Submit to Sensors Review for Sensors Propose a Special Issue

Journal Browser

► Journal Browser

Advances in Artificial Intelligence for Cyber Security

Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 12291

Share This Special Issue

Special Issue Editor

Dr. Thanh Thi Nguyen

E-Mail Website
Guest Editor

School of Information Technology, Deakin University, Burwood, VIC 3125, Australia
Interests: artificial intelligence; deep learning; deep reinforcement learning; data science; big data; cybersecurity; IoT; image processing; robotics; autonomous vehicles; multiagent systems; human–machine integration; defence technologies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) has been applied widely to address cybersecurity problems. Cyber attacks, however, are growing in volume and complexity. This is of greater concern in the area of sensors and sensing technology, as they are increasingly used in all sorts of platforms inspired by the emerging Internet of Things. There is a critical need for the development of advanced cybersecurity methods to mitigate and eliminate the impacts of cyber attacks. Protecting and defending mechanisms are required to be more responsive, adaptive, and scalable. Advances in AI can be highly capable of solving complex, dynamic, and especially high-dimensional cybersecurity problems. They can help to provide more accurate detection, effective response automation, and proactive protection mechanism.

This Special Issue of Sensors aims to address the increasingly complex cybersecurity problems using advances in AI.

Dr. Thanh Thi Nguyen
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. Sensors 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 2600 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

artificial intelligence
machine learning
sensor
sensing technology
smart devices
cybersecurity
cyber threats
cyber attacks
Internet of Things
cloud computing
edge computing

Published Papers (4 papers)

Download All Papers

Research

25 pages, 2215 KiB

Open AccessArticle

Android Spyware Detection Using Machine Learning: A Novel Dataset

by Majdi K. Qabalin, Muawya Naser and Mouhammd Alkasassbeh

Sensors 2022, 22(15), 5765; https://0-doi-org.brum.beds.ac.uk/10.3390/s22155765 - 02 Aug 2022

Cited by 13 | Viewed by 3607

Abstract

Smartphones are an essential part of all aspects of our lives. Socially, politically, and commercially, there is almost complete reliance on smartphones as a communication tool, a source of information, and for entertainment. Rapid developments in the world of information and cyber security have necessitated close attention to the privacy and protection of smartphone data. Spyware detection systems have recently been developed as a promising and encouraging solution for smartphone users’ privacy protection. The Android operating system is the most widely used worldwide, making it a significant target for many parties interested in targeting smartphone users’ privacy. This paper introduces a novel dataset collected in a realistic environment, obtained through a novel data collection methodology based on a unified activity list. The data are divided into three main classes: the first class represents normal smartphone traffic; the second class represents traffic data for the spyware installation process; finally, the third class represents spyware operation traffic data. The random forest classification algorithm was adopted to validate this dataset and the proposed model. Two methodologies were adopted for data classification: binary-class and multi-class classification. Good results were achieved in terms of accuracy. The overall average accuracy was 79% for the binary-class classification, and 77% for the multi-class classification. In the multi-class approach, the detection accuracy for spyware systems (UMobix, TheWiSPY, MobileSPY, FlexiSPY, and mSPY) was 90%, 83.7%, 69.3%, 69.2%, and 73.4%, respectively; in binary-class classification, the detection accuracy for spyware systems (UMobix, TheWiSPY, MobileSPY, FlexiSPY, and mSPY) was 93.9%, 85.63%, 71%, 72.3%, and 75.96%; respectively. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence for Cyber Security)

► Show Figures

Figure 1

26 pages, 2342 KiB

Open AccessArticle

Introducing the CYSAS-S3 Dataset for Operationalizing a Mission-Oriented Cyber Situational Awareness

by Roumen Daton Medenou Choumanof, Salvador Llopis Sanchez, Victor Manuel Calzado Mayo, Miriam Garcia Balufo, Miguel Páramo Castrillo, Francisco José González Garrido, Alvaro Luis Martinez, David Nevado Catalán, Ao Hu, David Sandoval Rodríguez-Bermejo, Gerardo Ramis Pasqual de Riquelme, Marco Antonio Sotelo Monge, Antonio Berardi, Paolo De Santis, Francesco Torelli and Jorge Maestre Vidal

Sensors 2022, 22(14), 5104; https://0-doi-org.brum.beds.ac.uk/10.3390/s22145104 - 07 Jul 2022

