Application of Artificial Intelligence in User Interfaces Design for Cyber Security Threat Modeling

Akinsola, J. E. T.; Akinseinde, Samuel; Olamide, Kalesanwo; Adeagbo, Moruf; Oladapo, Kayode; Awoseyi, Ayomikun; Kasali, Funmilayo

doi:10.5772/intechopen.96534

Cited by 5 publications

(2 citation statements)

References 26 publications

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“…Understandably, machine intelligence cannot replace human intelligence, and the next generation of AI will probably combine human and machine intelligence (Akinsola et al, 2021). Artificial Intelligence and Blockchain intersection is growing rapidly in healthcare and digitalization of healthcare operation has increase the risk level of their business operation (Muheidat & Tawalbeh, 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

Application of Reactive Artificial Intelligence Model to Predict Malicious Activities

Yamcharoen,

Folorunsho,

Bayewu

et al. 2021

AIMS Research Publication

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The critical infrastructure in the United States have been attacked countless time, and the federal, state, and private entities are concerned with securing their network infrastructure and sensitive data to prevent revenue loss and intellectual properties. Billion dollars are lost to cyberattacks globally, and the United States tops the list of countries that recorded more cyber-attacks. The healthcare industries have lost revenue due to litigation from compromised patient data and revenue paid as a ransom to cyber attackers. Integrating a reactive artificial neural network model into their cybersecurity architecture will monitor the activities of users' activities internally or externally accessing the organization's resources. The Newton method algorithm was applied to predict the malicious activities and the performance of the model support derivative matrix from the input variables that were used to train the model. The validation result of the algorithm has proven that an artificial neural network model can be deployed and integrated into the cybersecurity solution to predict the likelihood of an attack. Keywords: Healthcare, Cybersecurity, Artificial Intelligence, Threat Actors, Risk Assessment. Yamcharoen P., Folorunsho O.S., Bayewu A. & Ojo T.P. (2021): Application of Reactive Artificial Intelligence Model to Predict Malicious Activities. Journal of Advances in Mathematical & Computational Science. Vol. 9, No. 2. Pp 61-68. DOI: dx.doi.org/10.22624/AIMS/MATHS/V9N2P5 Available online at www.isteams.net/mathematics-computationaljournal.

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Section: Literature Reviewmentioning

confidence: 99%

Application of Reactive Artificial Intelligence Model to Predict Malicious Activities

Yamcharoen,

Folorunsho,

Bayewu

et al. 2021

AIMS Research Publication

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“…In the military application, we can utilize the principles to design and develop interactive systems that can help prove the effectiveness and efficiency of the military operation. An example of a military SCI system is comment on Control systems that enables real-time to distinguish making virtual training and unmanned aerial systems that provide remote surveillance [29][30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Non-Invasive Tongue-Based HCI System Using Deep Learning for Microgesture Detection

Jasim,

Shareef

2023

RIA

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Tongue-based Human-Computer Interaction (HCI) systems have surfaced as alternative input devices offering significant benefits to individuals with severe disabilities. However, these systems often employ invasive methods such as dental retainers, tongue piercings, and multiple mouth electrodes. These methods, due to hygiene issues and obtrusiveness, are deemed impractical for daily use. This paper presents a novel non-invasive tonguebased HCI system that utilizes deep learning for microgesture detection. The proposed system overcomes the limitations of previous methods by non-invasively detecting gestures. This is accomplished by measuring tongue vibrations via an accelerometer positioned on the Genioglossus muscle, thereby eliminating the need for in-mouth installations. The system's performance was evaluated by comparing the classification results of deep learning with four widely-used supervised machine learning algorithms, namely K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Decision Trees, and Random Forests. Raw data were preprocessed in both time and frequency domains to extract relevant patterns before classification. In addition, a deep learning Convolutional Neural Network (CNN) model was trained on the raw data, leveraging its proficiency in processing time series data and capturing intricate patterns automatically using convolutional and pooling layers. The CNN model demonstrated a 97% success rate in tongue gesture detection, indicating its high accuracy. The proposed system is also lowprofile, lightweight, and cost-effective, making it suitable for daily use in various contexts. This study thus introduces a non-invasive, efficient, and practical approach to tongue-based HCI systems.

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Recommender Systems in Cybersecurity

Ferreira

Silva

Itzazelaia³

2023

Knowl Inf Syst

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With the growth of CyberTerrorism, enterprises worldwide have been struggling to stop intruders from obtaining private data. Despite the efforts made by Cybersecurity experts, the shortage of skillful security teams and the usage of intelligent attacks have slowed down the enhancement of defense mechanisms. Furthermore, the pandemic in 2020 forced organizations to work in remote environments with poor security, leading to increased cyberattacks. One possible solution for these problems is the implementation of Recommender Systems to assist Cybersecurity human operators. Our goal is to survey the application of Recommender Systems in Cybersecurity architectures. These decision-support tools deal with information overload through filtering and prioritization methods, allowing businesses to increase revenue, achieve better user satisfaction, and make faster and more efficient decisions in various domains (e-commerce, healthcare, finance, and other fields). Several reports demonstrate the potential of using these recommendation structures to enhance the detection and prevention of cyberattacks and aid Cybersecurity experts in treating client incidents. This survey discusses several studies where Recommender Systems are implemented in Cybersecurity with encouraging results. One promising direction explored by the community is using Recommender Systems as attack predictors and navigation assistance tools. As contributions, we show the recent efforts in this area and summarize them in a table. Furthermore, we provide an in-depth analysis of potential research lines. For example, the inclusion of Recommender Systems in security information event management systems and security orchestration, automation, and response applications could decrease their complexity and information overload.

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Application of Artificial Intelligence in User Interfaces Design for Cyber Security Threat Modeling

Cited by 5 publications

References 26 publications

Application of Reactive Artificial Intelligence Model to Predict Malicious Activities

Application of Reactive Artificial Intelligence Model to Predict Malicious Activities

Non-Invasive Tongue-Based HCI System Using Deep Learning for Microgesture Detection

Recommender Systems in Cybersecurity

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