Advances in Computing, Communication & Security

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 40449

Special Issue Editors

1. Department of Electrical and Computer Engineering, Concordia University, Montreal, QC H3G 1M8, Canada
2. College of Engineering and Information Technology, University of Dubai, Dubai 14143, United Arab Emirates
Interests: electrical and computer engineering
Special Issues, Collections and Topics in MDPI journals
Bipin Tripathi Kumaon Institute of Technology, Dwarahat 263653, India
Interests: soft computing in data mining; information security with reference to privacy, security, spam filtration; data scheduling approaches in vehicular ad hoc networks from the points of view of hardware, computational paradigms, and computing applications

Special Issue Information

Dear Colleagues,

This Special Issue will present extended versions of selected papers presented at the 8th International Conference on Computing Communication and Security (ICCCS 2023), the annual ICCCS conference is for the computer, communication and signal processing fields. ICCCS 2023 is to be held in India during March 03-04, 2023. Initiated in 2014, ICACCE will provide a forum for researchers and engineers in both academia and industry to exchange the latest innovations and research advancements in Innovative Computing, Communication and Engineering. The conference aims at bringing together researchers and practitioners in the world working on computing, communications and security aspects of communication, networks, and signal processing, providing a forum to present and discuss emerging ideas and trends in this highly challenging research field. Several pioneer researchers including IEEE fellows and industrialist persons will be present for delivering future research directions.

However, for ICCCS 2023, we would like to put an emphasis on deep learning, as it has been used successfully in many applications, and is currently considered one of the most cutting-edge machine learning and AI techniques. For ICACCE 2023, we would like to put an emphasis on identifying the applications of deep learning, machine learning and artificial intelligence on emerging research topics, as well as the future development directions in the field of computing and communication engineering.

Authors of invited papers should be aware that the final submitted manuscript must provide a minimum of 50% new content and not exceed 30% copy/paste from the proceedings paper.

Dr. Amjad Gawanmeh
Dr. Vishal Kumar
Guest Editors

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. Information is an international peer-reviewed open access monthly 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 1600 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

  • deep learning
  • machine learning
  • data mining
  • natural language processing
  • planning
  • knowledge representation
  • multi-agent systems
  • robotics
  • image processing

Published Papers (15 papers)

