Privacy preserving support vector machine based on federated learning for distributed IoT-enabled data analysis

In a smart city, IoT devices are required to support monitoring of normal operations such as traffic, infrastructure, and the crowd of people. IoT-enabled systems offered by many IoT devices are expected to achieve sustainable developments from the information collected by the smart city. Indeed, artificial intelligence (AI) and machine learning (ML) are well-known methods for achieving this goal as long as the system framework and problem statement are well prepared. However, to better use AI/ML, the training data should be as global as possible, which can prevent the model from working only on local data. Such data can be obtained from different sources, but this induces the privacy issue where at least one party collects all data in the plain. The main focus of this article is on support vector machines (SVM). We aim to present a solution to the privacy issue and provide confidentiality to protect the data. We build a privacy-preserving scheme for SVM (SecretSVM) based on the framework of federated learning and distributed consensus. In this scheme, data providers self-organize and obtain training parameters of SVM without revealing their own models. Finally, experiments with real data analysis show the feasibility of potential applications in smart cities. This article is the extended version of that of Hsu et al. (Proceedings of the 15th ACM Asia Conference on Computer and Communications Security. ACM; 2020:904-906).     

The Therapeutic Potential of Milk Extracellular Vesicles on Colorectal Cancer

Colorectal cancer remains one of the leading prevalent cancers in the world and is the fourth most common cause of death from cancer. Unfortunately, the currently utilized chemotherapies fail in selectively targeting cancer cells and cause harm to healthy cells, which results in profound side effects. Researchers are focused on developing anti-cancer targeted medications, which is essential to making them safer, more effective, and more selective and to maximizing their therapeutic benefits. Milk-derived extracellular vesicles (EVs) from camels and cows have attracted much attention as a natural substitute product that effectively suppresses a wide range of tumor cells. This review sheds light on the biogenesis, methods of isolation, characterization, and molecular composition of milk EVs as well as the therapeutic potentials of milk EVs on colorectal cancer.     

Estimating Security Risk of Healthcare Web Applications: A Design Perspective

In the recent years, the booming web-based applications have attracted the hackers’ community. The security risk of the web-based hospital management system (WBHMS) has been increasing rapidly. In the given context, the main goal of all security professionals and website developers is to maintain security divisions and improve on the user’s confidence and satisfaction. At this point, the different WBHMS tackle different types of security risks. In WBHMS, the security of the patients’ medical information is of utmost importance. All in all, there is an inherent security risk of data and assets in the field of the medical industry as a whole. The objective of this study is to estimate the security risk assessment of WBHMS. The risks assessment pertains to securing the integrity of the information in alignment with the Health Insurance Portability and Accountability Act. This includes protecting the relevant financial records, as well as the identification, evaluation, and prevention of a data breach. In the past few years, according to the US-based cyber-security firm Fire-eye, 6.8 million data thefts have been recorded in the healthcare sector in India. The breach barometer report mentions that in the year 2019, the data breaches found were up to 48.6% as compared to the year 2018. Therefore, it is very important to assess the security risk in WBHMS. In this research, we have followed the hybrid technique fuzzy analytic hierarchy process-technique for order of preference by similarity to ideal solution (F-AHPTOPSIS) approach to assess the security risk in WBHMS. The place of this empirical database is at the local hospital of Varanasi, U.P., India. Given the affectability of WBHMS for its board framework, this work has used diverse types of web applications. The outcomes obtained and the procedure used in this assessment would support future researchers and specialists in organizing web applications through advanced support of safety and security.     

Parameter Tuned Machine Learning Based Emotion Recognition on Arabic Twitter Data

Arabic is one of the most spoken languages across the globe. However, there are fewer studies concerning Sentiment Analysis (SA) in Arabic. In recent years, the detected sentiments and emotions expressed in tweets have received significant interest. The substantial role played by the Arab region in international politics and the global economy has urged the need to examine the sentiments and emotions in the Arabic language. Two common models are available: Machine Learning and lexicon-based approaches to address emotion classification problems. With this motivation, the current research article develops a Teaching and Learning Optimization with Machine Learning Based Emotion Recognition and Classification (TLBOML-ERC) model for Sentiment Analysis on tweets made in the Arabic language. The presented TLBOML-ERC model focuses on recognising emotions and sentiments expressed in Arabic tweets. To attain this, the proposed TLBOML-ERC model initially carries out data pre-processing and a Continuous Bag Of Words (CBOW)-based word embedding process. In addition, Denoising Autoencoder (DAE) model is also exploited to categorise different emotions expressed in Arabic tweets. To improve the efficacy of the DAE model, the Teaching and Learning-based Optimization (TLBO) algorithm is utilized to optimize the parameters. The proposed TLBOML-ERC method was experimentally validated with the help of an Arabic tweets dataset. The obtained results show the promising performance of the proposed TLBOML-ERC model on Arabic emotion classification.     

