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Artificial Intelligence-Based Approaches for Future Cybersecurity Applications and Crime Detection
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Artificial Intelligence-Based Approaches for Future Cybersecurity Applications and Crime Detection

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 (10 June 2022) | Viewed by 23058

Special Issue Editors

Dr. Andrea Tundis

E-Mail Website
Guest Editor
Telecooperation Lab (TK), Department of Computer Science, Technical University of Darmstadt (TU Darmstadt), 64289 Darmstadt, Germany
Interests: internet-based crime detection; online propaganda detection; online social media analysis; cyber-terrorism
Dr. Lorenzo Musarella

E-Mail Website
Guest Editor
Department of Information Engineering, Infrastructure and Sustainable Energy, University Mediterranea of Reggio Calabria (UNIRC), 89214 Reggio Calabria, Italy
Interests: network analysis; trust; blockchain; cybersecurity
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Although in recent years, investments and efforts in finding and implementing new cybersecurity solutions have been increasing, companies, organizations, and private citizens are still victims of cyber-attacks. With the still ongoing pandemic, the process of migration of information toward the digital domain has sped up even more. Furthermore, the imposition of the IoT and cloud computing has enlarged the attack surface for malicious people, thus making it necessary to design and develop new countermeasures against potential risks and attacks.

Artificial intelligence is the ability of a machine to implement human capabilities such as reasoning, learning, planning, and creativity, thus allowing systems to understand their environment, relate to what they perceive, and to solve problems, acting towards a specific goal. Its employment, centered on classification models, is to exploit algorithms and data analysis techniques providing more efficient and performing solutions than traditional ones. Machine learning is deeply involved in these new techniques, which are adopted for the study of phenomena in several application domains, such as finance, healthcare, dependability, cybersecurity, network analysis, crime detection, and so on. In this context, we aim to deepen and promote the dissemination and exchange of novel theories, designs, applications based on artificial intelligence, as well as ongoing results among researchers and practitioners related to various cybersecurity perspectives.

This Special Issue will investigate several topics under the artificial intelligence for novel cybersecurity applications umbrella and welcomes paper submissions providing innovative research as well as technical contributions that foster the creation of a new value chain within this research field.

Dr. Andrea Tundis
Dr. Lorenzo Musarella
Guest Editors

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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

  • artificial Intelligence for cybersecurity
  • social network security
  • online social media analysis
  • online propaganda detection
  • organized cybercrime
  • cyber-terrorism
  • risk and threat management
  • dependability modeling and prediction
  • information security
  • IoT security
  • network security
  • cloud security

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Published Papers (5 papers)

