scholarly journals Detecting Cybersecurity Attacks in Internet of Things Using Artificial Intelligence Methods: A Systematic Literature Review

Electronics ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 198
Author(s):  
Mujaheed Abdullahi ◽  
Yahia Baashar ◽  
Hitham Alhussian ◽  
Ayed Alwadain ◽  
Norshakirah Aziz ◽  
...  

In recent years, technology has advanced to the fourth industrial revolution (Industry 4.0), where the Internet of things (IoTs), fog computing, computer security, and cyberattacks have evolved exponentially on a large scale. The rapid development of IoT devices and networks in various forms generate enormous amounts of data which in turn demand careful authentication and security. Artificial intelligence (AI) is considered one of the most promising methods for addressing cybersecurity threats and providing security. In this study, we present a systematic literature review (SLR) that categorize, map and survey the existing literature on AI methods used to detect cybersecurity attacks in the IoT environment. The scope of this SLR includes an in-depth investigation on most AI trending techniques in cybersecurity and state-of-art solutions. A systematic search was performed on various electronic databases (SCOPUS, Science Direct, IEEE Xplore, Web of Science, ACM, and MDPI). Out of the identified records, 80 studies published between 2016 and 2021 were selected, surveyed and carefully assessed. This review has explored deep learning (DL) and machine learning (ML) techniques used in IoT security, and their effectiveness in detecting attacks. However, several studies have proposed smart intrusion detection systems (IDS) with intelligent architectural frameworks using AI to overcome the existing security and privacy challenges. It is found that support vector machines (SVM) and random forest (RF) are among the most used methods, due to high accuracy detection another reason may be efficient memory. In addition, other methods also provide better performance such as extreme gradient boosting (XGBoost), neural networks (NN) and recurrent neural networks (RNN). This analysis also provides an insight into the AI roadmap to detect threats based on attack categories. Finally, we present recommendations for potential future investigations.

2019 ◽  
Vol 11 (3) ◽  
pp. 660 ◽  
Author(s):  
Kai Cao ◽  
Hui Guo ◽  
Ye Zhang

Accurate and timely classification and monitoring of urban functional zones prove to be significant in rapidly developing cities, to better understand the real and varying urban functions of cities to support urban planning and management. Many efforts have been undertaken to identify urban functional zones using various classification approaches and multi-source geospatial datasets. The complexity of this category of classification poses tremendous challenges to these studies especially in terms of classification accuracy, but on the opposite, the rapid development of machine learning technologies provides us with new opportunities. In this study, a set of commonly used urban functional zones classification approaches, including Multinomial Logistic Regression, K-Nearest Neighbors, Decision Tree, Support Vector Machine (SVM), and Random Forest, are examined and compared with the newly developed eXtreme Gradient Boosting (XGBoost) model, using the case study of Yuzhong District, Chongqing, China. The investigation is based on multi-variate geospatial data, including night-time imagery, geotagged Weibo data, points of interest (POI) from Gaode, and Baidu Heat Map. This study is the first endeavor of implementing the XGBoost model in the field of urban functional zones classification. The results suggest that the XGBoost classification model performed the best and was able to achieve an accuracy of 88.05%, which is significantly higher than the other commonly used approaches. In addition, the integration of night-time imagery, geotagged Weibo data, POI from Gaode, and Baidu Heat Map has also demonstrated their values for the classification of urban functional zones in this case study.


Author(s):  
Ruopeng Xie ◽  
Jiahui Li ◽  
Jiawei Wang ◽  
Wei Dai ◽  
André Leier ◽  
...  