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Research

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11 pages, 1018 KiB  
Article
Association between Obesity and COVID-19: Insights from Social Media Content
by Mohammed Alotaibi, Rajesh R. Pai, Sreejith Alathur, Naganna Chetty, Tareq Alhmiedat, Majed Aborokbah, Umar Albalawi, Ashraf Marie, Anas Bushnag and Vishal Kumar
Information 2023, 14(8), 448; https://doi.org/10.3390/info14080448 - 08 Aug 2023
Viewed by 1126
Abstract
The adoption of emerging technologies in healthcare systems plays a crucial part in anti-obesity initiatives. COVID-19 has intensified the Body Mass Index (BMI) discourses in AI (Artificial Intelligence)-powered social media. However, few studies have reported on the influence of digital content on obesity [...] Read more.
The adoption of emerging technologies in healthcare systems plays a crucial part in anti-obesity initiatives. COVID-19 has intensified the Body Mass Index (BMI) discourses in AI (Artificial Intelligence)-powered social media. However, few studies have reported on the influence of digital content on obesity prevention policies. Understanding the nature and forums of obese metaphors in social media is the first step in policy intervention. The purpose of this paper is to understand the mutual influence between obesity and COVID-19 and determine its policy implications. This paper analyzes the public responses to obesity using Twitter data collected during the COVID-19 pandemic. The emotional nature of tweets is analyzed using the NRC lexicon. The results show that COVID-19 significantly influences perceptions of obesity; this indicates that existing public health policies must be revisited. The study findings delineate prerequisites for obese disease control programs. This paper provides policy recommendations for improving social media interventions in health service delivery in order to prevent obesity. Full article
(This article belongs to the Special Issue Advances in Computing, Communication & Security)
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18 pages, 1069 KiB  
Article
A Blockchain-Based Efficient and Verifiable Attribute-Based Proxy Re-Encryption Cloud Sharing Scheme
by Tao Feng, Dewei Wang and Renbin Gong
Information 2023, 14(5), 281; https://doi.org/10.3390/info14050281 - 09 May 2023
Cited by 2 | Viewed by 1710
Abstract
When choosing a third-party cloud storage platform, the confidentiality of data should be the primary concern. To address the issue of one-to-many access control during data sharing, it is important to encrypt data with an access policy that enables fine-grained access. The attribute-based [...] Read more.
When choosing a third-party cloud storage platform, the confidentiality of data should be the primary concern. To address the issue of one-to-many access control during data sharing, it is important to encrypt data with an access policy that enables fine-grained access. The attribute-based encryption scheme can be used for this purpose. Additionally, attribute-based proxy re-encryption (ABPRE) can generate a secret key using the delegatee’s secret key and access policy to re-encrypt the ciphertext, allowing for one-to-many data sharing. However, this scheme still has some flaws, such as low efficiency, inability to update access rules, and private data leakage. To address these issues, we proposed a scheme that combines attribute-based encryption (ABE) and identity-based encryption (IBE) to achieve efficient data sharing and data correctness verification. We also integrated this scheme with blockchain technology to ensure tamper-proof and regulated data storage, addressing issues such as data tampering and lack of supervision on third-party servers. Finally, to demonstrate the security of our scheme, we evaluated the communication overhead and computation overhead. Our results showed that our scheme is more efficient than other schemes and is secure against chosen plaintext attacks with verifiable properties. Full article
(This article belongs to the Special Issue Advances in Computing, Communication & Security)
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22 pages, 6616 KiB  
Article
Continuous User Authentication on Multiple Smart Devices
by Yajie Wang, Xiaomei Zhang and Haomin Hu
Information 2023, 14(5), 274; https://doi.org/10.3390/info14050274 - 05 May 2023
Cited by 1 | Viewed by 2017
Abstract
Recent developments in the mobile and intelligence industry have led to an explosion in the use of multiple smart devices such as smartphones, tablets, smart bracelets, etc. To achieve lasting security after initial authentication, many studies have been conducted to apply user authentication [...] Read more.
Recent developments in the mobile and intelligence industry have led to an explosion in the use of multiple smart devices such as smartphones, tablets, smart bracelets, etc. To achieve lasting security after initial authentication, many studies have been conducted to apply user authentication through behavioral biometrics. However, few of them consider continuous user authentication on multiple smart devices. In this paper, we investigate user authentication from a new perspective—continuous authentication on multi-devices, that is, continuously authenticating users after both initial access to one device and transfer to other devices. In contrast to previous studies, we propose a continuous user authentication method that exploits behavioral biometric identification on multiple smart devices. In this study, we consider the sensor data captured by accelerometer and gyroscope sensors on both smartphones and tablets. Furthermore, multi-device behavioral biometric data are utilized as the input of our optimized neural network model, which combines a convolutional neural network (CNN) and a long short-term memory (LSTM) network. In particular, we construct two-dimensional domain images to characterize the underlying features of sensor signals between different devices and then input them into our network for classification. In order to strengthen the effectiveness and efficiency of authentication on multiple devices, we introduce an adaptive confidence-based strategy by taking historical user authentication results into account. This paper evaluates the performance of our multi-device continuous user authentication mechanism under different scenarios, and extensive empirical results demonstrate its feasibility and efficiency. Using the mechanism, we achieved mean accuracies of 99.8% and 99.2% for smartphones and tablets, respectively, in approximately 2.3 s, which shows that it authenticates users accurately and quickly. Full article
(This article belongs to the Special Issue Advances in Computing, Communication & Security)
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23 pages, 1086 KiB  
Article
IoT-Enabled Precision Agriculture: Developing an Ecosystem for Optimized Crop Management
by Shadi Atalla, Saed Tarapiah, Amjad Gawanmeh, Mohammad Daradkeh, Husameldin Mukhtar, Yassine Himeur, Wathiq Mansoor, Kamarul Faizal Bin Hashim and Motaz Daadoo
Information 2023, 14(4), 205; https://doi.org/10.3390/info14040205 - 27 Mar 2023
Cited by 21 | Viewed by 5575
Abstract
The Internet of Things (IoT) has the potential to revolutionize agriculture by providing real-time data on crop and livestock conditions. This study aims to evaluate the performance scalability of wireless sensor networks (WSNs) in agriculture, specifically in two scenarios: monitoring olive tree farms [...] Read more.
The Internet of Things (IoT) has the potential to revolutionize agriculture by providing real-time data on crop and livestock conditions. This study aims to evaluate the performance scalability of wireless sensor networks (WSNs) in agriculture, specifically in two scenarios: monitoring olive tree farms and stables for horse training. The study proposes a new classification approach of IoT in agriculture based on several factors and introduces performance assessment metrics for stationary and mobile scenarios in 6LowPAN networks. The study utilizes COOJA, a realistic WSN simulator, to model and simulate the performance of the 6LowPAN and Routing protocol for low-power and lossy networks (RPL) in the two farming scenarios. The simulation settings for both fixed and mobile nodes are shared, with the main difference being node mobility. The study characterizes different aspects of the performance requirements in the two farming scenarios by comparing the average power consumption, radio duty cycle, and sensor network graph connectivity degrees. A new approach is proposed to model and simulate moving animals within the COOJA simulator, adopting the random waypoint model (RWP) to represent horse movements. The results show the advantages of using the RPL protocol for routing in mobile and fixed sensor networks, which supports dynamic topologies and improves the overall network performance. The proposed framework is experimentally validated and tested through simulation, demonstrating the suitability of the proposed framework for both fixed and mobile scenarios, providing efficient communication performance and low latency. The results have several practical implications for precision agriculture by providing an efficient monitoring and management solution for agricultural and livestock farms. Overall, this study provides a comprehensive evaluation of the performance scalability of WSNs in the agriculture sector, offering a new classification approach and performance assessment metrics for stationary and mobile scenarios in 6LowPAN networks. The results demonstrate the suitability of the proposed framework for precision agriculture, providing efficient communication performance and low latency. Full article
(This article belongs to the Special Issue Advances in Computing, Communication & Security)
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14 pages, 501 KiB  
Article
MLP-Mixer-Autoencoder: A Lightweight Ensemble Architecture for Malware Classification
by Tuan Van Dao, Hiroshi Sato and Masao Kubo
Information 2023, 14(3), 167; https://doi.org/10.3390/info14030167 - 06 Mar 2023
Cited by 1 | Viewed by 2100
Abstract
Malware is becoming an effective support tool not only for professional hackers but also for amateur ones. Due to the support of free malware generators, anyone can easily create various types of malicious code. The increasing amount of novel malware is a daily [...] Read more.
Malware is becoming an effective support tool not only for professional hackers but also for amateur ones. Due to the support of free malware generators, anyone can easily create various types of malicious code. The increasing amount of novel malware is a daily global problem. Current machine learning-based methods, especially image-based malware classification approaches, are attracting significant attention because of their accuracy and computational cost. Convolutional Neural Networks are widely applied in malware classification; however, CNN needs a deep architecture and GPUs for parallel processing to achieve high performance. By contrast, a simple model merely contained a Multilayer Perceptron called MLP-mixer with fewer hyperparameters that can run in various environments without GPUs and is not too far behind CNN in terms of performance. In this study, we try applying an Autoencoder (AE) to improve the performance of the MLP-mixer. AE is widely used in several applications as dimensionality reduction to filter out the noise and identify crucial elements of the input data. Taking this advantage from AE, we propose a lightweight ensemble architecture by combining a customizer MLP-mixer and Autoencoder to refine features extracted from the MLP-mixer with the encoder-decoder architecture of the autoencoder. We achieve overperformance through various experiments compared to other cutting-edge techniques using Malimg and Malheur datasets which contain 9939 (25 malware families) and 3133 variant samples (24 malware families). Full article
(This article belongs to the Special Issue Advances in Computing, Communication & Security)
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17 pages, 2631 KiB  
Article
CSK-CNN: Network Intrusion Detection Model Based on Two-Layer Convolution Neural Network for Handling Imbalanced Dataset
by Jiaming Song, Xiaojuan Wang, Mingshu He and Lei Jin
Information 2023, 14(2), 130; https://doi.org/10.3390/info14020130 - 16 Feb 2023
Cited by 3 | Viewed by 1663
Abstract
In computer networks, Network Intrusion Detection System (NIDS) plays a very important role in identifying intrusion behaviors. NIDS can identify abnormal behaviors by analyzing network traffic. However, the performance of classifier is not very good in identifying abnormal traffic for minority classes. In [...] Read more.
In computer networks, Network Intrusion Detection System (NIDS) plays a very important role in identifying intrusion behaviors. NIDS can identify abnormal behaviors by analyzing network traffic. However, the performance of classifier is not very good in identifying abnormal traffic for minority classes. In order to improve the detection rate on class imbalanced dataset, we propose a network intrusion detection model based on two-layer CNN and Cluster-SMOTE + K-means algorithm (CSK-CNN) to process imbalanced dataset. CSK combines the cluster based Synthetic Minority Over Sampling Technique (Cluster-SMOTE) and K-means based under sampling algorithm. Through the two-layer network, abnormal traffic can not only be identified, but also be classified into specific attack types. This paper has been verified on UNSW-NB15 dataset and CICIDS2017 dataset, and the performance of the proposed model has been evaluated using such indicators as accuracy, recall, precision, F1-score, ROC curve, AUC value, training time and testing time. The experiment shows that the proposed CSK-CNN in this paper is obviously superior to other comparison algorithms in terms of network intrusion detection performance, and is suitable for deployment in the real network environment. Full article
(This article belongs to the Special Issue Advances in Computing, Communication & Security)
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14 pages, 2181 KiB  
Article
A Comparative Analysis of Supervised and Unsupervised Models for Detecting Attacks on the Intrusion Detection Systems
by Tala Talaei Khoei and Naima Kaabouch
Information 2023, 14(2), 103; https://doi.org/10.3390/info14020103 - 07 Feb 2023
Cited by 13 | Viewed by 3735
Abstract
Intrusion Detection Systems are expected to detect and prevent malicious activities in a network, such as a smart grid. However, they are the main systems targeted by cyber-attacks. A number of approaches have been proposed to classify and detect these attacks, including supervised [...] Read more.
Intrusion Detection Systems are expected to detect and prevent malicious activities in a network, such as a smart grid. However, they are the main systems targeted by cyber-attacks. A number of approaches have been proposed to classify and detect these attacks, including supervised machine learning. However, these models require large labeled datasets for training and testing. Therefore, this paper compares the performance of supervised and unsupervised learning models in detecting cyber-attacks. The benchmark of CICDDOS 2019 was used to train, test, and validate the models. The supervised models are Gaussian Naïve Bayes, Classification and Regression Decision Tree, Logistic Regression, C-Support Vector Machine, Light Gradient Boosting, and Alex Neural Network. The unsupervised models are Principal Component Analysis, K-means, and Variational Autoencoder. The performance comparison is made in terms of accuracy, probability of detection, probability of misdetection, probability of false alarm, processing time, prediction time, training time per sample, and memory size. The results show that the Alex Neural Network model outperforms the other supervised models, while the Variational Autoencoder model has the best results compared to unsupervised models. Full article
(This article belongs to the Special Issue Advances in Computing, Communication & Security)
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15 pages, 1516 KiB  
Article
Secure Medical Blockchain Model
by Ibrahim Shawky Farahat, Waleed Aladrousy, Mohamed Elhoseny, Samir Elmougy and Ahmed Elsaid Tolba
Information 2023, 14(2), 80; https://doi.org/10.3390/info14020080 - 30 Jan 2023
Cited by 3 | Viewed by 1617
Abstract
The Internet of Medical Things (IoMT) uses wireless networks to help patients to communicate with healthcare professionals. Therefore, IoMT devices suffer from a lack of security controls, just like many Internet of Things (IoT) gadgets. Thus, in this paper, we develop a system [...] Read more.
The Internet of Medical Things (IoMT) uses wireless networks to help patients to communicate with healthcare professionals. Therefore, IoMT devices suffer from a lack of security controls, just like many Internet of Things (IoT) gadgets. Thus, in this paper, we develop a system that uses a blockchain to secure medical data for each transaction between physicians and patients. This system also helps the physician to send the treatment to the blockchain. The blockchain creates a new block for the treatment and connects it with the previous block. This system also helps patients to access their treatment through the blockchain. SHA-256 is used to hash the new block using some information about the last block. We modify SHA-256 using the LZ4 algorithm to compress data. We also prevent a new block hash code starting with a specific number of zeros, which made the proposed system give a time complexity better than all related work. In this paper, we also develop a party-authentication technique that ensures the two parties of the transaction. The proposed system makes a transaction with O(n) time complexity. Thus, our system takes 1 s to create a block for the transaction. We also make a green computing algorithm comparison between our proposed system and the blockchain version. This comparison proves that our proposed method consumes less energy to create a new block. This paper proves that our method performs better than all previous blockchain versions. Full article
(This article belongs to the Special Issue Advances in Computing, Communication & Security)
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23 pages, 2732 KiB  
Article
A Closer Look at Machine Learning Effectiveness in Android Malware Detection
by Filippos Giannakas, Vasileios Kouliaridis and Georgios Kambourakis
Information 2023, 14(1), 2; https://doi.org/10.3390/info14010002 - 21 Dec 2022
Cited by 5 | Viewed by 2232
Abstract
Nowadays, with the increasing usage of Android devices in daily life activities, malware has been increasing rapidly, putting peoples’ security and privacy at risk. To mitigate this threat, several researchers have proposed different methods to detect Android malware. Recently, machine learning based models [...] Read more.
Nowadays, with the increasing usage of Android devices in daily life activities, malware has been increasing rapidly, putting peoples’ security and privacy at risk. To mitigate this threat, several researchers have proposed different methods to detect Android malware. Recently, machine learning based models have been explored by a significant mass of researchers checking for Android malware. However, selecting the most appropriate model is not straightforward, since there are several aspects that must be considered. Contributing to this domain, the current paper explores Android malware detection from diverse perspectives; this is achieved by optimizing and evaluating various machine learning algorithms. Specifically, we conducted an experiment for training, optimizing, and evaluating 27 machine learning algorithms, and a Deep Neural Network (DNN). During the optimization phase, we performed hyperparameter analysis using the Optuna framework. The evaluation phase includes the measurement of different performance metrics against a contemporary, rich dataset, to conclude with the most accurate model. The best model was further interpreted by conducting feature analysis, using the Shapley Additive Explanations (SHAP) framework. Our experiment results showed that the best model is the DNN consisting of four layers (two hidden), using the Adamax optimizer, as well as the Binary Cross-Entropy (loss), and the Softsign activation functions. The model succeeded with 86% prediction accuracy, while the balanced accuracy, the F1-score, and the ROC-AUC metrics were at 82%. Full article
(This article belongs to the Special Issue Advances in Computing, Communication & Security)
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13 pages, 2105 KiB  
Article
Anomaly Detection Approach in Industrial Control Systems Based on Measurement Data
by Xiaosong Zhao, Lei Zhang, Yixin Cao, Kai Jin and Yupeng Hou
Information 2022, 13(10), 450; https://doi.org/10.3390/info13100450 - 25 Sep 2022
Cited by 4 | Viewed by 2815
Abstract
Anomaly detection problems in industrial control systems (ICSs) are always tackled by a network traffic monitoring scheme. However, traffic-based anomaly detection systems may be deceived by anomalous behaviors that mimic normal system activities and fail to achieve effective anomaly detection. In this work, [...] Read more.
Anomaly detection problems in industrial control systems (ICSs) are always tackled by a network traffic monitoring scheme. However, traffic-based anomaly detection systems may be deceived by anomalous behaviors that mimic normal system activities and fail to achieve effective anomaly detection. In this work, we propose a novel solution to this problem based on measurement data. The proposed method combines a one-dimensional convolutional neural network (1DCNN) and a bidirectional long short-term memory network (BiLSTM) and uses particle swarm optimization (PSO), which is called PSO-1DCNN-BiLSTM. It enables the system to detect any abnormal activity in the system, even if the attacker tries to conceal it in the system’s control layer. A supervised deep learning model was generated to classify normal and abnormal activities in an ICS to evaluate the method’s performance. This model was trained and validated against the open-source simulated power system dataset from Mississippi State University. In the proposed approach, we applied several deep-learning models to the dataset, which showed remarkable performance in detecting the dataset’s anomalies, especially stealthy attacks. The results show that PSO-1DCNN-BiLSTM performed better than other classifier algorithms in detecting anomalies based on measured data. Full article
(This article belongs to the Special Issue Advances in Computing, Communication & Security)
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16 pages, 3021 KiB  
Article
An Intrusion Detection Method for Industrial Control System Based on Machine Learning
by Yixin Cao, Lei Zhang, Xiaosong Zhao, Kai Jin and Ziyi Chen
Information 2022, 13(7), 322; https://doi.org/10.3390/info13070322 - 03 Jul 2022
Cited by 6 | Viewed by 2830
Abstract
The integration of communication networks and the internet of industrial control in Industrial Control System (ICS) increases their vulnerability to cyber attacks, causing devastating outcomes. Traditional Intrusion Detection Systems (IDS) largely rely on predefined models and are trained mostly on specific cyber attacks, [...] Read more.
The integration of communication networks and the internet of industrial control in Industrial Control System (ICS) increases their vulnerability to cyber attacks, causing devastating outcomes. Traditional Intrusion Detection Systems (IDS) largely rely on predefined models and are trained mostly on specific cyber attacks, which means the traditional IDS cannot cope with unknown attacks. Additionally, most IDS do not consider the imbalanced nature of ICS datasets, thus suffering from low accuracy and high False Positive Rates when being put to use. In this paper, we propose the NCO–double-layer DIFF_RF–OPFYTHON intrusion detection method for ICS, which consists of NCO modules, double-layer DIFF_RF modules, and OPFYTHON modules. Detected traffic will be divided into three categories by the double-layer DIFF_RF module: known attacks, unknown attacks, and normal traffic. Then, the known attacks will be classified into specific attacks by the OPFYTHON module according to the feature of attack traffic. Finally, we use the NCO module to improve the model input and enhance the accuracy of the model. The results show that the proposed method outperforms traditional intrusion detection methods, such as XGboost and SVM. The detection of unknown attacks is also considerable. The accuracy of the dataset used in this paper reaches 98.13%. The detection rates for unknown attacks and known attacks reach 98.21% and 95.1%, respectively. Moreover, the method we proposed has achieved suitable results on other public datasets. Full article
(This article belongs to the Special Issue Advances in Computing, Communication & Security)
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11 pages, 524 KiB  
Article
LoRaWAN Based Indoor Localization Using Random Neural Networks
by Winfred Ingabire, Hadi Larijani, Ryan M. Gibson and Ayyaz-UI-Haq Qureshi
Information 2022, 13(6), 303; https://doi.org/10.3390/info13060303 - 16 Jun 2022
Cited by 10 | Viewed by 2466
Abstract
Global Positioning Systems (GPS) are frequently used as a potential solution for localization applications. However, GPS does not work indoors due to a lack of direct Line-of-Sight (LOS) satellite signals received from the End Device (ED) due to thick solid materials blocking the [...] Read more.
Global Positioning Systems (GPS) are frequently used as a potential solution for localization applications. However, GPS does not work indoors due to a lack of direct Line-of-Sight (LOS) satellite signals received from the End Device (ED) due to thick solid materials blocking the ultra-high frequency signals. Furthermore, fingerprint localization using Received Signal Strength Indicator (RSSI) values is typical for localization in indoor environments. Therefore, this paper develops a low-power intelligent localization system for indoor environments using Long-Range Wide-Area Networks (LoRaWAN) RSSI values with Random Neural Networks (RNN). The proposed localization system demonstrates 98.5% improvement in average localization error compared to related studies with a minimum average localization error of 0.12 m in the Line-of-Sight (LOS). The obtained results confirm LoRaWAN-RNN-based localization systems suitable for indoor environments in LOS applied in big sports halls, hospital wards, shopping malls, airports, and many more with the highest accuracy of 99.52%. Furthermore, a minimum average localization error of 13.94 m was obtained in the Non-Line-of-Sight (NLOS) scenario, and this result is appropriate for the management and control of vehicles in indoor car parks, industries, or any other fleet in a pre-defined area in the NLOS with the highest accuracy of 44.24%. Full article
(This article belongs to the Special Issue Advances in Computing, Communication & Security)
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12 pages, 539 KiB  
Article
An Accurate Detection Approach for IoT Botnet Attacks Using Interpolation Reasoning Method
by Mohammad Almseidin and Mouhammd Alkasassbeh
Information 2022, 13(6), 300; https://doi.org/10.3390/info13060300 - 14 Jun 2022
Cited by 4 | Viewed by 1912
Abstract
Nowadays, the rapid growth of technology delivers many new concepts and notations that aim to increase the efficiency and comfort of human life. One of these techniques is the Internet of Things (IoT). The IoT has been used to achieve efficient operation management, [...] Read more.
Nowadays, the rapid growth of technology delivers many new concepts and notations that aim to increase the efficiency and comfort of human life. One of these techniques is the Internet of Things (IoT). The IoT has been used to achieve efficient operation management, cost-effective operations, better business opportunities, etc. However, there are many challenges facing implementing an IoT smart environment. The most critical challenge is protecting the IoT smart environment from different attacks. The IoT Botnet attacks are considered a serious challenge. The danger of this attack lies in that it could be used for several threatening commands. Therefore, the Botnet attacks could be implemented to perform the DDoS attacks, phishing attacks, spamming, and other attack scenarios. This paper has introduced a detection approach against the IoT Botnet attacks using the interpolation reasoning method. The suggested detection approach was implemented using the interpolation reasoning method instead of the classical reasoning methods to handle the knowledge base issues and reduce the size of the detection fuzzy rules. The suggested detection approach was designed, tested, and evaluated using an open-source benchmark IoT Botnet attacks dataset. The implemented experiments show that the suggested detection approach was able to detect the IoT Botnet attacks effectively with a 96.4% detection rate. Furthermore, the obtained results were compared with other literature results; the accomplished comparison showed that the suggested method is a rivalry with other methods, and it effectively reduced the false positive rate and interpolated the IoT Botnet attacks alerts even in case of a sparse rule base. Full article
(This article belongs to the Special Issue Advances in Computing, Communication & Security)
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17 pages, 501 KiB  
Article
A Comparison of Machine Learning Techniques for the Quality Classification of Molded Products
by Andrea Polenta, Selene Tomassini, Nicola Falcionelli, Paolo Contardo, Aldo Franco Dragoni and Paolo Sernani
Information 2022, 13(6), 272; https://doi.org/10.3390/info13060272 - 26 May 2022
Cited by 8 | Viewed by 3525
Abstract
The developments in the internet of things (IoT), artificial intelligence (AI), and cyber-physical systems (CPS) are paving the way to the implementation of smart factories in what is commonly recognized as the fourth industrial revolution. In the manufacturing sector, these technological advancements are [...] Read more.
The developments in the internet of things (IoT), artificial intelligence (AI), and cyber-physical systems (CPS) are paving the way to the implementation of smart factories in what is commonly recognized as the fourth industrial revolution. In the manufacturing sector, these technological advancements are making Industry 4.0 a reality, with data-driven methodologies based on machine learning (ML) that are capable of extracting knowledge from the data collected by sensors placed on production machines. This is particularly relevant in plastic injection molding, with the objective of monitoring the quality of molded products from the parameters of the production process. In this regard, the main contribution of this paper is the systematic comparison of ML techniques to predict the quality classes of plastic molded products, using real data collected during the production process. Specifically, we compare six different classifiers on the data coming from the production of plastic road lenses. To run the comparison, we collected a dataset composed of the process parameters of 1451 road lenses. On such samples, we tested a multi-class classification, providing a statistical analysis of the results as well as of the importance of the input features. Among the tested classifiers, the ensembles of decision trees, i.e., random forest and gradient-boosted trees (GBT), achieved 95% accuracy in predicting the quality classes of molded products, showing the viability of the use of ML-based techniques for this purpose. The collected dataset and the source code of the experiments are available in a public, open-access repository, making the presented research fully reproducible. Full article
(This article belongs to the Special Issue Advances in Computing, Communication & Security)
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Review