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Research

24 pages, 4826 KiB  
Article
A VPN-Encrypted Traffic Identification Method Based on Ensemble Learning
by Jie Cao, Xing-Liang Yuan, Ying Cui, Jia-Cheng Fan and Chin-Ling Chen
Appl. Sci. 2022, 12(13), 6434; https://doi.org/10.3390/app12136434 - 24 Jun 2022
Cited by 4 | Viewed by 3051
Abstract
One of the foundational and key means of optimizing network service in the field of network security is traffic identification. Various data transmission encryption technologies have been widely employed in recent years. Wrongdoers usually bypass the defense of network security facilities through VPN [...] Read more.
One of the foundational and key means of optimizing network service in the field of network security is traffic identification. Various data transmission encryption technologies have been widely employed in recent years. Wrongdoers usually bypass the defense of network security facilities through VPN to carry out network intrusion and malicious attacks. The existing encrypted traffic identification system faces a severe problem as a result of this phenomenon. Previous encrypted traffic identification methods suffer from feature redundancy, data class imbalance, and low identification rate. To address these three problems, this paper proposes a VPN-encrypted traffic identification method based on ensemble learning. Firstly, aiming at the problem of feature redundancy in VPN-encrypted traffic features, a method of selecting encrypted traffic features based on mRMR is proposed; secondly, aiming at the problem of data class imbalance, improving the Xgboost identification model by using the focal loss function for the data class imbalance problem; Finally, in order to improve the identification rate of VPN-encrypted traffic identification methods, an ensemble learning model parameter optimization method based on optimal Bayesian is proposed. Experiments revealed that our proposed VPN-encrypted traffic identification method produced more desirable VPN-encrypted traffic identification outcomes. Meanwhile, using two encrypted traffic datasets, eight common identification algorithms are compared, and the method appears to be more accurate in identifying encrypted traffic. Full article
(This article belongs to the Special Issue Artificial Intelligence-Based Approaches for Future Cybersecurity Applications and Crime Detection)
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24 pages, 15313 KiB  
Article
An Intelligent Penetration Test Simulation Environment Construction Method Incorporating Social Engineering Factors
by Yang Li, Yongjie Wang, Xinli Xiong, Jingye Zhang and Qian Yao
Appl. Sci. 2022, 12(12), 6186; https://doi.org/10.3390/app12126186 - 17 Jun 2022
Cited by 6 | Viewed by 3615
Abstract
The penetration test has many repetitive operations and requires advanced expert knowledge, therefore, the manual penetration test is inefficient. With the development of reinforcement learning, the intelligent penetration test has been a research hotspot. However, the existing intelligent penetration test simulation environments only [...] Read more.
The penetration test has many repetitive operations and requires advanced expert knowledge, therefore, the manual penetration test is inefficient. With the development of reinforcement learning, the intelligent penetration test has been a research hotspot. However, the existing intelligent penetration test simulation environments only focus on the exploits of target hosts by the penetration tester agent’s actions while ignoring the important role of social engineering in the penetration test in reality. In addition, the construction of the existing penetration test simulation environment is based on the traditional network graph model without integrating security factors and attributes, and it is difficult to express the interaction between the penetration tester and the target network. This paper constructs an improved network graph model for penetration test (NMPT), which integrates the relevant security attributes of the penetration test. The NMPT model lays the foundation for extending the penetration tester’s social engineering actions. Then, we propose an intelligent penetration test method that incorporates social engineering factors (SE-AIPT) based on the Markov Decision Process. We adopt several mainstream reinforcement learning algorithms to train attack agents. The experiments show that the SE-AIPT method could vividly model the penetration tester agent’s social engineering actions, which effectively improves the reality of the simulation environment. Moreover, the penetration tester agent shows superior effects in the attack path discovery in the intelligent penetration test simulation environment constructed by the SE-AIPT method. Full article
(This article belongs to the Special Issue Artificial Intelligence-Based Approaches for Future Cybersecurity Applications and Crime Detection)
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16 pages, 654 KiB  
Article
Aggression Detection in Social Media from Textual Data Using Deep Learning Models
by Umair Khan, Salabat Khan, Atif Rizwan, Ghada Atteia, Mona M. Jamjoom and Nagwan Abdel Samee
Appl. Sci. 2022, 12(10), 5083; https://doi.org/10.3390/app12105083 - 18 May 2022
Cited by 30 | Viewed by 6346
Abstract
It is an undeniable fact that people excessively rely on social media for effective communication. However, there is no appropriate barrier as to who becomes a part of the communication. Therefore, unknown people ruin the fundamental purpose of effective communication with irrelevant—and sometimes [...] Read more.