Cited by 1 | Viewed by 2149

Abstract

The digital transformation of the defence sector is not exempt from innovative requirements and challenges, with the lack of availability of reliable, unbiased and consistent data for training automatisms (machine learning algorithms, decision-making, what-if recreation of operational conditions, support the human understanding of the hybrid operational picture, personnel training/education, etc.) being one of the most relevant gaps. In the context of cyber defence, the state-of-the-art provides a plethora of data network collections that tend to lack presenting the information of all communication layers (physical to application). They are synthetically generated in scenarios far from the singularities of cyber defence operations. None of these data network collections took into consideration usage profiles and specific environments directly related to acquiring a cyber situational awareness, typically missing the relationship between incidents registered at the hardware/software level and their impact on the military mission assets and objectives, which consequently bypasses the entire chain of dependencies between strategic, operational, tactical and technical domains. In order to contribute to the mitigation of these gaps, this paper introduces CYSAS-S3, a novel dataset designed and created as a result of a joint research action that explores the principal needs for datasets by cyber defence centres, resulting in the generation of a collection of samples that correlate the impact of selected Advanced Persistent Threats (APT) with each phase of their cyber kill chain, regarding mission-level operations and goals. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence for Cyber Security)

► Show Figures

Figure 1

21 pages, 571 KiB

Open AccessArticle

LPG–PCFG: An Improved Probabilistic Context- Free Grammar to Hit Low-Probability Passwords

by Xiaozhou Guo, Kaijun Tan, Yi Liu, Min Jin and Huaxiang Lu

Sensors 2022, 22(12), 4604; https://0-doi-org.brum.beds.ac.uk/10.3390/s22124604 - 18 Jun 2022

Cited by 2 | Viewed by 1918

Abstract

With the development of the Internet, information security has attracted more attention. Identity authentication based on password authentication is the first line of defense; however, the password-generation model is widely used in offline password attacks and password strength evaluation. In real attack scenarios, high-probability passwords are easy to enumerate; extremely low-probability passwords usually lack semantic structure and, so, are tough to crack by applying statistical laws in machine learning models, but these passwords with lower probability have a large search space and certain semantic information. Improving the low-probability password hit rate in this interval is of great significance for improving the efficiency of offline attacks. However, obtaining a low-probability password is difficult under the current password-generation model. To solve this problem, we propose a low-probability generator–probabilistic context-free grammar (LPG–PCFG) based on PCFG. LPG–PCFG directionally increases the probability of low-probability passwords in the models’ distribution, which is designed to obtain a degeneration distribution that is friendly for generating low-probability passwords. By using the control variable method to fine-tune the degeneration of LPG–PCFG, we obtained the optimal combination of degeneration parameters. Compared with the non-degeneration PCFG model, LPG–PCFG generates a larger number of hits. When generating

10^{7}

and

10^{8}

times, the number of hits to low-probability passwords increases by

50.4 %

and

42.0 %

, respectively. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence for Cyber Security)

► Show Figures

Figure 1

22 pages, 8915 KiB

Open AccessArticle

A Novel Detection and Multi-Classification Approach for IoT-Malware Using Random Forest Voting of Fine-Tuning Convolutional Neural Networks

by Safa Ben Atitallah, Maha Driss and Iman Almomani

Sensors 2022, 22(11), 4302; https://0-doi-org.brum.beds.ac.uk/10.3390/s22114302 - 06 Jun 2022

Cited by 25 | Viewed by 3377

Abstract

The Internet of Things (IoT) is prone to malware assaults due to its simple installation and autonomous operating qualities. IoT devices have become the most tempting targets of malware due to well-known vulnerabilities such as weak, guessable, or hard-coded passwords, a lack of secure update procedures, and unsecured network connections. Traditional static IoT malware detection and analysis methods have been shown to be unsatisfactory solutions to understanding IoT malware behavior for mitigation and prevention. Deep learning models have made huge strides in the realm of cybersecurity in recent years, thanks to their tremendous data mining, learning, and expression capabilities, thus easing the burden on malware analysts. In this context, a novel detection and multi-classification vision-based approach for IoT-malware is proposed. This approach makes use of the benefits of deep transfer learning methodology and incorporates the fine-tuning method and various ensembling strategies to increase detection and classification performance without having to develop the training models from scratch. It adopts the fusion of 3 CNNs, ResNet18, MobileNetV2, and DenseNet161, by using the random forest voting strategy. Experiments are carried out using a publicly available dataset, MaleVis, to assess and validate the suggested approach. MaleVis contains 14,226 RGB converted images representing 25 malware classes and one benign class. The obtained findings show that our suggested approach outperforms the existing state-of-the-art solutions in terms of detection and classification performance; it achieves a precision of 98.74%, recall of 98.67%, a specificity of 98.79%, F1-score of 98.70%, MCC of 98.65%, an accuracy of 98.68%, and an average processing time per malware classification of 672 ms. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence for Cyber Security)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Advances in Artificial Intelligence for Cyber Security

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Published Papers (4 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI