scholarly journals BAT—Block Analytics Tool Integrated with Blockchain Based IoT Platform

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1525
Author(s):  
Chathurangi Edussuriya ◽  
Kasun Vithanage ◽  
Namila Bandara ◽  
Janaka Alawatugoda ◽  
Manjula Sandirigama ◽  
...  
Keyword(s):  
Security And Privacy ◽  
Sensitive Information ◽  
Pharmaceutical Drugs ◽  
Case Scenario ◽  
Real World Data ◽  
Data Set ◽  
Pharmaceutical Supply Chain ◽  
Iot Platform ◽  
Novel Approach ◽  
Iot Devices

The Internet of Things (IoT) is the novel paradigm of connectivity and the driving force behind state-of-the-art applications and services. However, the exponential growth of the number of IoT devices and services, their distributed nature, and scarcity of resources has increased the number of security and privacy concerns ranging from the risks of unauthorized data alterations to the potential discrimination enabled by data analytics over sensitive information. Thus, a blockchain based IoT-platform is introduced to address these issues. Built upon the tamper-proof architecture, the proposed access management mechanisms ensure the authenticity and integrity of data. Moreover, a novel approach called Block Analytics Tool (BAT), integrated with the platform is proposed to analyze and make predictions on data stored on the blockchain. BAT enables the data-analysis applications to be developed using the data stored in the platform in an optimized manner acting as an interface to off-chain processing. A pharmaceutical supply chain is used as the use case scenario to show the functionality of the proposed platform. Furthermore, a model to forecast the demand of the pharmaceutical drugs is investigated using a real-world data set to demonstrate the functionality of BAT. Finally, the performance of BAT integrated with the platform is evaluated.

scholarly journals Hy-Bridge: A Hybrid Blockchain for Privacy-Preserving and Trustful Energy Transactions in Internet-of-Things Platforms

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 928 ◽  
Author(s):  
Mahdi Daghmehchi Firoozjaei ◽  
Ali Ghorbani ◽  
Hyoungshick Kim ◽  
JaeSeung Song
Keyword(s):  
Service Management ◽  
Energy Use ◽  
Information Leakage ◽  
Security And Privacy ◽  
User Privacy ◽  
Case Scenario ◽  
Iot Platform ◽  
Financial Transactions ◽  
Privacy Issues ◽  
Local Block

In the current centralized IoT ecosystems, all financial transactions are routed through IoT platform providers. The security and privacy issues are inevitable with an untrusted or compromised IoT platform provider. To address these issues, we propose Hy-Bridge, a hybrid blockchain-based billing and charging framework. In Hy-Bridge, the IoT platform provider plays no proxy role, and IoT users can securely and efficiently share a credit with other users. The trustful end-to-end functionality of blockchain helps us to provide accountability and reliability features in IoT transactions. Furthermore, with the blockchain-distributed consensus, we provide a credit-sharing feature for IoT users in the energy and utility market. To provide this feature, we introduce a local block framework for service management in the credit-sharing group. To preserve the IoT users’ privacy and avoid any information leakage to the main blockchain, an interconnection position, called bridge, is introduced to isolate IoT users’ peer-to-peer transactions and link the main blockchain to its subnetwork blockchain(s) in a hybrid model. To this end, a k-anonymity protection is performed on the bridge. To evaluate the performance of the introduced hybrid blockchain-based billing and charging, we simulated the energy use case scenario using Hy-Bridge. Our simulation results show that Hy-Bridge could protect user privacy with an acceptable level of information loss and CPU and memory usage.

scholarly journals Design and Implementation of ASCII based Method for Author Attribution

2019 ◽  
Vol 8 (9) ◽  
pp. 1963-1967
Keyword(s):  
Virtual World ◽  
Authorship Attribution ◽  
English Text ◽  
The Novel ◽  
Real World Data ◽  
Data Set ◽  
Design And Implementation ◽  
Novel Approach ◽  
Criminal Personality ◽  
The Web

With the presence of computer and internet, a developing variety of hoodlums are utilizing the web to spread a wide extend of illicit materials and wrong information universally in mysterious manner, making criminal personality following troublesome in the cybercrime examination handle. The virtual world provides criminals with an anonymous environment to conduct malicious activities such as malware, sending random messages, spamming, stealing intellectual property and sending ransom e-mails. All of these activities are text in somehow. Therefore, there is a need for a tool in order to identify the author or creator of this criminality by analyzing the text. Text-based Authorship Attribution techniques are used to identify the most possible author from a bunch of potential suspects of text. In this paper, the novel approach is presented for authorship attribution in English text using ASCII based processing approach Using this ASCII based method for authorship attribution help us to obtain better result in terms of accuracy and computational efficiency. The result is based on the text which is posted on social media considering real world data set.

