IEEE 2023: WEB SECURITY OR CYBER CRIME

 

IEEE 2023:  Machine Learning and Software-Defined Networking to Detect DDoS Attacks in IOT Networks

Abstract: In an era marked by the rapid proliferation of IoT devices, safeguarding network security has become an imperative task. This interim report provides an overview of our ongoing project, "Empowering Cyber Defense: Machine Learning Algorithm for Attack Identification," aimed at enhancing intrusion detection in IoT environments. Leveraging the power of Artificial Neural Networks (ANN), SVM (Support Vector Machine), a system tackles the formidable challenge of identifying attacks in the NF-ToN-IoT-V2 dataset, Our research explores four key attack classifications: Benign, Scanning, Password-based, and DDoS attacks. Through rigorous experimentation, we assess the efficacy of our proposed model in accurately identifying these attacks. By harnessing the rich features of IoT network traffic data, our system demonstrates promising results in terms of precision, recall, and overall classification performance. This interim report provides insights into our methodology, dataset, and initial findings, underscoring the significance of our research in bolstering IoT network security and defending against evolving cyber threats. In this we achieved validation accuracy.

Click for more details

IEEE 2023:  Analysis Of Cyber Aggression And Cyber Bullying In Social Networking.

Abstract: Cyber-bullying is a form of cyber-aggression that has become a major concern in today’s information society. In the digital age, the Internet has enabled the circulation of ideas and information and, in turn, has increased awareness among people. However, this does not come with its drawbacks. With the proliferation of online platforms, hoaxers can easily lure people towards their propagandist views or false news. The need to root out such false information and hate speech during this COVID-19 pandemic has never been more essential. The following study presents a survey of various papers that attempt to tackle similar problem statements with fake news, sentiment classification, and topic extraction. The paper focuses on how existing quality research can help improve the current state of research on COVID-19 related datasets by guiding researchers towards valuable procedures to help governmental authorities combat the rise in the spread of false news and the malicious and hate comments. 

Click for more details

IEEE 2023:  Malware Threat on Edge / Fog Computing Environments from Internet of Things Devices.

Abstract: Integration of the Internet into the entities of the different domains of human society (such as smart homes, health care, smart grids, manufacturing processes, product supply chains, and environmental monitoring) is emerging as a new paradigm called the Internet of Things (IoT). However, the ubiquitous and wide-range IoT networks make them prone to cyber attacks. This research aims to investigate and mitigate the escalating threat of malware in Edge/Fog Computing environments, specifically originating from Internet of Things (IoT) devices. Leveraging the CICIDS 2017 dataset, we propose a two-fold approach employing Support Vector Machine (SVM) algorithm and a Hybrid Deep Learning model, combining Convolutional Neural Network (CNN) with Long Short-Term Memory (LSTM) networks. The SVM algorithm will provide a robust baseline for detecting known malware patterns, while the CNN-LSTM hybrid model will enhance the system's capability to identify complex, evolving threats. Our study seeks to contribute to the development of advanced intrusion detection systems tailored for the unique challenges posed by Edge/Fog Computing, ensuring the security and integrity of IoT-driven ecosystems. The research outcomes are anticipated to shed light on effective strategies to safeguard critical infrastructures in the era of pervasive IoT adoption.

Click for more details

IEEE 2023: Phishing Web Sites Features Classification Based on Learning Machine.

Abstract:  Phishing are one of the most common and most dangerous attacks among cybercrimes. The aim of these attacks is to steal the information used by individuals and organizations to conduct transactions. Phishing websites contain various hints among their contents and web browser-based information. Phishing is a new type of network attack where the attacker creates a replica of an existing Web page to fool users (e.g., by using specially designed e-mails or instant messages) into submitting personal, financial, or password data to what they think is their service provides' Web site. The purpose of this study is to perform Extreme Learning Machine (ELM) based classification for 30 features including Phishing Websites Data in UCI Machine Learning Repository database.

Click for more details

IEEE 2023: MACHINE LEARNING

 

IEEE 2023: Improving Medicine Recommendation with Sentiment Analysis of Text Reviews and Machine Learning

Abstract: This study presents a comprehensive comparative analysis of the performance of Artificial Neural Networks (ANNs) and Random Forest Classifiers in binary classification tasks. The ANN model exhibited a commendable accuracy rate of 98%, showcasing its efficacy in accurately classifying instances. Additionally, the Random Forest Classifier demonstrated an equivalent accuracy rate of 98%, affirming its robustness as a classification tool. In-depth examination of confusion matrices provided valuable insights into the strengths and limitations of each model. The study also delves into the interpretability and computational complexity of both approaches, shedding light on their suitability for different applications. Overall, this research contributes to the understanding of the comparative performance of ANN and Random Forest Classifier models, offering valuable guidance for practitioners in selecting the most suitable approach for specific binary classification tasks.

Click for more details

IEEE 2023:  Secure IDS with Machine Learning & Network Decoy System

Abstract: The rapid growth of network-based attacks and security breaches has highlighted the need for robust Intrusion Detection Systems (IDS) to protect computer networks and sensitive data. The main objective of the proposed system is to develop an Intrusion Detection System (IDS) integrated with a Decoy system, employing the powerful Random Forest and K-Nearest Neighbors (KNN) machine learning algorithms. The primary objective of this project is to design and implement intelligent IDS that can effectively detect and classify various types of network intrusions while minimizing false positives. To enhance the system's security, a Decoy system will be integrated, which aims to deceive potential attackers and divert their attention from the actual network resources. The proposed IDS will leverage the Random Forest algorithm, a powerful ensemble learning technique that combines multiple decision trees to achieve high accuracy and robustness. The project will also explore the application of the K-Nearest Neighbors (KNN) algorithm for intrusion detection. KNN is a non-parametric algorithm that classifies new data points based on the majority vote of their nearest neighbors.

Click for more details

IEEE 2023: DEEP LEARNING

 

IEEE 2023:  Enhancing Facial Image Forgery Detection through Transfer Learning-Based Classification

Abstract :   In today's world, digital images have become ubiquitous, adorning the screens of our smartphones and populating the web pages of online platforms. Their applications span across numerous industries, including media, forensic and criminal investigations, medicine, and more. However, the accessibility and widespread usage of consumer photo editing tools have made image alteration effortless. This poses significant risks and challenges, particularly in fields where image integrity is paramount, making it exceedingly difficult to authenticate and establish the credibility of digital images.

Digital picture forgery involves manipulating an image's meaning by tampering with its content without leaving discernible traces. This study focuses on the classification of facial image forgeries using transfer learning techniques, specifically employing Convolutional Neural Networks (CNN) techniques. Three transfer learning methods, namely VGG-19, Inception V3, and Dense Net 201, are demonstrated to detect and classify facial image forgeries accurately.

Click for more details

                              

IEEE 2023: Transfer Learning Strategies for Cardiovascular Disease Detection in ECG Imagery

Abstract : This study proposes a novel approach for detecting cardiovascular diseases (CVD) from electrocardiogram (ECG) imagery using Convolutional Neural Networks (CNNs) and transfer learning techniques. Leveraging the pre-trained models Inception V3 and DenseNet201, we aim to overcome limitations posed by small datasets in medical imaging. Our methodology involves fine-tuning these architectures on a custom dataset of ECG images to adapt them to the task of CVD detection. By transferring knowledge learned from large-scale image datasets, our models achieve superior performance in terms of accuracy and generalization. Through extensive experiments and evaluation, we demonstrate the effectiveness of our approach in accurately diagnosing various cardiovascular conditions. This research contributes to the advancement of computer-aided diagnosis systems for CVDs, providing a scalable and efficient solution for early detection and intervention. Our implementation in Python facilitates easy integration into existing healthcare systems.

Click for more details

IEEE 2023: Harnessing Audio for Avian Identification:  A Review of Bird Species Identification Techniques.