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19 pages, 636 KiB  
Review
A Survey on Feature Selection Techniques Based on Filtering Methods for Cyber Attack Detection
by Yang Lyu, Yaokai Feng and Kouichi Sakurai
Information 2023, 14(3), 191; https://doi.org/10.3390/info14030191 - 17 Mar 2023
Cited by 12 | Viewed by 2931
Abstract
Cyber attack detection technology plays a vital role today, since cyber attacks have been causing great harm and loss to organizations and individuals. Feature selection is a necessary step for many cyber-attack detection systems, because it can reduce training costs, improve detection performance, [...] Read more.
Cyber attack detection technology plays a vital role today, since cyber attacks have been causing great harm and loss to organizations and individuals. Feature selection is a necessary step for many cyber-attack detection systems, because it can reduce training costs, improve detection performance, and make the detection system lightweight. Many techniques related to feature selection for cyber attack detection have been proposed, and each technique has advantages and disadvantages. Determining which technology should be selected is a challenging problem for many researchers and system developers, and although there have been several survey papers on feature selection techniques in the field of cyber security, most of them try to be all-encompassing and are too general, making it difficult for readers to grasp the concrete and comprehensive image of the methods. In this paper, we survey the filter-based feature selection technique in detail and comprehensively for the first time. The filter-based technique is one popular kind of feature selection technique and is widely used in both research and application. In addition to general descriptions of this kind of method, we also explain in detail search algorithms and relevance measures, which are two necessary technical elements commonly used in the filter-based technique. Full article
(This article belongs to the Special Issue Advances in Computing, Communication & Security)
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