Information Security and Cyber Intelligence

A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).

Deadline for manuscript submissions: closed (27 October 2022) | Viewed by 4773

Special Issue Editor


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Guest Editor
College of Engineering and Computing, University of South Carolina, Columbia, SC 29208, USA
Interests: information security; data inference problem; secure data provenance; semantics-based security

Special Issue Information

Dear Colleagues,

It is our pleasure to announce a new Special Issue “Information Security and Cyber Intelligence” in the journal Big Data and Cognitive Computing. Around the globe, information security has been one of the most critical issues for people’s daily life. It is urgent for us to have a secure and intelligent cyber world, considering the threats we are facing.

The Special Issue invites contributions, including, but not limited to, the following topics:

  • Security models and techniques;
  • Adversarial modeling in machine learning;
  • Applied cryptography;
  • Blockchain security;
  • Data and metadata protection;
  • Data poisoning;
  • Ethical, legal, and business implications of cyber attacks;
  • Insider threat;
  • Personal data protection for information systems;
  • Privacy enhancing technologies;
  • Reliability, dependability, and reproducibility;
  • Risk assessment;
  • Secure software development methodologies;
  • Security and privacy for big data;
  • Security issues in high performance systems;
  • Smart contracts in intelligent control systems.

Prof. Dr. Csilla Farkas
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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Keywords

  • security
  • privacy
  • anonymity
  • adversarial machine learning
  • risk assessment
  • security models and techniques

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Published Papers (1 paper)

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Research

21 pages, 1020 KiB  
Article
Hardening the Security of Multi-Access Edge Computing through Bio-Inspired VM Introspection
by Huseyn Huseynov, Tarek Saadawi and Kenichi Kourai
Big Data Cogn. Comput. 2021, 5(4), 52; https://doi.org/10.3390/bdcc5040052 - 8 Oct 2021
Cited by 6 | Viewed by 3856
Abstract
The extreme bandwidth and performance of 5G mobile networks changes the way we develop and utilize digital services. Within a few years, 5G will not only touch technology and applications, but dramatically change the economy, our society and individual life. One of the [...] Read more.
The extreme bandwidth and performance of 5G mobile networks changes the way we develop and utilize digital services. Within a few years, 5G will not only touch technology and applications, but dramatically change the economy, our society and individual life. One of the emerging technologies that enables the evolution to 5G by bringing cloud capabilities near to the end users is Edge Computing or also known as Multi-Access Edge Computing (MEC) that will become pertinent towards the evolution of 5G. This evolution also entails growth in the threat landscape and increase privacy in concerns at different application areas, hence security and privacy plays a central role in the evolution towards 5G. Since MEC application instantiated in the virtualized infrastructure, in this paper we present a distributed application that aims to constantly introspect multiple virtual machines (VMs) in order to detect malicious activities based on their anomalous behavior. Once suspicious processes detected, our IDS in real-time notifies system administrator about the potential threat. Developed software is able to detect keyloggers, rootkits, trojans, process hiding and other intrusion artifacts via agent-less operation, by operating remotely or directly from the host machine. Remote memory introspection means no software to install, no notice to malware to evacuate or destroy data. Experimental results of remote VMI on more than 50 different malicious code demonstrate average anomaly detection rate close to 97%. We have established wide testbed environment connecting networks of two universities Kyushu Institute of Technology and The City College of New York through secure GRE tunnel. Conducted experiments on this testbed deliver high response time of the proposed system. Full article
(This article belongs to the Special Issue Information Security and Cyber Intelligence)
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