Abstract : This project aims to advance avian identification through the utilization of audio signals, employing a comprehensive approach that integrates with deep learning techniques. The study focuses on developing three distinct models: Ridge Classifier, Support Vector Machine (SVM) Classifier, and Artificial Neural Network (ANN). By converting audio signals into numerical data using standard Python libraries, we aim to create robust models capable of accurately identifying bird species based on their distinct audio signatures. The Ridge and SVM classifiers, representing classical ML paradigms, will leverage advanced feature extraction methodologies. Simultaneously, the ANN model, a deep learning approach, will harness the power of neural networks to capture intricate patterns within the audio data. This interdisciplinary fusion of ML and deep learning techniques is poised to significantly enhance avian species identification, contributing to biodiversity monitoring and conservation efforts.

Click for more details

IEEE 2023: ISR-GAN: Improved Super-Resolution based GAN to Resolve Low Resolution Text Images

Abstract : The rapid advancement of Generative Adversarial Networks (GANs) has paved the way for significant breakthroughs in image processing tasks, particularly in the domain of super-resolution. In this proposal, we introduce ISR-GAN (Improved Super-Resolution based GAN), a novel framework tailored specifically for enhancing low-resolution text images. Our aim is to address the challenges associated with resolving degraded text content, where traditional methods often struggle due to the intricate details and sharp edges characteristic of text. By leveraging the adversarial training mechanism of GANs, coupled with advanced super-resolution techniques, ISR-GAN aims to produce high-fidelity, visually appealing reconstructions of low-resolution text images. We propose to employ carefully designed network architecture, incorporating both perceptual and adversarial loss functions, to encourage the generation of text images that not only exhibit enhanced resolution but also preserve textual content and clarity. Through extensive experimentation and evaluation, we seek to demonstrate the effectiveness and superiority of ISR-GAN over existing methods, offering a promising solution for improving the quality and readability of low-resolution text images.

Click for more details

IEEE 2023 : Machine Learning with Internet of Things

IEEE 2023: Freshness of Food Detection Using IoT & Machine Learning

Abstract: In today's world, food spoilage is a crucial problem as consuming spoiled food is harmful for consumers. Our project aims at detecting spoiled food using appropriate sensors and monitoring gases released by the particular food item. A micro controller that senses this, issues an alert using internet of things, so that appropriate action can be taken. This has wide scale application in food industries where food detection is done manually. We plan on implementing machine learning to this model so we can estimate how likely a food is going to get spoiled and in what duration, if brought from a particular vendor. This will increase competition among retailers to sell more healthy and fresh food and create a safe world for all consumers alike.

Click for more details

   

\

IEEE 2023: Enhanced Air Quality Monitoring using Machine Learning and IoT

Abstract:  The main goal of this project is to develop a model for Air Pollution Monitoring system using Machine learning with Internet of Things (IoT is basically a network of physical nodes at internet server) which facilitates the monitoring of air pollution by reading the values of Carbon Monoxide (CO), Carbon Monoxide (CO), Benzene, Nitrogen Dioxide (NO2), etc from environment using embedded system, values are send to Web Server through IOT Cloud service. In web server Air Pollution classification model will be build using Machine Learning process. For the IOT Cloud Service the environment data are collected by web server and it will pass to ML Air pollution classification trained model, which process the data and gives out the Air pollution classification result.

Click for more details 

IEEE 2023: An IoT-Enabled Worker Safety Helmet with Web-Based Monitoring and Alert System                 

Abstract:  This system proposes an Internet of Things (IoT)-enabled worker safety helmet equipped with a web-based monitoring and alert system. The helmet integrates sensors for temperature, heart rate, humidity, and harmful gases, alongside GPS for real-time location tracking. Data collected by the sensors is transmitted to the Thing Speak platform and visualized on a web interface, enabling supervisors to monitor worker safety in real-time. The system analyzes data and triggers alerts when unsafe conditions like high temperature, excessive heart rate, or gas presence are detected, facilitating proactive responses to ensure worker well-being. This novel application of IoT technology promotes workplace safety and protects workers from environmental hazards. 

Click for more details 

IEEE 2023: Integrated IoT Solutions for Smart Urban Infrastructure

Abstract: As urbanization accelerates globally, the need for sustainable and efficient urban infrastructure becomes paramount. This paper explores the integration of Internet of Things (IoT) technologies in the development of a smart city, focusing on three key components: street lighting, smart bridges, and traffic control. The proposed smart city framework leverages interconnected sensors, actuators, and data analytics to enhance the overall urban experience. The proposed integrated IoT solutions for street lighting, smart bridges, and traffic control collectively form a comprehensive framework for the development of a smart city. Through the seamless connectivity and data-driven decision-making offered by IoT technologies, cities can enhance their resilience, sustainability, and overall quality of life for their residents. 

IEEE 2021: PYTHON MACHINE LEARNING | IMAGE PROCESSING

IEEE 2021:  Convolution Neural Network model to detect and classify Tuberculosis (TB) manifestation in X-ray

ABSTRACT:  In developing or poor countries, it is not the easy job to discard the Tuberculosis (TB) outbreak by the persistent social inequalities in health. The less number of local health care professionals like doctors and the weak healthcare apparatus found in poor expedients settings. The modern computer enlargement strategies has corrected the recognition of TB testificanduming.  In this paper, It offer a paperback plan of action using Convolutional Neural Network (CNN) to handle with um-balanced; less-category X-ray portrayals (data sets), by using CNN plan of action, our plan of action boost the efficiency and correctness for stratifying multiple TB demonstration by a large margin It  traverse the effectiveness and efficiency of shamble with cross validation in instructing the network and discover the amazing effect in medical portrayal classification. This plan of actions and conclusions manifest a promising path for more accurate and quicker. Tuberculosis healthcare facilities recognition.

Click for More details

IEEE 2022:  Remote Sensing Image Scene Classification Using Deep Learning

ABSTRACT Remote sensing image scene classification, which aims at labeling remote sensing images with a set of semantic categories based on their contents, has broad applications in a range of fields. Propelled by the powerful feature learning capabilities of deep neural networks, remote sensing image scene classification driven by deep learning has drawn remarkable attention and achieved significant breakthroughs. However, to the best of our knowledge, a comprehensive review of recent achievements regarding deep learning for scene classification of remote sensing images is still lacking. To be specific, we discuss the main challenges of remote sensing image scene classification using Convolutional Neural Network-based remote sensing image scene classification methods, In addition, we introduce the image preprocessing technique used for remote sensing image scene classification and summarize the performance.

IEEE 2022:  Deep Learning for the Detection of COVID-19 Using Deep Learning                                     ABSTRACT:  Covid-19 disease is the one off the disorders. Though the symptoms are benign initially, they become more severe over time. Although for most people COVID-19 causes only mild illness, it can make some people very ill. More rarely, the disease can be fatal. Older people, and those with pre- existing medical conditions (such as high blood pressure, heart problems or diabetes) appear to be more vulnerable. In this project we are going to use chest CT Scan images for classify the covid-19. We are using Deep Learning algorithm name called Convolutional neural network for classify diagnose the disease and we able to a achieve the best accuracy.

Click for more details

IEEE 2023: INTERNET OF THINGS PROJECTS

IEEE 2023: IoT based wearable device to monitor the signs of COVID-19

Abstract: Monitoring and managing potential infected patients of COVID-19 is still a great challenge for the latest technologies. In this work, IoT based wearable monitoring device is designed to measure various vital signs related to COVID-19. Moreover, the system automatically alerts the concerned medical authorities about any violations of quarantine for potentially infected patients by monitoring their real time GPS data. The wearable sensor placed on the body is connected to edge node in IoT cloud where the data is processed and analyzed to define the state of health condition. The proposed system is implemented with three layered functionalities as wearable IoT sensor layer, cloud layer with Application Peripheral Interface (API) and Android web layer for mobile phones. Each layer has individual functionality, first the data is measured from IoT sensor layer to define the health symptoms. The next layer is used to store the information in the cloud database for preventive measures, alerts, and immediate actions. The Android mobile application layer is responsible for providing notifications and alerts for the potentially infected patient family respondents. The integrated system has both API and mobile application synchronized with each other for predicting and alarming the situation. The design serves as an essential platform that defines the measured readings of COVID-19 symptoms for monitoring, management, and analysis. Furthermore, the work disseminates how digital remote platform as wearable device can be used as a monitoring device to track the health and recovery of a COVID-19 patient..

Click for more details

IEEE 2023: Automation in Agriculture Using IOT and Machine Learning

Abstract: In the current age of high competition and risk in markets, technological advancements are a must for better growth and sustainability. The same applies to the agriculture industry. Every farmer has high stakes on the crops, their yield and quality. Rising water issues and need for proper methodologies for farm maintenance is a hot issue that needs to be tackled at utmost propriety. An automation of irrigation systems in farms is proposed in this research. The proposed solution is based on the Internet of Things (IoT), which would be a cheaper and more precise solution to the farm needs. A Monitoring system whose main purpose is to solve the over irrigation, soil erosion and crop-specific irrigation problem will be developed to ease and efficiently manage Irrigation problems. Since it is a well-known fact that the water is a scarce resource and over wastage of such an essential resource should be minimized. The proposed solution will be developed by establishing a distributed wireless sensor network (WSN), wherein each region of the farm would be covered by various sensor modules which will be transmitting data on a common server. Machine learning (ML) algorithms will support predictions for irrigation patterns based on crops and weather scenarios. So, a sustainable approach to irrigation is provided in this paper.

Click for more details

IEEE 2023: Development of Smart Home System Controlled by Android Application

Abstract: This project demonstrates the general layout of a wireless, inexpensive home automation system. It focuses primarily on the creation of an IOT-based home automation system capable of remotely controlling multiple components or being automatically set up to function based on environmental circumstances. In this project, we plan to create a firmware for smart control that can successfully be automated while minimizing human contact to maintain the integrity of all the electrical appliances in the house. To carry out the automation process, we made use of Node MCU, a well-known open source IOT platform. To transfer the user’s control of the devices through Node MCU to the real components of the system, various system components will employ various transmission modes. Remote access via a smartphone is made possible by the main control system’s use of wireless technology. By giving unrestricted access to the equipment for the user regardless of location, the project would become more realistic thanks to the deployment of a cloud server-based communication system. To enable increased automation, we offered a data transmission network. With a relatively low cost design, an easy-to-use interface, and an easy installation process, the system aimed to control electrical gadgets and appliances in homes. An android platform would allow for control of the appliance as well as access to its status. In order to meet the needs of the elderly and disabled in their homes, this system is intended to help and offer support. Additionally, the system’s use of the smart home idea enhances Keywords: Cloud Computing, Wi-Fi, Sensors, Arduino, IOT, Home Automation.

Click for more details

IEEE 2023: ADVANCED JAVA WITH BLOCKCHAIN AND CLOUD COMPUTING

IEEE-2023: A Proxy Re-Encryption Approach to Secure Data sharing in the Internet of Things based on Blockchain            

Abstract: Blockchain first emerged in 2008 because secretive transactions over the internet needed enormous trust between donor and NGO or organization to mediate. Now that digital currencies have been firmly established, charities have the opportunity to engage with a new set of donors. Looking across borders, fundraising platforms that accept donations are the easiest first place to look for charities to starting out. Using Blockchain technology we can track the donation funds contributed to the fundraiser cause and get reassured that the funds are reaching their required destination without any middle intervention and saving the donors from scams. The AI helps predict the cost estimation required for the total cause using datasets and approaching potential donors while maintaining data hygiene. AI is used to predict the requirement for approximate fund for any task to be accomplished..

Click for more details

IEEE-2023: Sensing Image sharing with Storage optimization Techniques in Cloud

 Abstract:  Blockchain is a newly emerging technology for data sharing and application. It can exchange de-centralized information in distributed systems without mutual trust by means of data encryption, timestamp and distributed consensus, so as to improve the efficiency of data sharing and application. This technology can be fully utilized in the large data remote sensing image system, and the multi-system shared node storage system can be managed efficiently and uniformly, so as to improve the economic efficiency of the system. This system designs the shared architecture based on block chain technology, proposes key research technologies. The main objective of this system is to identify a duplicate image and minimizing the storage space in Block chain.

Click for more details

IEEE 2023: Fake Product Identification System Using Blockchain.    

Abstract: Fake product identification is a growing concern in today’s global market. The use of blockchain technology can help address this issue by providing a secure and transparent way to track the provenance of products. We propose a system for fake product identification using blockchain, which involves assigning a unique identifier to each product at the time of manufacture and storing its transaction history on the blockchain. By leveraging the decentralized nature of blockchain, this system ensures the authenticity and integrity of product information, making it virtually impossible to tamper with. We discuss the benefits and challenges of implementing such a system and highlights the potential impact it could have on consumer trust. Overall, we provide insights into the potential of blockchain technology to tackle the issue of fake products in a secure and efficient manner. Moreover, block chain-based solutions for fake product identification enable stakeholders to trace the source of counterfeit products. This enables them to take appropriate measures to prevent further counterfeiting and safeguard their brand reputation. In conclusion, blockchain-based solutions for fake product identification offer a secure and transparent way to combat counterfeiting and protect consumers from potentially harmful products. By creating an immutable record of a product’s blockchain technology can enable manufacturers, retailers, and consumers to verify the authenticity of products and prevent counterfeiting.

Click for more details 

 IEEE-2023: An Efficient Cloud-Of-Cloud system For Storing and Sharing Big Data.  

 Abstract:  Visual We present CHARON, a cloud-backed storage system capable of storing and sharing big data in a reliable and efficient way using multiple cloud storage repositories to comply with the legal requirements of sensitive personal data.  Features: •It efficiently deals with large files over a set of geo-dispersed storage services.  •Efficient system which cut down network traffic cost.  •Map out a novel intermediate data participant schema.

The map reduce type simplifies the large scale data deal with product group even though many times effort have been made to maximize the execution of map reduce work.

A hash capacity made use of session middle of the topology among minimizes the task even so is note movement valued in network topology. At last board reproduced outcome that shows at the proposed algorithm can together minimize network cost.

.

Click for more details

IEEE 2020: ADVANCED CLOUD COMPUTING PROJECTS

IEEE 2020: Toward Practical Privacy-Preserving Frequent Item set Mining on Encrypted Cloud Data

Abstract: Frequent item set mining, which is the essential operation in association rule mining, is one of the most widely used data mining techniques on massive datasets nowadays. With the dramatic increase on the scale of datasets collected and stored with cloud services in recent years, it is promising to carry this computation-intensive mining process in the cloud. Amount of work also transferred the approximate mining computation into the exact computation, where such methods not only improve the accuracy also aim to enhance the efficiency. However, while mining data stored on public clouds, it inevitably introduces privacy concerns on sensitive datasets.
Click for more details

IEEE 2020: An Attribute-based Availability Model for Large Scale IaaS Clouds with CARMA

Abstract:  High availability is one of the core properties of Infrastructure as a Service (IaaS) and ensures that users have anytime access to on-demand cloud services. However, significant variations of workflow and the presence of super-tasks, mean that heterogeneous workload can severely impact the availability of IaaS clouds. Although previous work has investigated global queues, VM deployment, and failure of PMs, two aspects are yet to be fully explored: one is the impact of task size and the other is the differing features across PMs such as the variable execution rate and capacity. To address these challenges we propose an attribute-based availability model of large scale IaaS developed in the formal modeling language CARMA. The size of tasks in our model can be a fixed integer value or follow the normal, uniform or log-normal distribution.