Abstract Virulence factors (VFs) enable pathogens to infect their hosts. A wealth of individual, disease-focused studies has identified a wide variety of VFs, and the growing mass of bacterial genome sequence data provides an opportunity for computational methods aimed at predicting VFs. Despite their attractive advantages and performance improvements, the existing methods have some limitations and drawbacks. Firstly, as the characteristics and mechanisms of VFs are continually evolving with the emergence of antibiotic resistance, it is more and more difficult to identify novel VFs using existing tools that were previously developed based on the outdated data sets; secondly, few systematic feature engineering efforts have been made to examine the utility of different types of features for model performances, as the majority of tools only focused on extracting very few types of features. By addressing the aforementioned issues, the accuracy of VF predictors can likely be significantly improved. This, in turn, would be particularly useful in the context of genome wide predictions of VFs. In this work, we present a deep learning (DL)-based hybrid framework (termed DeepVF) that is utilizing the stacking strategy to achieve more accurate identification of VFs. Using an enlarged, up-to-date dataset, DeepVF comprehensively explores a wide range of heterogeneous features with popular machine learning algorithms. Specifically, four classical algorithms, including random forest, support vector machines, extreme gradient boosting and multilayer perceptron, and three DL algorithms, including convolutional neural networks, long short-term memory networks and deep neural networks are employed to train 62 baseline models using these features. In order to integrate their individual strengths, DeepVF effectively combines these baseline models to construct the final meta model using the stacking strategy. Extensive benchmarking experiments demonstrate the effectiveness of DeepVF: it achieves a more accurate and stable performance compared with baseline models on the benchmark dataset and clearly outperforms state-of-the-art VF predictors on the independent test. Using the proposed hybrid ensemble model, a user-friendly online predictor of DeepVF (http://deepvf.erc.monash.edu/) is implemented. Furthermore, its utility, from the user’s viewpoint, is compared with that of existing toolkits. We believe that DeepVF will be exploited as a useful tool for screening and identifying potential VFs from protein-coding gene sequences in bacterial genomes.


2020 ◽  
Author(s):  
Nikolaos Dikaios

AbstractThis paper aims to differentiate cancer types from primary tumour samples based on somatic point mutations (SPM). Primary cancer site identification is necessary to perform site-specific and potentially targeted treatment. Current methods like histopathology/lab-tests cannot accurately determine cancers origin, which results in empirical patient treatment and poor survival rates. The availability of large deoxyribonucleic-acid sequencing datasets has allowed scientists to examine the ability of SPM to classify primary cancer sites. These datasets are highly sparse since most genes will not be mutated, have low signal-to-noise ratio and are imbalanced since rare cancers have less samples. To overcome these limitations a sparse-input neural network (spinn) is suggested that projects the input data in a lower dimensional space, where the more informative genes are used for learning. To train and evaluate spinn, an extensive dataset was collected from the cancer genome atlas containing 7624 samples spanning 32 cancer types. Different sampling strategies were performed to balance the dataset but have not benefited the classifiers performance except for removing Tomek-links. This is probably due to high amount of class overlapping. Spinn consistently outperformed algorithms like extreme gradient-boosting, deep neural networks and support-vector-machines, achieving an accuracy up to 73% on independent testing data.


2021 ◽  
Author(s):  
Ahmed Samir Rizk ◽  
Moussa Tembely ◽  
Waleed AlAmeri ◽  
Emad W. Al-Shalabi

Abstract Estimation of petrophysical properties is essential for accurate reservoir predictions. In recent years, extensive work has been dedicated into training different machine-learning (ML) models to predict petrophysical properties of digital rock using dry rock images along with data from single-phase direct simulations, such as lattice Boltzmann method (LBM) and finite volume method (FVM). The objective of this paper is to present a comprehensive literature review on petrophysical properties estimation from dry rock images using different ML workflows and direct simulation methods. The review provides detailed comparison between different ML algorithms that have been used in the literature to estimate porosity, permeability, tortuosity, and effective diffusivity. In this paper, various ML workflows from the literature are screened and compared in terms of the training data set, the testing data set, the extracted features, the algorithms employed as well as their accuracy. A thorough description of the most commonly used algorithms is also provided to better understand the functionality of these algorithms to encode the relationship between the rock images and their respective petrophysical properties. The review of various ML workflows for estimating rock petrophysical properties from dry images shows that models trained using features extracted from the image (physics-informed models) outperformed models trained on the dry images directly. In addition, certain tree-based ML algorithms, such as random forest, gradient boosting, and extreme gradient boosting can produce accurate predictions that are comparable to deep learning algorithms such as deep neural networks (DNNs) and convolutional neural networks (CNNs). To the best of our knowledge, this is the first work dedicated to exploring and comparing between different ML frameworks that have recently been used to accurately and efficiently estimate rock petrophysical properties from images. This work will enable other researchers to have a broad understanding about the topic and help in developing new ML workflows or further modifying exiting ones in order to improve the characterization of rock properties. Also, this comparison represents a guide to understand the performance and applicability of different ML algorithms. Moreover, the review helps the researchers in this area to cope with digital innovations in porous media characterization in this fourth industrial age – oil and gas 4.0.


