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Cyber Security and Software Engineering
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Cyber Security and Software Engineering

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 September 2024 | Viewed by 1398

Special Issue Editors

Dr. Young Lee

E-Mail Website
Guest Editor
The Department of Computational, Engineering, and Mathematical Sciences, College of Arts and Sciences, Texas A&M University-San Antonio, 71007, San Antonio, TX 78224, USA
Interests: computer science; software engineering; software visualization; source; code analysis; software security
Dr. Jeong Yang

E-Mail Website
Guest Editor
The Department of Computational, Engineering, and Mathematical Sciences, College of Arts and Sciences, Texas A&M University-San Antonio, 71007, San Antonio, TX 78224, USA
Interests: secure software development; securing applications; intelligent applications with cloud-based AI services; AI-powered secure data sharing; software supply chain security; development & evaluation of cybersecurity modules
Prof. Dr. Luis Javier Garcia Villalba

E-Mail Website
Guest Editor
Group of Analysis, Security and Systems (GASS), Universidad Complutense de Madrid (UCM), 28040 Madrid, Spain
Interests: artificial intelligence; big data; computer networks; computer security; information theory; IoT; multimedia forensics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue of the esteemed journal Cyber Security and Software Engineering is dedicated to gathering and showcasing the most recent scholarly findings, research, and innovations surrounding the ever-evolving field of cybersecurity and privacy.

Cybersecurity and privacy are pivotal in the digital world. These concerns extend beyond software developers and electronics manufacturers to everyday digital device users. Since the advent of software applications, particularly those handling private and sensitive data, security attacks have become inevitable. In this Special Issue, we concentrate on developing secure software applications in various environments and preventing malicious activities in cyber computer systems.

We warmly invite all dedicated researchers, professionals, and experts in the field to share their invaluable insights, groundbreaking discoveries, and forward-thinking perspectives. Through this collaborative effort, we aim to significantly enrich the field's existing body of knowledge, inspire new directions in research, and ultimately contribute to the development of a safer, more secure digital world.

Dr. Young Lee
Dr. Jeong Yang
Prof. Dr. Luis Javier Garcia Villalba
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. 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

  • cybersecurity
  • secure software development
  • mobile application security
  • cloud security
  • security vulnerabilities
  • privacy and data security
  • artificial intelligence and machine learning in cyber security
  • IoT security
  • algorithm for data encryption
  • data protection
  • network security
  • internet safety
  • encryption
  • authentication
  • cyber threats
  • intrusion detection
  • security architecture

Published Papers (2 papers)

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Research

15 pages, 254 KiB  
Article
Improving VulRepair’s Perfect Prediction by Leveraging the LION Optimizer
by Brian Kishiyama, Young Lee and Jeong Yang
Appl. Sci. 2024, 14(13), 5750; https://doi.org/10.3390/app14135750 - 1 Jul 2024
Viewed by 612
Abstract
In current software applications, numerous vulnerabilities may be present. Attackers attempt to exploit these vulnerabilities, leading to security breaches, unauthorized entry, data theft, or the incapacitation of computer systems. Instead of addressing software or hardware vulnerabilities at a later stage, it is better [...] Read more.
In current software applications, numerous vulnerabilities may be present. Attackers attempt to exploit these vulnerabilities, leading to security breaches, unauthorized entry, data theft, or the incapacitation of computer systems. Instead of addressing software or hardware vulnerabilities at a later stage, it is better to address them immediately or during the development phase. Tools such as AIBugHunter provide solutions designed to tackle software issues by predicting, categorizing, and fixing coding vulnerabilities. Essentially, developers can see where their code is susceptible to attacks and obtain details about the nature and severity of these vulnerabilities. AIBugHunter incorporates VulRepair to detect and repair vulnerabilities. VulRepair currently predicts patches for vulnerable functions at 44%. To be truly effective, this number needs to be increased. This study examines VulRepair to see whether the 44% perfect prediction can be increased. VulRepair is based on T5 and uses both natural language and programming languages during its pretraining phase, along with byte pair encoding. T5 is a text-to-text transfer transformer model with an encoder and decoder as part of its neural network. It outperforms other models such as VRepair and CodeBERT. However, the hyperparameters may not be optimized due to the development of new optimizers. We reviewed a deep neural network (DNN) optimizer developed by Google in 2023. This optimizer, the Evolved Sign Momentum (LION), is available in PyTorch. We applied LION to VulRepair and tested its influence on the hyperparameters. After adjusting the hyperparameters, we obtained a 56% perfect prediction, which exceeds the value of the VulRepair report of 44%. This means that VulRepair can repair more vulnerabilities and avoid more attacks. As far as we know, our approach utilizing an alternative to AdamW, the standard optimizer, has not been previously applied to enhance VulRepair and similar models. Full article
(This article belongs to the Special Issue Cyber Security and Software Engineering)
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20 pages, 3138 KiB  
Article
A Framework to Quantify the Quality of Source Code Obfuscation
by Hongjoo Jin, Jiwon Lee, Sumin Yang, Kijoong Kim and Dong Hoon Lee
Appl. Sci. 2024, 14(12), 5056; https://doi.org/10.3390/app14125056 - 10 Jun 2024
Viewed by 401
Abstract
Malicious reverse engineering of software has served as a valuable technique for attackers to infringe upon and steal intellectual property. We can employ obfuscation techniques to protect against such attackers as useful tools to safeguard software. Applying obfuscation techniques to source code can [...] Read more.
Malicious reverse engineering of software has served as a valuable technique for attackers to infringe upon and steal intellectual property. We can employ obfuscation techniques to protect against such attackers as useful tools to safeguard software. Applying obfuscation techniques to source code can prevent malicious attackers from reverse engineering a program. However, the ambiguity surrounding the protective efficacy of these source code obfuscation tools and techniques presents challenges for users in evaluating and comparing the varying degrees of protection provided. This paper addresses these issues and presents a methodology to quantify the effect of source code obfuscation. Our proposed method is based on three main types of data: (1) the control flow graph, (2) the program path, and (3) the performance overhead added to the process—all of which are derived from a program analysis conducted by human experts and automated tools. For the first time, we have implemented a tool that can quantitatively evaluate the quality of obfuscation techniques. Then, to validate the effectiveness of the implemented framework, we conducted experiments using four widely recognized commercial and open-source obfuscation tools. Our experimental findings, based on quantitative values related to obfuscation techniques, demonstrate that our proposed framework effectively assesses obfuscation quality. Full article
(This article belongs to the Special Issue Cyber Security and Software Engineering)
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