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Techniques and Frameworks to Detect and Mitigate Insider Attacks
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Techniques and Frameworks to Detect and Mitigate Insider Attacks

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Security and Privacy".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 6490

Special Issue Editors

Dr. Santosh Aditham

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Guest Editor
Sr Machine Learning Scientist, Apple, Cupertino, CA, USA
Interests: security; privacy; machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Sokratis Katsikas

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Guest Editor
Department of Information Security and Communication Technology, Norwegian University of Science and Technology, 2815 Gjøvik, Norway
Interests: information and cybersecurity
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Traditionally, the word “security” in the technology industry was synonymous with addressing attacks that originate externally. However, we are noticing a rise in concerns about attacks that originate internally. Such attacks are known as insider attacks. There is a need to address this problem of growing security attacks from insiders. As a result, it is our goal to explore the state-of-the-art research dealing with new surveys, policies, tools, techniques, concepts, and applications concerning the detection, response and recovery, mitigation, and prevention of insider attacks.

Topics of interest include, but are not limited to:

  • Insider attack modeling and attack vectors;
  • Implications of insider attacks;
  • Policies and regulations to prevent insider attacks;
  • Authentication and authorization techniques to address insider attacks;
  • Behavioral analytics and fraud detection;
  • Data governance and differential privacy to mitigate data leaks;
  • Insider attack recovery mechanisms;
  • Insider attack datasets;
  • Applications of machine learning to detect and prevent insider

Dr. Santosh Aditham
Prof. Dr. Sokratis Katsikas
Guest Editors

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.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • attack vectors
  • security
  • intrusion detection
  • fraud detection
  • machine learning

Published Papers (2 papers)

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Research

19 pages, 9301 KiB  
Article
An Adversarial Attack Method against Specified Objects Based on Instance Segmentation
by Dapeng Lang, Deyun Chen, Sizhao Li and Yongjun He
Information 2022, 13(10), 465; https://doi.org/10.3390/info13100465 - 29 Sep 2022
Cited by 1 | Viewed by 1779
Abstract
The deep model is widely used and has been demonstrated to have more hidden security risks. An adversarial attack can bypass the traditional means of defense. By modifying the input data, the attack on the deep model is realized, and it is imperceptible [...] Read more.
The deep model is widely used and has been demonstrated to have more hidden security risks. An adversarial attack can bypass the traditional means of defense. By modifying the input data, the attack on the deep model is realized, and it is imperceptible to humans. The existing adversarial example generation methods mainly attack the whole image. The optimization iterative direction is easy to predict, and the attack flexibility is low. For more complex scenarios, this paper proposes an edge-restricted adversarial example generation algorithm (Re-AEG) based on semantic segmentation. The algorithm can attack one or more specific objects in the image so that the detector cannot detect the objects. First, the algorithm automatically locates the attack objects according to the application requirements. Through the semantic segmentation algorithm, the attacked object is separated and the mask matrix for the object is generated. The algorithm proposed in this paper can attack the object in the region, converge quickly and successfully deceive the deep detection model. The algorithm only hides some sensitive objects in the image, rather than completely invalidating the detection model and causing reported errors, so it has higher concealment than the previous adversarial example generation algorithms. In this paper, a comparative experiment is carried out on ImageNet and coco2017 datasets, and the attack success rate is higher than 92%. Full article
(This article belongs to the Special Issue Techniques and Frameworks to Detect and Mitigate Insider Attacks)
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17 pages, 969 KiB  
Article
Analysis of Insider Threats in the Healthcare Industry: A Text Mining Approach
by In Lee
Information 2022, 13(9), 404; https://doi.org/10.3390/info13090404 - 27 Aug 2022
Cited by 4 | Viewed by 3810
Abstract
To address rapidly growing data breach incidents effectively, healthcare providers need to identify various insider and outsider threats, analyze the vulnerabilities of their internal security systems, and develop more appropriate data security measures against the threats. While there have been studies on trends [...] Read more.
To address rapidly growing data breach incidents effectively, healthcare providers need to identify various insider and outsider threats, analyze the vulnerabilities of their internal security systems, and develop more appropriate data security measures against the threats. While there have been studies on trends of data breach incidents, there is a lack of research on the analysis of descriptive contents posted on the data breach reporting website of the U.S. Department of Health and Human Services (HHS) Office for Civil Rights (OCR). Hence, this study develops a novel approach to the analysis of descriptive data breach information with the use of text mining and visualization. Insider threats, vulnerabilities, breach incidents, impacts, and responses to the breaches are analyzed for three data breach types. Full article
(This article belongs to the Special Issue Techniques and Frameworks to Detect and Mitigate Insider Attacks)
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