2020 ◽  
Vol 8 (5) ◽  
pp. 3061-3070

Healthcare has come a long way with the technological upgrades that it has witnessed and embraced. Technological disrupters like automation, big data, cloud, AI, IOT, have contributed in making the healthcare domain more enhanced and efficient. In the healthcare when we focus more on hospital related services, we see that the changes have started to reflect. Considering Nursing as one of the major services, this study attempts to understand the long ranging impact of AI and IOT on nursing specifically. Nursing as a choice of work has always been looked down upon by the society. The overall work environments, pay structures have also not been conducive for the work force. Being a crucial component of the healthcare domain, the impact of AI and IOT would be more pronounced and visible on nursing in the coming years. The paper attempts to find the gaps and areas of improvement in nursing to make it more efficient. Research Methods and Approach: The paper finds relevant attributes which impact the performance of the nurses. The attributes are derived using mixed method of research and triangulation. This is planned by conducting the systematic literature review followed first by qualitative research methodology based semi structured interviews of patients and their relatives and later by interviewing the doctors and nurses, hospital management. A Systematic literature review of minimum 75 to 100 research papers from the following databases Pubmed, Ieee, ACM, Science Direct and Google Scholar was planned. For the interviews it was decided to conduct a heterogeneous quota sampling for interviewing the patients, their relatives and doctors and nurses, hospital management. Purpose of the Study: Artificial intelligence and Internet-of-Things are the most path breaking techniques today69. The paper integrates these in the hospital domain focussing only “nursing” as service. It answers the crucial research questions like ‘If AI and IOT is used as a methodology in ”nursing Services” ,visualizing the impact of AI enabled Bots in nursing, pros and cons of introducing the AI technology intervention in Nursing and to conceptualize a model for the same.


Machines ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 351
Author(s):  
Lorenzo Colantonio ◽  
Lucas Equeter ◽  
Pierre Dehombreux ◽  
François Ducobu

In turning operations, the wear of cutting tools is inevitable. As workpieces produced with worn tools may fail to meet specifications, the machining industries focus on replacement policies that mitigate the risk of losses due to scrap. Several strategies, from empiric laws to more advanced statistical models, have been proposed in the literature. More recently, many monitoring systems based on Artificial Intelligence (AI) techniques have been developed. Due to the scope of different artificial intelligence approaches, having a holistic view of the state of the art on this subject is complex, in part due to a lack of recent comprehensive reviews. This literature review therefore presents 20 years of literature on this subject obtained following a Systematic Literature Review (SLR) methodology. This SLR aims to answer the following research question: “How is the AI used in the framework of monitoring/predicting the condition of tools in stable turning condition?” To answer this research question, the “Scopus” database was consulted in order to gather relevant publications published between 1 January 2000 and 1 January 2021. The systematic approach yielded 8426 articles among which 102 correspond to the inclusion and exclusion criteria which limit the application of AI to stable turning operation and online prediction. A bibliometric analysis performed on these articles highlighted the growing interest of this subject in the recent years. A more in-depth analysis of the articles is also presented, mainly focusing on six AI techniques that are highly represented in the literature: Artificial Neural Network (ANN), fuzzy logic, Support Vector Machine (SVM), Self-Organizing Map (SOM), Hidden Markov Model (HMM), and Convolutional Neural Network (CNN). For each technique, the trends in the inputs, pre-processing techniques, and outputs of the AI are presented. The trends highlight the early and continuous importance of ANN, and the emerging interest of CNN for tool condition monitoring. The lack of common benchmark database for evaluating models performance does not allow clear comparisons of technique performance.