Click for more details

IEEE-2019: A Secure Cloud-of-Clouds System for Storing and Sharing Big Data

Abstract: We present CHARON, a cloud-backed storage system capable of storing and sharing big data in a secure, reliable, and efficient way using multiple cloud providers and storage repositories to comply with the legal requirements of sensitive personal data. CHARON implements three distinguishing features: (1) it does not require trust on any single entity, (2) it does not require any client-managed server, and (3) it efficiently deals with large files over a set of geo-dispersed storage services. Besides that, we developed a novel Byzantine-resilient data-centric leasing protocol to avoid write-write conflicts between clients accessing shared repositories. We evaluate CHARON using micro and application-based benchmarks simulating representative workflows from bioinformatics, a prominent big data domain. The results show that our unique design is not only feasible but also presents an end-to-end performance of up to 2:5_ better than other cloud-backed solutions.

Click for more details

IEEE-2019:Crypt-DAC:Cryptographically Enforced Dynamic Access Control in the Cloud

Abstract: Enabling cryptographically enforced access controls for data hosted in untrusted cloud is attractive for many users and organizations. However, designing efficient cryptographically enforced dynamic access control system in the cloud is still challenging. In this paper, we propose Crypt-DAC, a system that provides practical cryptographic enforcement of dynamic access control. Crypt-DAC revokes access permissions by delegating the cloud to update encrypted data. In Crypt-DAC, a file is encrypted by a symmetric key list which records a file key and a sequence of revocation keys. In each revocation, a dedicated administrator uploads a new revocation key to the cloud and requests it to encrypt the file with a new layer of encryption and update the encrypted key list accordingly. Crypt-DAC proposes three key techniques to constrain the size of key list and encryption layers. As a result, Crypt-DAC enforces dynamic access control that provides efficiency, as it does not require expensive decryption/reencryption and uploading/re-uploading of large data at the administrator side, and security, as it immediately revokes access permissions. We use formalization framework and system implementation to demonstrate the security and efficiency of our construction.

Click for more details

IEEE 2018: Secure Attribute-Based Signature Scheme with Multiple Authorities for Blockchain in Electronic Health Records Systems
Abstract: Electronic Health Records (EHRs) are entirely controlled by hospitals instead of patients, which complicates seeking medical advices from different hospitals. Patients face a critical need to focus on the details of their own healthcare and restore management of their own medical data. The rapid development of blockchain technology promotes population healthcare, including medical records as well as patient-related data. This technology provides patients with comprehensive, immutable records, and access to EHRs free from service providers and treatment websites. In this paper, to guarantee the validity of EHRs encapsulated in blockchain, we present an attribute-based signature scheme with multiple authorities, in which a patient endorses a message according to the attribute while disclosing no information other than the evidence that he has attested to it. Furthermore, there are multiple authorities without a trusted single or central one to generate and distribute public/private keys of the patient, which avoids the escrow problem and conforms t the mode of distributed data storage in the blockchain. By sharing the secret pseudorandom function seeds among authorities, this protocol resists collusion attack out of N from N 􀀀1 corrupted authorities. Under the assumption of the computational bilinear Dif_e-Hellman, we also formally demonstrate that, in terms of the unforgeability and perfect privacy of the attribute-signer, this attribute-based signature scheme is secure in the random oracle model. The comparison shows the ef_ciency and properties between the proposed method and methods proposed in other studies.
Click for more details

EEE 2019: Intelligent Neonatal Monitoring System Based on Android Application using Multi Sensors

