often need [...] Read more.
Currently, cryptographic hash functions are widely used in various applications, including message authentication codes, cryptographic random generators, digital signatures, key derivation functions, and post-quantum algorithms. Notably, they play a vital role in establishing secure communication between servers and clients. Specifically, servers often need to compute a large number of hash functions simultaneously to provide smooth services to connected clients. In this paper, we present highly optimized parallel implementations of Lightweight Secure Hash (LSH), a hash algorithm developed in Korea, on server sides. To optimize LSH performance, we leverage two parallel architectures: AVX-512 on high-end CPUs and NVIDIA GPUs. In essence, we introduce a word-level parallel processing design suitable for AVX-512 instruction sets and a data parallel processing design appropriate for the NVIDIA CUDA platform. In the former approach, we parallelize the core functions of LSH using AVX-512 registers and instructions. As a result, our first implementation achieves a performance improvement of up to 50.37% compared to the latest LSH AVX-2 implementation. In the latter approach, we optimize the core operation of LSH with CUDA PTX assembly and apply a coalesced memory access pattern. Furthermore, we determine the optimal number of blocks/threads configuration and CUDA streams for RTX 2080Ti and RTX 3090. Consequently, in the RTX 3090 architecture, our optimized CUDA implementation achieves about a 180.62% performance improvement compared with the initially ported LSH implementation to the CUDA platform. As far as we know, this is the first work on optimizing LSH with AVX-512 and NVIDIA GPU. The proposed implementation methodologies can be used alone or together in a server environment to achieve the maximum throughput of LSH computation. Full article
(This article belongs to the Special Issue Big Data and Cyber Security: Emerging Approaches and Applications)
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