It is an undeniable fact that people excessively rely on social media for effective communication. However, there is no appropriate barrier as to who becomes a part of the communication. Therefore, unknown people ruin the fundamental purpose of effective communication with irrelevant—and sometimes aggressive—messages. As its popularity increases, its impact on society also increases, from primarily being positive to negative. Cyber aggression is a negative impact; it is defined as the willful use of information technology to harm, threaten, slander, defame, or harass another person. With increasing volumes of cyber-aggressive messages, tweets, and retweets, there is a rising demand for automated filters to identify and remove these unwanted messages. However, most existing methods only consider NLP-based feature extractors, e.g., TF-IDF, Word2Vec, with a lack of consideration for emotional features, which makes these less effective for cyber aggression detection. In this work, we extracted eight novel emotional features and used a newly designed deep neural network with only three numbers of layers to identify aggressive statements. The proposed DNN model was tested on the Cyber-Troll dataset. The combination of word embedding and eight different emotional features were fed into the DNN for significant improvement in recognition while keeping the DNN design simple and computationally less demanding. When compared with the state-of-the-art models, our proposed model achieves an F1 score of 97%, surpassing the competitors by a significant margin. Full article
(This article belongs to the Special Issue Artificial Intelligence-Based Approaches for Future Cybersecurity Applications and Crime Detection)
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24 pages, 1541 KiB  
Article
Empirical Analysis of Forest Penalizing Attribute and Its Enhanced Variations for Android Malware Detection
by Abimbola G. Akintola, Abdullateef O. Balogun, Luiz Fernando Capretz, Hammed A. Mojeed, Shuib Basri, Shakirat A. Salihu, Fatima E. Usman-Hamza, Peter O. Sadiku, Ghaniyyat B. Balogun and Zubair O. Alanamu
Appl. Sci. 2022, 12(9), 4664; https://doi.org/10.3390/app12094664 - 6 May 2022
Cited by 13 | Viewed by 2865
Abstract
As a result of the rapid advancement of mobile and internet technology, a plethora of new mobile security risks has recently emerged. Many techniques have been developed to address the risks associated with Android malware. The most extensively used method for identifying Android [...] Read more.
As a result of the rapid advancement of mobile and internet technology, a plethora of new mobile security risks has recently emerged. Many techniques have been developed to address the risks associated with Android malware. The most extensively used method for identifying Android malware is signature-based detection. The drawback of this method, however, is that it is unable to detect unknown malware. As a consequence of this problem, machine learning (ML) methods for detecting and classifying malware applications were developed. The goal of conventional ML approaches is to improve classification accuracy. However, owing to imbalanced real-world datasets, the traditional classification algorithms perform poorly in detecting malicious apps. As a result, in this study, we developed a meta-learning approach based on the forest penalizing attribute (FPA) classification algorithm for detecting malware applications. In other words, with this research, we investigated how to improve Android malware detection by applying empirical analysis of FPA and its enhanced variants (Cas_FPA and RoF_FPA). The proposed FPA and its enhanced variants were tested using the Malgenome and Drebin Android malware datasets, which contain features gathered from both static and dynamic Android malware analysis. Furthermore, the findings obtained using the proposed technique were compared with baseline classifiers and existing malware detection methods to validate their effectiveness in detecting malware application families. Based on the findings, FPA outperforms the baseline classifiers and existing ML-based Android malware detection models in dealing with the unbalanced family categorization of Android malware apps, with an accuracy of 98.94% and an area under curve (AUC) value of 0.999. Hence, further development and deployment of FPA-based meta-learners for Android malware detection and other cybersecurity threats is recommended. Full article
(This article belongs to the Special Issue Artificial Intelligence-Based Approaches for Future Cybersecurity Applications and Crime Detection)
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25 pages, 5317 KiB  
Article
Predictive Fraud Analysis Applying the Fraud Triangle Theory through Data Mining Techniques
by Marco Sánchez-Aguayo, Luis Urquiza-Aguiar and José Estrada-Jiménez
Appl. Sci. 2022, 12(7), 3382; https://doi.org/10.3390/app12073382 - 26 Mar 2022
Cited by 9 | Viewed by 6037
Abstract
Fraud is increasingly common, and so are the losses caused by this phenomenon. There is, thus, an essential economic incentive to study this problem, particularly fraud prevention. One barrier complicating the research in this direction is the lack of public data sets that [...] Read more.
Fraud is increasingly common, and so are the losses caused by this phenomenon. There is, thus, an essential economic incentive to study this problem, particularly fraud prevention. One barrier complicating the research in this direction is the lack of public data sets that embed fraudulent activities. In addition, although efforts have been made to detect fraud using machine learning, such actions have not considered the component of human behavior when detecting fraud. We propose a mechanism to detect potential fraud by analyzing human behavior within a data set in this work. This approach combines a predefined topic model and a supervised classifier to generate an alert from the possible fraud-related text. Potential fraud would be detected based on a model built from such a classifier. As a result of this work, a synthetic fraud-related data set is made. Four topics associated with the vertices of the fraud triangle theory are unveiled when assessing different topic modeling techniques. After benchmarking topic modeling techniques and supervised and deep learning classifiers, we find that LDA, random forest, and CNN have the best performance in this scenario. The results of our work suggest that our approach is feasible in practice since several such models obtain an average AUC higher than 0.8. Namely, the fraud triangle theory combined with topic modeling and linear classifiers could provide a promising framework for predictive fraud analysis. Full article
(This article belongs to the Special Issue Artificial Intelligence-Based Approaches for Future Cybersecurity Applications and Crime Detection)
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