IEEE 2020: Lightweight and Privacy-Preserving ID-as-a-Service provisioning in Vehicular Cloud Computing
Abstract: Vehicular cloud computing (VCC) is composed of multiple distributed vehicular clouds (VCs), which are formed on-the-fly by dynamically integrating underutilized vehicular resources including computing power, storage, and so on. Existing proposals for identity-as-a-service (IDaaS) are not suitable for use in VCC due to limited computing resources and storage capacity of onboard vehicle devices. In this paper, we first propose an improved ciphertext-policy attribute-bas Utilizing the improved CP-ABE scheme and the permissioned blockchain technology, we propose a lightweight and privacy-preserving IDaaS architecture for VCC named IDaaSoVCC.ed encryption (CPABE) scheme.
Click for more details 

IEEE 2020: Lightweight Sharable and Traceable Secure Mobile Health System

Abstract: Mobile health (mHealth) has emerged as a new patient centric model which allows real-time collection of patient data via wearable sensors, aggregation and encryption of these data at mobile devices, and then uploading the encrypted data to the cloud for storage and access by healthcare staff and researchers. However, efficient and scalable sharing of encrypted data has been a very challenging problem. In this paper, we propose a Lightweight Sharable and Traceable (LiST) secure mobile health system in which patient data are encrypted end-to-end from a patient’s mobile device to data users.

Click for more details 

IEEE 2019: Intelligent Neonatal Monitoring System Based on Android Application using Multi Sensors 

Abstract: The purpose of the project is to develop an Intelligent Neonatal Monitoring System based on temperature and pulse rate data. In the Neonatal Intensive Care Unit (NICU), there are premature babies and other ill babies who need extra care from the doctors, nurses as well as medical supplies. Therefore, an intelligent neonatal monitoring system should be a good solution in order to help them to observe neonates frequently and consistently. This system transmits the vital signs of the neonate such as body temperature and pulse rate to the Internet of Things (IoT) called ThingSpeak. The body temperature and the pulse rate will be detected by LM35 temperature sensor and pulse sensor respectively. These information will be sent to the IoT via ESP8266 Wi-Fi Shield. IoT helps the doctors and nurses to be connected with the neonate’s vital signs and it is helpful in monitoring the neonates at anytime and anywhere through the internet. The percentage difference between LM35 temperature sensor and digital thermometer is less than 3% while the pulse rate can be varied according to the physical activity. This develops system will providing efficiency and reliability which will play a vital role for better care.
Click for more details

IEEE 2019: Secured Vehicle Toll Payment System Using NFC

Abstract: Nowadays, uses for NFC technology have been emerging day by day, the best application of NFC technology is in the contactless payment system. Similarly, due to various advantages of web application such as ease of maintenance and various user-friendly released version, the demand for new web applications supporting distinctive kinds of gadgets and intentions are persistently. Now different technologies such as Bluetooth, NFC, and BLE are being used for initiating the online payment. Considering the parameters such as cost, more reliability, and increased security, NFC technology is a best-fitted option for initiating the online vehicle toll payment system. Thus, the application of Cloud-based web application along with different IoT devices like Smartphone (having NFC feature) and NFC tag (ISO/IEC 14443) is explained in this paper. Paper the online vehicle toll payment system developed by using NFC technology is used for triggering the vehicle toll payment system supported by the web application.
Click for more details

IEEE 2018: MEDIBOX – IoT Enabled Patient Assisting Device

Abstract: The health and wellness sector is critical to human society and as such should be one of the first to receive the benefits of upcoming technologies like IoT. Some of the Internet of Medical Things (IoMT) are connected to IoT networks to monitor the day-to-day activities of the patients. Recently there has been attempts to design new medical devices which monitor the medications and help aged people for a better assisted living. In this paper, one such attempt is made to design a multipurpose portable intelligent device named MEDIBOX which helps the patients take their medications at the right time. This box is a proficient system which maintains the parameters like temperature and humidity in a controlled range recommended by the drug manufacturer and thus maintains the potency of the medicines even if the patient is travelling. Related to this, we have developed a Host Management System (HMS) which is capable of cloud-based installation and monitoring that stores and controls the MEDIBOX functionality for further analysis and future modification in design aspects.
Click for more details