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Applied Sciences
Special Issues
Novel Approaches for Software Security
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Novel Approaches for Software Security

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 May 2024 | Viewed by 1465

Special Issue Editor

Dr. Jun Lee

E-Mail Website
Guest Editor
Depaerment of AI Content Convergence, Hoseo University, Asan 336-795, Republic of Korea
Interests: HCI; sensor-based interactions; Metaverse; XR; collaborative works
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Software security is the practice of protecting information by mitigating information risks. In recent years, significant advancements have been made in security technology, enabling effective responses to security vulnerabilities and hacking attempts. The realm of software security is expanding at an unprecedented rate thanks to the advancements in various cutting-edge IT technologies, including AI and metaverse.

The objective of this Special Issue is to present research underlying novel software security methods useful in web, cloud computing, metaverse, Internet of Things and, in the current scenario, challenges and perspectives in computer science.

Dr. Jun Lee
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.

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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

  • AI Security
  • HCI security and privacy
  • blockchain security
  • automobile security
  • web security
  • cloud security
  • authentication and authorization
  • applications of security techniques
  • trustworthy computing

Published Papers (1 paper)

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20 pages, 2678 KiB  
Systematic Review
Systemic Literature Review of Recognition-Based Authentication Method Resistivity to Shoulder-Surfing Attacks
by Lateef Adekunle Adebimpe, Ian Ouii Ng, Mohd Yamani Idna Idris, Mohammed Okmi, Chin Soon Ku, Tan Fong Ang and Lip Yee Por
Appl. Sci. 2023, 13(18), 10040; https://doi.org/10.3390/app131810040 - 06 Sep 2023
Viewed by 1178
Abstract
The rapid advancement of information technology (IT) has given rise to a new era of efficient and fast communication and transactions. However, the increasing adoption of and reliance on IT has led to the exposure of personal and sensitive information online. Safeguarding this [...] Read more.
The rapid advancement of information technology (IT) has given rise to a new era of efficient and fast communication and transactions. However, the increasing adoption of and reliance on IT has led to the exposure of personal and sensitive information online. Safeguarding this information against unauthorized access remains a persistent challenge, necessitating the implementation of improved computer security measures. The core objective of computer security is to ensure the confidentiality, availability, and integrity of data and services. Among the mechanisms developed to counter security threats, authentication stands out as a pivotal defense strategy. Graphical passwords have emerged as a popular authentication approach, yet they face vulnerability to shoulder-surfing attacks, wherein an attacker can clandestinely observe a victim’s actions. Shoulder-surfing attacks present a significant security challenge within the realm of graphical password authentication. These attacks occur when an unauthorized individual covertly observes the authentication process of a legitimate user by shoulder surfing the user or capturing the interaction through a video recording. In response to this challenge, various methods have been proposed to thwart shoulder-surfing attacks, each with distinct advantages and limitations. This study thus centers on reviewing the resilience of existing recognition-based graphical password techniques against shoulder-surfing attacks by conducting a comprehensive examination and evaluation of their benefits, strengths, and weaknesses. The evaluation process entailed accessing pertinent academic resources through renowned search engines, including Web of Science, Science Direct, IEEE Xplore, ProQuest, Scopus, Springer, Wiley Online Library, and EBSCO. The selection criteria were carefully designed to prioritize studies that focused on recognition-based graphical password methods. Through this rigorous approach, 28 studies were identified and subjected to a thorough review. The results show that fourteen of them adopted registered objects as pass-objects, bolstering security through object recognition. Additionally, two methods employed decoy objects as pass-objects, enhancing obfuscation. Notably, one technique harnessed both registered and decoy objects, amplifying the security paradigm. The results also showed that recognition-based graphical password techniques varied in their resistance to different types of shoulder-surfing attacks. Some methods were effective in preventing direct observation attacks, while others were vulnerable to video-recorded and multiple-observation attacks. This vulnerability emerged due to attackers potentially extracting key information by analyzing user interaction patterns in each challenge set. Notably, one method stood out as an exception, demonstrating resilience against all three types of shoulder-surfing attacks. In conclusion, this study contributes to a comprehensive understanding of the efficacy of recognition-based graphical password methods in countering shoulder-surfing attacks by analyzing the diverse strategies employed by these methods and revealing their strengths and weaknesses. Full article
(This article belongs to the Special Issue Novel Approaches for Software Security)
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