2020 ◽  
Author(s):  
Avishek Choudhury ◽  
Onur Asan

BACKGROUND Artificial intelligence (AI) provides opportunities to identify the health risks of patients and thus influence patient safety outcomes. OBJECTIVE The purpose of this systematic literature review was to identify and analyze quantitative studies utilizing or integrating AI to address and report clinical-level patient safety outcomes. METHODS We restricted our search to the PubMed, PubMed Central, and Web of Science databases to retrieve research articles published in English between January 2009 and August 2019. We focused on quantitative studies that reported positive, negative, or intermediate changes in patient safety outcomes using AI apps, specifically those based on machine-learning algorithms and natural language processing. Quantitative studies reporting only AI performance but not its influence on patient safety outcomes were excluded from further review. RESULTS We identified 53 eligible studies, which were summarized concerning their patient safety subcategories, the most frequently used AI, and reported performance metrics. Recognized safety subcategories were clinical alarms (n=9; mainly based on decision tree models), clinical reports (n=21; based on support vector machine models), and drug safety (n=23; mainly based on decision tree models). Analysis of these 53 studies also identified two essential findings: (1) the lack of a standardized benchmark and (2) heterogeneity in AI reporting. CONCLUSIONS This systematic review indicates that AI-enabled decision support systems, when implemented correctly, can aid in enhancing patient safety by improving error detection, patient stratification, and drug management. Future work is still needed for robust validation of these systems in prospective and real-world clinical environments to understand how well AI can predict safety outcomes in health care settings.


2021 ◽  
Vol 7 (12) ◽  
pp. 120974-120995
Author(s):  
Augusto Vinícius da Silva ◽  
Gabriel Caldas Barros e Sá ◽  
Samara Martins Nascimento ◽  
Náthalee Cavalcanti de Almeida Lima

The COVID-19 pandemic has been a challenge to world health, and Brazil has been ranked among the first countries with most deaths and cumulative confirmed cases. Among several tools, Artificial Intelligence (AI) has been used to identify solutions that may predict events related to COVID-19. Thus, this work is a Systematic Literature Review that aims to present an overview of researches that use AI to forecast events and data related to the COVID-19 in Brazil. The methodology of this study was based on the protocol presented by Kitchenham and Charters and the results show that the main techniques used to forecast events related to the coronavirus by the time this work was conducted are ARIMA, SVR and Neural Networks, besides, this study also details information such as the most used attributes and the most sought objectives in the prediction. Based on the results obtained, it can be said that few significant studies seek to forecast the progress of coronavirus in Brazil and, therefore, the present study contributes to the grouped analysis of the techniques and solutions discovered, in addition to providing possible directions for future studies.


10.2196/18599 ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. e18599 ◽  
Author(s):  
Avishek Choudhury ◽  
Onur Asan

Background Artificial intelligence (AI) provides opportunities to identify the health risks of patients and thus influence patient safety outcomes. Objective The purpose of this systematic literature review was to identify and analyze quantitative studies utilizing or integrating AI to address and report clinical-level patient safety outcomes. Methods We restricted our search to the PubMed, PubMed Central, and Web of Science databases to retrieve research articles published in English between January 2009 and August 2019. We focused on quantitative studies that reported positive, negative, or intermediate changes in patient safety outcomes using AI apps, specifically those based on machine-learning algorithms and natural language processing. Quantitative studies reporting only AI performance but not its influence on patient safety outcomes were excluded from further review. Results We identified 53 eligible studies, which were summarized concerning their patient safety subcategories, the most frequently used AI, and reported performance metrics. Recognized safety subcategories were clinical alarms (n=9; mainly based on decision tree models), clinical reports (n=21; based on support vector machine models), and drug safety (n=23; mainly based on decision tree models). Analysis of these 53 studies also identified two essential findings: (1) the lack of a standardized benchmark and (2) heterogeneity in AI reporting. Conclusions This systematic review indicates that AI-enabled decision support systems, when implemented correctly, can aid in enhancing patient safety by improving error detection, patient stratification, and drug management. Future work is still needed for robust validation of these systems in prospective and real-world clinical environments to understand how well AI can predict safety outcomes in health care settings.


Sign in / Sign up

Export Citation Format

Share Document