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Symmetry
Special Issues
Sustainable Security Solutions for Mobile Applications with Symmetry
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Sustainable Security Solutions for Mobile Applications with Symmetry

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 2462

Special Issue Editors

Dr. Haipeng Cai

E-Mail Website
Guest Editor
School of Electrical Engineering and Computer Science (EECS), Washington State University, Pullman, WA 99164, USA
Interests: machine learning for software engineering, software security; program analysis; software evolution
Dr. Jia-Ning Luo

E-Mail Website
Co-Guest Editor
Department of Computer Science and Information Engineering, Chung Cheng Institute of Technology, National Defense University, Taoyuan 335009, Taiwan
Interests: network security; cryptographic protocols; wireless and mobile networks

Special Issue Information

Dear Colleagues,

With mobile software applications increasingly affecting our lives and societies, their security and privacy issues have received growing attention in the research community, particularly techniques utilizing data-driven, notably machine/deep learning, methods having demonstrated tremendous potential in recent years, a major challenge being that such solutions tend to not be sustainable because of the moving defense targets; that is, a technique devised to work effectively for one population of mobile applications developed in a certain period of time may not work well for applications coming out later in time, because of the constant evolution of the population. This Special Issue is dedicated to exploring and discovering more sustainable novel solutions so that we can eliminate the need to repeatedly and frequently develop/update techniques for newer mobile application populations; in fact, even if it is affordable to do so, we may not have newer samples available for re-training/updating the previously trained learning models. The topic pursues a synergy between sustainable mobile software security and robust data-driven (especially AI/ML) models, where the symmetry concept is reflected between sustainability in the former and robustness in the latter, this Special Issue collecting papers that highlight the recent advances and broad research efforts tackling the challenge of technique deterioration, the lack of training samples that represent new/emerging software applications, and the subsequent proliferation of zero-day security breaches.

Dr. Haipeng Cai
Dr. Jia-Ning Luo
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. Symmetry 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 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

  • data-driven technique
  • mobile security
  • sustainability
  • data evolution
  • deterioration
  • robustness
  • adversarial samples

Published Papers (1 paper)

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Research

27 pages, 1705 KiB  
Article
Early Detection of Abnormal Attacks in Software-Defined Networking Using Machine Learning Approaches
by Hsiu-Min Chuang, Fanpyn Liu and Chung-Hsien Tsai
Symmetry 2022, 14(6), 1178; https://doi.org/10.3390/sym14061178 - 08 Jun 2022
Cited by 9 | Viewed by 2453
Abstract
Recent developments have made software-defined networking (SDN) a popular technology for solving the inherent problems of conventional distributed networks. The key benefit of SDN is the decoupling between the control plane and the data plane, which makes the network more flexible and easier [...] Read more.
Recent developments have made software-defined networking (SDN) a popular technology for solving the inherent problems of conventional distributed networks. The key benefit of SDN is the decoupling between the control plane and the data plane, which makes the network more flexible and easier to manage. SDN is a new generation network architecture; however, its configuration settings are centralized, making it vulnerable to hackers. Our study investigated the feasibility of applying artificial intelligence technology to detect abnormal attacks in an SDN environment based on the current unit network architecture; therefore, the concept of symmetry includes the sustainability of SDN applications and robust performance of machine learning (ML) models in the case of various malicious attacks. In this study, we focus on the early detection of abnormal attacks in an SDN environment. On detection of malicious traffic in SDN topology, the AI module in the topology is applied to detect and act against the attack source through machine learning algorithms, making the network architecture more flexible. Under multiple abnormal attacks, we propose a hierarchical multi-class (HMC) architecture to effectively address the imbalanced dataset problem and improve the performance of minority classes. The experimental results show that the decision tree, random forest, bagging, AdaBoost, and deep learning models exhibit the best performance for distributed denial-of-service (DDoS) attacks. In addition, for the imbalanced dataset problem of multiclass classification, our proposed HMC architecture performs better than previous single classifiers. We also simulated the SDN topology and scenario verification. In summary, we concatenated the AI module to enhance the security and effectiveness of SDN networks in a practical manner. Full article
(This article belongs to the Special Issue Sustainable Security Solutions for Mobile Applications with Symmetry)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Performance Evaluation of Feature Selection for Static Malware Analysis
Authors: Hsiu-Min Chuang; Hsing-Chung Chen; Tian-Jan Chian; Chung-Hsien Tsai; Chao-Lung Chou
Affiliation: Department of Computer Science and Information Engineering, Chung Cheng Institute of Technology, National Defense University, Taoyuan City 335, Taiwan

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