Recurrent Neural Networks are powerful machine learning frameworks that allow for data to be saved and referenced in a temporal sequence. This opens many new possibilities in fields such as handwriting analysis and speech recognition. This paper seeks to explore current research being conducted on RNNs in four very important areas, being biometric authentication, expression recognition, anomaly detection, and applications to aircraft. This paper reviews the methodologies, purpose, results, and the benefits and drawbacks of each proposed method below. These various methodologies all focus on how they can leverage distinct RNN architectures such as the popular Long Short-Term Memory (LSTM) RNN or a Deep-Residual RNN. This paper also examines which frameworks work best in certain situations, and the advantages and disadvantages of each proposed model.

Applications of Recurrent Neural Network for Biometric Authentication & Anomaly Detection

IoT devices generate enormous amounts of data, which deep learning algorithms can learn from more effectively than shallow learning algorithms. The approach for threat detection may ultimately benefit fog computing or fog networking (fogging). The authors present a cutting-edge distributed DL method for detecting cyberattacks and vulnerability injection (CAVID) in this paper. In terms of the evaluation metrics tested in the tests, the DL model performs better than the SL models. They demonstrated a distributed DL-driven fog computing CAVID approach using the open-source NSL-KDD dataset. A pre-trained SAE was utilised for feature engineering, whereas Softmax was employed for categorization. They used parametric evaluation for system assessment to evaluate the model in comparison to SL techniques. For scalability, accuracy across several worker nodes was taken into consideration. In addition to the robustness, effectiveness, and optimization of distributed parallel learning among fog nodes for enhancing accuracy, the findings demonstrate DL models exceeding classic ML architectures.

/pdf/intelligent-fog-computing-surveillance-system-for-crime-and-1zy505gy.pdf

Intelligent Fog Computing Surveillance System for Crime and Vulnerability Identification and Tracing

Nowadays, there is a growing demand for information security and security rules all across the world. Intrusion detection (ID) is a critical technique for detecting dangers in a network during data transmission. Artificial Intelligence (AI) methods support the Internet of Things (IoT) and smart cities by creating gadgets replicating intelligent behavior and enabling decision making with little or no human intervention. This research proposes novel technique for secure data transmission and detecting an intruder in a biometric authentication system by feature extraction with classification. Here, an intruder is detected by collecting the biometric database of the smart building based on the IoT. These biometric data are processed for noise removal, smoothening, and normalization. The processed data features are extracted using the kernel-based principal component analysis (KPCA). Then, the processed features are classified using the convolutional VGG−16 Net architecture. Then, the entire network is secured using a deterministic trust transfer protocol (DTTP). The suggested technique’s performance was calculated utilizing several measures, such as the accuracy, f-score, precision, recall, and RMSE. The simulation results revealed that the proposed method provides better intrusion detection outcomes.

/pdf/biometric-authentication-based-intrusion-detection-using-vz42z4d6.pdf

Biometric Authentication-Based Intrusion Detection Using Artificial Intelligence Internet of Things in Smart City

Cyber Loopholes in smart devices' applications invited intruders to conduct malicious activities. The growing quantity and diversity of smart devices has posed significant cyber security concerns. Cloud computing centralization makes it difficult to provide dispersed vulnerability tracing services for IoT designs at network's edge. Despite the existing encryption methods on the conventional Internet, the inevitability of detection mechanisms has been demonstrated by growing attack surfaces and hacking talents. Machine Intelligence focused threat detection mechanisms have demonstrated lower accuracy and scalability for threat detection in massively dispersed nodes -Internet of things. Modern Deep Learning may pave the way for identifying the present level of complexity of vulnerabilities towards edge networks and has proven effectiveness in large data applications. Due to huge quantity of data created by Internet of things Nodes, Deep Learning can learn better than Shallow Learning algorithms. Fog computing or fog networking (fogging) might be the ultimate benefactor of the method for threat detection. Here we present a novel distributed Deep Learning technique for cyber attack and vulnerability injection detection. The Deep Learning model outperforms Shallow Learning models in terms of assessment parameters evaluated in the experiments. 

Rooted Learning Model at Fog Computing Analysis for Crime Incident Surveillance

In recent years, the amount of secure information being stored on mobile devices has grown exponentially. However, current security schemas for mobile devices such as physiological biometrics and passwords are not secure enough to protect this information. Behavioral biometrics have been heavily researched as a possible solution to this security deficiency for mobile devices. This study aims to contribute to this innovative research by evaluating the performance of a multi-modal behavioral biometric based user authentication scheme using touch dynamics and phone movement. This study uses a fusion of two popular publicly available datasets - the Hand Movement Orientation and Grasp (HMOG) dataset and the BioIdent dataset. This study evaluates our model&#x0027;s performance using three common machine learning algorithms; Random Forest, Support Vector Machine, and K-Nearest Neighbor reaching accuracy rates as high as 82&#x0025;, with each algorithm performing respectively for all success metrics reported. 

/pdf/hold-on-and-swipe-a-touch-movement-based-continuous-390pifbc.pdf

Hold On and Swipe: A Touch-Movement Based Continuous Authentication Schema based on Machine Learning

Since the beginning of the Internet of Things (IoT), the number of IoT devices connected to the Internet has grown rapidly. However, many IoT devices lack the security standards that non-IoT devices have. This means that billions of smart devices could be used as part of a botnet attack or point of entry into a secured network. The potential to exploit an IoT device makes the search to find suitable IoT security measures extremely important. In order to fill this need, this study explores the use of machine learning in IoT security measures. Upon reviewing recent developments of machine learning in IoT security, it was found that the methods with the highest threat detection accuracy utilized the random forest and K-nearest neighbor algorithms and the most efficient methods utilized software-defined networks (SDN) and the fog layer of networks. In addition, the methods which determine the type of IoT device one is when it connects to a network primarily used the random forest algorithm. This study will take an in-depth look at the use of machine learning algorithms to detect malicious and anomalous data within IoT systems.

An Analysis of IoT Cyber Security Driven by Machine Learning

Modern scientific advancements often contribute to the introduction and refinement of never-before-seen technologies. This can be quite the task for humans to maintain and monitor and as a result, our society has become reliant on machine learning to assist in this task. With new technology comes new methods and thus new ways to circumvent existing cyber security measures. This study examines the effectiveness of three distinct Internet of Things cyber security algorithms currently used in industry today for malware and intrusion detection: Random Forest (RF), Support-Vector Machine (SVM), and K-Nearest Neighbor (KNN). Each algorithm was trained and tested on the Aposemat IoT-23 dataset which was published in January 2020 with the earliest of captures from 2018 and latest from 2019. The RF, SVM, and KNN reached peak accuracies of 92.96%, 86.23%, and 91.48%, respectively, in intrusion detection and 92.27%, 83.52%, and 89.80% in malware detection. It was found all three algorithms are capable of being effectively utilized for the current landscape of IoT cyber security in 2021.

A Modern Analysis of Aging Machine Learning Based IoT Cybersecurity Methods

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An Analysis of IoT Cyber Security Driven by Machine Learning

A Modern Analysis of Aging Machine Learning Based IoT Cybersecurity Methods