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Applied Sciences
Special Issues
State-of-the-Art of Network Attack Detection and Situation Awareness Analysis
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State-of-the-Art of Network Attack Detection and Situation Awareness Analysis

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 August 2024 | Viewed by 2544

Special Issue Editors

Dr. Zhaoquan Gu

E-Mail Website
Guest Editor
School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China
Interests: artificial intelligence security; cyber attack and defense; situation awareness analysis; big data analysis; intelligent connected vehicle; knowledge graph
Special Issues, Collections and Topics in MDPI journals
Dr. Cuiyun Gao

E-Mail Website
Guest Editor
Department of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, China
Interests: vulnerability detection; intelligent software engineering
Prof. Dr. Aiping Li

E-Mail
Guest Editor
School of Computer, National University of Defense Technology, Changsha 410073, China
Interests: big data; data mining; spatial databases; cyberspace security
Prof. Dr. Yong Ding

E-Mail Website
Guest Editor
Guangxi Key Laboratory of Cryptography and Information Security, School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541010, China
Interests: information security; big data; internet-of-things; blockchain; cryptography

Special Issue Information

Dear Colleagues,

The Special Issue aims to showcase the latest advancements in the field of network attack detection and situation awareness analysis. The information revolution has changed the way that we communicate throughout the world, and has drawn unprecedented attention to network security issues. The Special Issue seeks to explore innovative techniques, methodologies, and tools that enhance our ability to detect, analyze, and respond to network attacks effectively.

Authors are invited to contribute original research papers and conceptual articles addressing various aspects of network attack detection and situation awareness analysis for the comprehensive evaluation of various elements in the time and space environment of the overall network security. This may include topics such as intrusion detection systems, anomaly detection algorithms, AI-driven approaches, data visualization techniques, threat intelligence integration, and real-time monitoring solutions.

In this Special Issue, we welcome the submission of articles that explore cutting-edge research and recent advances in the field of network attack detection. Both theoretical and experimental studies are welcome, as well as comprehensive review and survey papers.

Prof. Dr. Zhaoquan Gu
Dr. Cuiyun Gao
Prof. Dr. Aiping Li
Prof. Dr. Yong Ding
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

  • network attack detection
  • situation awareness analysis
  • anomaly detection
  • intrusion detection systems
  • cyber threat analysis
  • network forensics
  • in-vehicle network security
  • cyber adversarial attacks and defenses
  • explainable artificial intelligence for network security

Published Papers (3 papers)

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Research

23 pages, 4029 KiB  
Article
Research on the Simulation Method of HTTP Traffic Based on GAN
by Chenglin Yang, Dongliang Xu and Xiao Ma
Appl. Sci. 2024, 14(5), 2121; https://doi.org/10.3390/app14052121 - 04 Mar 2024
Viewed by 534
Abstract
Due to the increasing severity of network security issues, training corresponding detection models requires large datasets. In this work, we propose a novel method based on generative adversarial networks to synthesize network data traffic. We introduced a network traffic data normalization method based [...] Read more.
Due to the increasing severity of network security issues, training corresponding detection models requires large datasets. In this work, we propose a novel method based on generative adversarial networks to synthesize network data traffic. We introduced a network traffic data normalization method based on Gaussian mixture models (GMM), and for the first time, incorporated a generator based on the Swin Transformer structure into the field of network traffic generation. To further enhance the robustness of the model, we mapped real data through an AE (autoencoder) module and optimized the training results in the form of evolutionary algorithms. We validated the training results on four different datasets and introduced four additional models for comparative experiments in the experimental evaluation section. Our proposed SEGAN outperformed other state-of-the-art network traffic emulation methods. Full article
(This article belongs to the Special Issue State-of-the-Art of Network Attack Detection and Situation Awareness Analysis)
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20 pages, 1976 KiB  
Article
Distributed Detection of Large-Scale Internet of Things Botnets Based on Graph Partitioning
by Kexiang Qian, Hongyu Yang, Ruyu Li, Weizhe Chen, Xi Luo and Lihua Yin
Appl. Sci. 2024, 14(4), 1615; https://doi.org/10.3390/app14041615 - 17 Feb 2024
Viewed by 554
Abstract
With the rapid growth of IoT devices, the threat of botnets is becoming increasingly worrying. There are more and more intelligent detection solutions for botnets that have been proposed with the development of artificial intelligence. However, due to the current lack of computing [...] Read more.
With the rapid growth of IoT devices, the threat of botnets is becoming increasingly worrying. There are more and more intelligent detection solutions for botnets that have been proposed with the development of artificial intelligence. However, due to the current lack of computing power in IoT devices, these intelligent methods often cannot be well-applied to IoT devices. Based on the above situation, this paper proposes a distributed botnet detection method based on graph partitioning, efficiently detecting botnets using graph convolutional networks. In order to alleviate the wide range of IoT environments and the limited computing power of IoT devices, the algorithm named METIS is used to divide the network traffic structure graph into small graphs. To ensure robust information flow between nodes while preventing gradient explosion, diagonal enhancement is applied to refine the embedding representations at each layer, facilitating accurate botnet attack detection. Through comparative analysis with GATv2, GraphSAGE, and GCN across the C2, P2P, and Chord datasets, our method demonstrates superior performance in both accuracy and F1 score metrics. Moreover, an exploration into the effects of varying cluster numbers and depths revealed that six cluster levels yielded optimal results on the C2 dataset. This research significantly contributes to mitigating the IoT botnet threat, offering a scalable and effective solution for diverse IoT ecosystems. Full article
(This article belongs to the Special Issue State-of-the-Art of Network Attack Detection and Situation Awareness Analysis)
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14 pages, 1643 KiB  
Article
Android Malware Detection Based on Hypergraph Neural Networks
by Dehua Zhang, Xiangbo Wu, Erlu He, Xiaobo Guo, Xiaopeng Yang, Ruibo Li and Hao Li
Appl. Sci. 2023, 13(23), 12629; https://doi.org/10.3390/app132312629 - 23 Nov 2023
Viewed by 809
Abstract
Android has been the most widely used operating system for mobile phones over the past few years. Malicious attacks against android are a major privacy and security concern. Malware detection techniques for android applications are therefore significant. A class of methods using Function [...] Read more.
Android has been the most widely used operating system for mobile phones over the past few years. Malicious attacks against android are a major privacy and security concern. Malware detection techniques for android applications are therefore significant. A class of methods using Function Call Graphs (FCGs) for android malware detection has shown great potential. The relationships between functions are limited to simple binary relationships (i.e., graphs) in these methods. However, one function often calls several other functions to produce specific effects in android applications, which cannot be captured with FCGs. In this paper, we propose to formalize android malware detection as a hypergraph-level classification task. A hypergraph is a topology capable of portraying complex relationships between multiple vertices, which can better characterize the functional behavior of android applications. We model android applications using hypergraphs and extract the embedded features of android applications using hypergraph neural networks to represent the functional behavior of android applications. Hypergraph neural networks can encode high-order data correlation in a hypergraph structure for data representation learning. In experiments, we validate the gaining effect of hypergraphs on detection performance across two open-source android application datasets. Especially, HGNNP obtains the best classification performance of 91.10% on the Malnet-Tiny dataset and 97.1% on the Drebin dataset, which outperforms all baseline methods. Full article
(This article belongs to the Special Issue State-of-the-Art of Network Attack Detection and Situation Awareness Analysis)
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