Artificial Intelligence in Cyberspace Security

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: 31 July 2024 | Viewed by 1797

Special Issue Editors


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Guest Editor
School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China
Interests: AI security; data security; software protection
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100081, China
Interests: network security; secure data sharing; AI security
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Information Management, Beijing Information Science and Technology University, Beijing 100192, China
Interests: data security; secure data sharing; steganography

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Guest Editor
School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Interests: network security; blockchain security; cryptographic protocol

Special Issue Information

Dear Colleagues,

AI (Artificial Intelligence) has made progress in many scientific and technological fields, such as unmanned system, pattern recognition, natural language processes, expert systems, etc.  Many researchers insist that the technological singularity may soon occur when machines may acquire intelligence similar to or exceeding that of humans. While many researchers believe that a technological singularity is an arm's length away, many argue against the same possibility due to a lack of concrete evidence. The rapid development of AI not only facilitates people's daily life and work, but also introduces new cyberspace security challenges. AI security has become a hot topic and has attracted wide attention from scholars.

Security issues keep emerging since AI technology was introduced into cyberspace. On the one hand, the explosive growth of data has made it impractical to manually manage network data security, and the threat of new and rapidly iterative network attack methods are becoming increasingly common. On the other hand, existing network attacks have gradually adapted to various existing defense technologies in terms of transmission, infection, and evasion, and are constantly iteratively emerging new variants, making them increasingly difficult to detect and predict.To ensure the security of new information technologies in scenarios such as smart lives, smart cities, smart networks, etc., and to promote and enhance the development of network security, we have organized a Special Issue topic on "Artificial Intelligence in Cyberspace Security". In this Special Issue, the new generation of network attack and defense technology, new secure cryptographic algorithms, data security and privacy protection technology, network and communication security protocols, security analyses, and the evaluation of new application scenarios are discussed. We call for papers in this Special Issue to provide a platform to discuss, exchange insights, and share experiences among researchers, industry specialists, and application developers.

Authors are invited to submit original research on both theoretical and practical aspects of security and privacy in networks. We especially welcome submissions that present implementation and deployment results. Topics of interest for submission include, but are not limited to:

  • AI for cyberspace security and safety;
  • AI-driven communication network security and privacy protection;
  • adversarial machine learning;
  • applications of AI for cyberspace security and safety;
  • attack and defense methods with adversarial examples;
  • big data security;
  • cyber physical systems security;
  • cyberspace security and safety for IoT;
  • database security;
  • digital forensics;
  • explainable machine learning for cyberspace security and safety;
  • firmware security;
  • human machine intelligence for cyberspace security and safety;
  • malware and botnet;
  • network-intrusion detection and safety;
  • operation system security;
  • privacy and data protection;
  • public-key techniques in MPC or other protocols;
  • secure AI modeling and architecture;
  • secure and resilient communication and control architecture;
  • secure data provenance;
  • secure data sharing, digital signature, and multi-party secure computing;
  • security issues of federated learning;
  • security protocols for AI;
  • self-healing for cyberspace security and safety;
  • social networking security and privacy;
  • software security;
  • trust computing;
  • trust management and safety;
  • vulnerability and risk assessment;
  • Web security

Dr. Yuanzhang Li
Prof. Dr. Yu-an Tan
Dr. Chen Liang
Dr. Hongfei Zhu
Guest Editors

Manuscript Submission Information

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Keywords

  • AI-driven network security
  • AI-driven privacy protection
  • adversarial machine learning
  • cyberspace security
  • malware detection
  • secure communication
  • secure data sharing
  • security protocol
  • software security
  • vulnerability

Published Papers (1 paper)

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Research

22 pages, 12903 KiB  
Article
Delving Deep into Reverse Engineering of UEFI Firmwares via Human Interface Infrastructure
by Siyi Chen, Yu-An Tan, Kefan Qiu, Zheng Zhang, Yuanzhang Li and Quanxin Zhang
Electronics 2023, 12(22), 4601; https://doi.org/10.3390/electronics12224601 - 10 Nov 2023
Viewed by 1476
Abstract
The Unified Extensible Firmware Interface (UEFI) provides a specification of the software interface between an OS and its underlying platform firmware. UEFI UI is an interactive interface that allows users to configure and manage UEFI settings, which is closely related to HII (Human [...] Read more.
The Unified Extensible Firmware Interface (UEFI) provides a specification of the software interface between an OS and its underlying platform firmware. UEFI UI is an interactive interface that allows users to configure and manage UEFI settings, which is closely related to HII (Human Interface Infrastructure). In practice, HII provides a mechanism that allows developers to create UI elements with HII-related protocols. In this paper, we provide a comprehensive analysis of the UEFI combined with a case study. We proposed a protocol-centered static analysis method to obtain UEFI’s password policy, using HII-related protocols to find password implementation. Existing static analyses are ineffective in detecting such password policy in stripped UEFI firmware images. By reverse-engineering the IFR (Internal Forms Representation) in HII, we located where much sensitive information is stored. Lastly, we studied hardware port configurations, using Secure Boot as a case in point. We analyzed how UEFI uses the HII protocol to set relevant information in the UEFI UI. This paper is the first to offer a reverse-engineering systematic analysis of exploring UEFI via HII, providing valuable insights into its structure and potential enhancements for firmware security. Full article
(This article belongs to the Special Issue Artificial Intelligence in Cyberspace Security)
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