Modified Garden Balsan Optimization Based Machine Learning for Intrusion Detection

The Internet of Things (IoT) environment plays a crucial role in the design of smart environments. Security and privacy are the major challenging problems that exist in the design of IoT-enabled real-time environments. Security susceptibilities in IoT-based systems pose security threats which affect smart environment applications. Intrusion detection systems (IDS) can be used for IoT environments to mitigate IoT-related security attacks which use few security vulnerabilities. This paper introduces a modified garden balsan optimization-based machine learning model for intrusion detection (MGBO-MLID) in the IoT cloud environment. The presented MGBO-MLID technique focuses on the identification and classification of intrusions in the IoT cloud atmosphere. Initially, the presented MGBO-MLID model applies min-max normalization that can be utilized for scaling the features in a uniform format. In addition, the MGBO-MLID model exploits the MGBO algorithm to choose the optimal subset of features. Moreover, the attention-based bidirectional long short-term (ABiLSTM) method can be utilized for the detection and classification of intrusions. At the final level, the Aquila optimization (AO) algorithm is applied as a hyperparameter optimizer to fine-tune the ABiLSTM methods. The experimental validation of the MGBO-MLID method is tested using a benchmark dataset. The extensive comparative study reported the betterment of the MGBO-MLID algorithm over recent approaches.     

A secure and efficient remote patient‐monitoring authentication protocol for cloud‐IoT

The ongoing Cloud‐IoT (Internet of Things)–based technological advancements have revolutionized the ways in which remote patients could be monitored and provided with health care facilities. The real‐time monitoring of patient's health leads to dispensing the right medical treatment at the right time. The health professionals need to access patients' sensitive data for such monitoring, and if treated with negligence, it could also be used for malevolent objectives by the adversary. Hence, the Cloud‐IoT–based technology gains could only be conferred to the patients and health professionals, if the latter authenticate one another properly. Many authentication protocols are proposed for remote patient health care monitoring, but with limitations. Lately, Sharma and Kalra (DOI: 10.1007/s40998‐018‐0146‐5) present a remote patient‐monitoring authentication scheme based on body sensors. However, we discover that the scheme still bears many drawbacks including stolen smart card attack, session key compromise, and user impersonation attacks. In view of those limitations, we have designed an efficient authentication protocol for remote patient health monitoring that counters all the above‐mentioned drawbacks. Moreover, we prove the security features of our protocol using BAN logic‐based formal security analysis and validate the results in ProVerif automated security tool.     

IoT-Cloud Assisted Botnet Detection Using Rat Swarm Optimizer withDeepLearning

Nowadays, Internet of Things (IoT) has penetrated all facets of human life while on the other hand, IoT devices are heavily prone to cyberattacks. It has become important to develop an accurate system that can detect malicious attacks on IoT environments in order to mitigate security risks. Botnet is one of the dreadful malicious entities that has affected many users for the past few decades. It is challenging to recognize Botnet since it has excellent carrying and hidden capacities. Various approaches have been employed to identify the source of Botnet at earlier stages. Machine Learning (ML) and Deep Learning (DL) techniques are developed based on heavy influence from Botnet detection methodology. In spite of this, it is still a challenging task to detect Botnet at early stages due to low number of features accessible from Botnet dataset. The current study devises IoT with Cloud Assisted Botnet Detection and Classification utilizing Rat Swarm Optimizer with Deep Learning (BDC-RSODL) model. The presented BDC-RSODL model includes a series of processes like pre-processing, feature subset selection, classification, and parameter tuning. Initially, the network data is pre-processed to make it compatible for further processing. Besides, RSO algorithm is exploited for effective selection of subset of features. Additionally, Long Short Term Memory (LSTM) algorithm is utilized for both identification and classification of botnets. Finally, Sine Cosine Algorithm (SCA) is executed for fine-tuning the hyperparameters related to LSTM model. In order to validate the promising performance of BDC-RSODL system, a comprehensive comparison analysis was conducted. The obtained results confirmed the supremacy of BDC-RSODL model over recent approaches.     

Sailfish Optimizer with Deep Transfer Learning-Enabled Arabic Handwriting Character Recognition

The recognition of the Arabic characters is a crucial task in computer vision and Natural Language Processing fields. Some major complications in recognizing handwritten texts include distortion and pattern variabilities. So, the feature extraction process is a significant task in NLP models. If the features are automatically selected, it might result in the unavailability of adequate data for accurately forecasting the character classes. But, many features usually create difficulties due to high dimensionality issues. Against this background, the current study develops a Sailfish Optimizer with Deep Transfer Learning-Enabled Arabic Handwriting Character Recognition (SFODTL-AHCR) model. The projected SFODTL-AHCR model primarily focuses on identifying the handwritten Arabic characters in the input image. The proposed SFODTL-AHCR model pre-processes the input image by following the Histogram Equalization approach to attain this objective. The Inception with ResNet-v2 model examines the pre-processed image to produce the feature vectors. The Deep Wavelet Neural Network (DWNN) model is utilized to recognize the handwritten Arabic characters. At last, the SFO algorithm is utilized for fine-tuning the parameters involved in the DWNN model to attain better performance. The performance of the proposed SFODTL-AHCR model was validated using a series of images. Extensive comparative analyses were conducted. The proposed method achieved a maximum accuracy of 99.73%. The outcomes inferred the supremacy of the proposed SFODTL-AHCR model over other approaches.     

Urolithin A and B Alter Cellular Metabolism and Induce Metabolites Associated with Apoptosis in Leukemic Cells

Leukemia is persistently a significant cause of illness and mortality worldwide. Urolithins, metabolites of ellagic acid and ellagitannins produced by gut microbiota, showed better bioactive compounds liable for the health benefits exerted by ellagic acid and ellagitannins containing pomegranate and walnuts. Here, we assessed the potential antileukemic activities of both urolithin A and urolithin B. Results showed that both urolithin A and B significantly inhibited the proliferation of leukemic cell lines Jurkat and K562, among which urolithin A showed the more prominent antiproliferative capability. Further, urolithin treatment alters leukemic cell metabolism, as evidenced by increased metabolic rate and notable changes in glutamine metabolism, one-carbon metabolism, and lipid metabolism. Next, we evidenced that both urolithins equally promoted apoptosis in leukemic cell lines. Based on these observations, we concluded that both urolithin A and B alter leukemic cell metabolome, resulting in a halt of proliferation, followed by apoptosis. The data can be used for designing new combinational therapies to eradicate leukemic cells.     

Deep Learning Control for Autonomous Robot

Several applications of machine learning and artificial intelligence, have acquired importance and come to the fore as a result of recent advances and improvements in these approaches. Autonomous cars are one such application. This is expected to have a significant and revolutionary influence on society. Integration with smart cities, new infrastructure and urban planning with sophisticated cyber-security are some of the current ramifications of self-driving automobiles. The autonomous automobile, often known as self-driving systems or driverless vehicles, is a vehicle that can perceive its surroundings and navigate predetermined routes without human involvement. Cars are on the verge of evolving into autonomous robots, thanks to significant breakthroughs in artificial intelligence and related technologies, and this will have a wide range of socio-economic implications. However, in order for these automobiles to become a reality, they must be endowed with the perception and cognition necessary to deal with high-pressure real-life events and make proper judgments and take appropriate action. The majority of self-driving car technologies are based on computer systems that automate vehicle control parts. From forward-collision warning and antilock brakes to lane-keeping and adaptive drive control, to fully automated driving, these technological components have a wide range of capabilities. A self-driving car combines a wide range of sensors, actuators, and cameras. Recent researches on computer vision and deep learning are used to control autonomous driving systems. For self-driving automobiles, lane-keeping is crucial. This study presents a deep learning approach to obtain the proper steering angle to maintain the robot in the lane. We propose an advanced control for a self-driving robot by using two controllers simultaneously. Convolutional neural networks (CNNs) are employed, to predict the car’ and a proportional-integral-derivative (PID) controller is designed for speed and steering control. This study uses a Raspberry PI based camera to control the robot car.