Special Issue "Applications of Artificial Intelligence and Machine Learning in Cyber 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: closed (20 January 2023) | Viewed by 2360

Special Issue Editors

Dr. Chin-Shiuh Shieh
E-Mail Website
Guest Editor
Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 807618, Taiwan
Interests: computer networking; information security; computational intelligence; embedded systems; medical informatics
Special Issues, Collections and Topics in MDPI journals
Dr. Shu-Chuan Chu
E-Mail Website
Guest Editor
College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
Interests: evolutionary computation; wireless sensor networks; swarm intelligence; information security
Special Issues, Collections and Topics in MDPI journals
Dr. Tarek Gaber
E-Mail Website
Guest Editor
School of Science, Engineering & Environment, University of Salford, Greater Manchester M5 4WT, UK
Interests: biometric authentication and identification; cybersecurity; machine learning; secure software engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the last decade, the emergence of artificial intelligence (AI) and machine learning (ML) has seen tremendous advancements and evolution in various spheres of life. However, the widespread usage of these technologies in everyday applications has created new types of cybersecurity threats, such as backdoor attacks, deep fakes, and adversarial attacks. Nevertheless, on the other hand, contemporary algorithms such as deep learning, continuous learning, and generative adversarial networks (GAN) have been effectively used to tackle various security tasks. As a result, it is vital to apply these innovative methods to life-critical missions and evaluate the efficacy of these less-traditional algorithms in cybersecurity sectors.

  • Secure artificial intelligence;
  • Private machine learning;
  • Adversarial machine learning;
  • Adversarial attack;
  • Deep fakes;
  • Anomaly detection;
  • Malware detection;
  • Differential privacy;
  • Imbalanced datasets;
  • Security in cloud services;
  • Security in RFID;
  • Smart systems in cyber security;
  • Security in wireless sensor networks.

Dr. Chin-Shiuh Shieh
Dr. Shu-Chuan Chu
Dr. Tarek Gaber
Guest Editors

Manuscript Submission Information

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Published Papers (2 papers)

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Research

Article
An Intrusion Detection and Classification System for IoT Traffic with Improved Data Engineering
Appl. Sci. 2022, 12(23), 12336; https://doi.org/10.3390/app122312336 - 02 Dec 2022
Viewed by 771
Abstract
Nowadays, the Internet of Things (IoT) devices and applications have rapidly expanded worldwide due to their benefits in improving the business environment, industrial environment, and people’s daily lives. However, IoT devices are not immune to malicious network traffic, which causes potential negative consequences [...] Read more.
Nowadays, the Internet of Things (IoT) devices and applications have rapidly expanded worldwide due to their benefits in improving the business environment, industrial environment, and people’s daily lives. However, IoT devices are not immune to malicious network traffic, which causes potential negative consequences and sabotages IoT operating devices. Therefore, developing a method for screening network traffic is necessary to detect and classify malicious activity to mitigate its negative impacts. This research proposes a predictive machine learning model to detect and classify network activity in an IoT system. Specifically, our model distinguishes between normal and anomaly network activity. Furthermore, it classifies network traffic into five categories: normal, Mirai attack, denial of service (DoS) attack, Scan attack, and man-in-the-middle (MITM) attack. Five supervised learning models were implemented to characterize their performance in detecting and classifying network activities for IoT systems. This includes the following models: shallow neural networks (SNN), decision trees (DT), bagging trees (BT), k-nearest neighbor (kNN), and support vector machine (SVM). The learning models were evaluated on a new and broad dataset for IoT attacks, the IoTID20 dataset. Besides, a deep feature engineering process was used to improve the learning models’ accuracy. Our experimental evaluation exhibited an accuracy of 100% recorded for the detection using all implemented models and an accuracy of 99.4–99.9% recorded for the classification process. Full article
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Article
FCNN-SE: An Intrusion Detection Model Based on a Fusion CNN and Stacked Ensemble
Appl. Sci. 2022, 12(17), 8601; https://doi.org/10.3390/app12178601 - 27 Aug 2022
Viewed by 798
Abstract
As a security defense technique to protect networks from attacks, a network intrusion detection model plays a crucial role in the security of computer systems and networks. Aiming at the shortcomings of a complex feature extraction process and insufficient information extraction of the [...] Read more.
As a security defense technique to protect networks from attacks, a network intrusion detection model plays a crucial role in the security of computer systems and networks. Aiming at the shortcomings of a complex feature extraction process and insufficient information extraction of the existing intrusion detection models, an intrusion detection model named the FCNN-SE, which uses the fusion convolutional neural network (FCNN) for feature extraction and stacked ensemble (SE) for classification, is proposed in this paper. The proposed model mainly includes two parts, feature extraction and feature classification. Multi-dimensional features of traffic data are first extracted using convolutional neural networks of different dimensions and then fused into a network traffic dataset. The heterogeneous base learners are combined and used as a classifier, and the obtained network traffic dataset is fed to the classifier for final classification. The comprehensive performance of the proposed model is verified through experiments, and experimental results are evaluated using a comprehensive performance evaluation method based on the radar chart method. The comparison results on the NSL-KDD dataset show that the proposed FCNN-SE has the highest overall performance among all compared models, and a more balanced performance than the other models. Full article
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