Discovering Important Services Based on Weighted K-Core Decomposition

2019 ◽  
Vol 16 (1) ◽  
pp. 22-36
Author(s):  
Muchou Wang ◽  
Yiming Li ◽  
Sheng Luo ◽  
Zhuxin Hu
Keyword(s):  
Layered Structure ◽  
Service Oriented Architecture ◽  
Service Networks ◽  
Real World Data ◽  
Service Network ◽  
High Quality ◽  
Data Set ◽  
Decomposition Approach ◽  
Novel Approach ◽  
Service Oriented

With the development of service-oriented architecture, the number of services is expanding rapidly. Important services usually have high quality, and they can be recommended to users if the users do not give any keyword. However, how to discover the important services is still a problem facing many people. In this article, the authors propose a novel approach to discover important services based on service networks. First, their approach uses service networks to abstract services and the relations between them. Second, the authors employ the weighted k-core decomposition approach in the field of complex networks to partition the service network into a layered structure and calculate the weighted coreness value of each service node. Finally, services will be ranked according to their weighted coreness values in a descending order. The top-ranked services are the important ones the authors' approach recommends. Experimental results on a real-world data set crawled from ProgrammableWeb validate the effectiveness of their approach.

scholarly journals Intelligent Botnet Detection Approach in Modern Applications

2021 ◽  
Vol 15 (16) ◽  
pp. 113
Author(s):  
Khattab M. Ali Alheeti ◽  
Ibrahim Alsukayti ◽  
Mohammed Alreshoodi
Keyword(s):  
Detection System ◽  
Security And Privacy ◽  
Sensitive Information ◽  
Learning Networks ◽  
Detection Systems ◽  
Testing Dataset ◽  
Detection Approach ◽  
Secure Environment ◽  
Iot Devices ◽  
The Internet Of Things

<p class="0abstract">Innovative applications are employed to enhance human-style life. The Internet of Things (IoT) is recently utilized in designing these environments. Therefore, security and privacy are considered essential parts to deploy and successful intelligent environments. In addition, most of the protection systems of IoT are vulnerable to various types of attacks. Hence, intrusion detection systems (IDS) have become crucial requirements for any modern design. In this paper, a new detection system is proposed to secure sensitive information of IoT devices. However, it is heavily based on deep learning networks. The protection system can provide a secure environment for IoT. To prove the efficiency of the proposed approach, the system was tested by using two datasets; normal and fuzzification datasets. The accuracy rate in the case of the normal testing dataset was 99.30%, while was 99.42% for the fuzzification testing dataset. The experimental results of the proposed system reflect its robustness, reliability, and efficiency.</p>

scholarly journals Logisticschain: A Blockchain-Based Secure Storage Scheme for Logistics Data

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Hongzhi Li ◽  
Dezhi Han ◽  
Mingdong Tang
Keyword(s):  
Rapid Development ◽  
Strong Support ◽  
Security And Privacy ◽  
Sensitive Information ◽  
Logistics Network ◽  
Sensing Data ◽  
Secure Storage ◽  
Storage Scheme ◽  
Iot Devices ◽  
Logistics Process

With the rapid development of information technology, logistics systems are developing towards intelligence. The Internet of Things (IoT) devices throughout the logistics network could provide strong support for smart logistics. However, due to the limited computing and storage resources of IoT devices, logistics data with user sensitive information are generally stored in a centralized cloud center, which could easily cause privacy leakage. In this paper, we propose Logisticschain, a blockchain-based secure storage scheme for logistics data. In this scheme, the sensing data from IoT devices should be encrypted for fine-grained access control, and a customized blockchain structure is proposed to improve the storage efficiency of systems. Also, an efficient consensus mechanism is introduced to improve the efficiency of the consensus process in the blockchain. Specific to the logistics process, the sensing data generated from IoT devices will be encrypted and aggregated into the blockchain to ensure data security. Moreover, the stored logistics records can be securely audited by leveraging the blockchain network; both IoT data and logistics demands cannot be deleted or tampered to avoid disputes. Finally, we analyze the security and privacy properties of our Logisticschain and evaluate its performance in terms of computational costs by developing an experimental platform.

scholarly journals Learning to Describe: A New Approach to Computer Vision Based Ancient Coin Analysis

Sci ◽  
2020 ◽  
Vol 2 (2) ◽  
pp. 27
Author(s):  
Jessica Cooper ◽  
Ognjen Arandjelović
Keyword(s):  
Computer Vision ◽  
Real World ◽  
Research Effort ◽  
Practical Interest ◽  
Semantic Content ◽  
Real World Data ◽  
Data Set ◽  
Novel Approach ◽  
Semantic Concepts ◽  
Ancient Coin

In recent years, a range of problems under the broad umbrella of computer vision based analysis of ancient coins have been attracting an increasing amount of attention. Notwithstanding this research effort, the results achieved by the state of the art in published literature remain poor and far from sufficiently well performing for any practical purpose. In the present paper we present a series of contributions which we believe will benefit the interested community. We explain that the approach of visual matching of coins, universally adopted in existing published papers on the topic, is not of practical interest because the number of ancient coin types exceeds by far the number of those types which have been imaged, be it in digital form (e.g., online) or otherwise (traditional film, in print, etc.). Rather, we argue that the focus should be on understanding the semantic content of coins. Hence, we describe a novel approach—to first extract semantic concepts from real-world multimodal input and associate them with their corresponding coin images, and then to train a convolutional neural network to learn the appearance of these concepts. On a real-world data set, we demonstrate highly promising results, correctly identifying a range of visual elements on unseen coins with up to 84% accuracy.

scholarly journals Open-Source Federated Learning Frameworks for IoT: A Comparative Review and Analysis

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 167
Author(s):  
Ivan Kholod ◽  
Evgeny Yanaki ◽  
Dmitry Fomichev ◽  
Evgeniy Shalugin ◽  
Evgenia Novikova ◽  
...  
Keyword(s):  
Open Source ◽  
Rapid Development ◽  
Image Data ◽  
Ease Of Use ◽  
Security And Privacy ◽  
Data Sets ◽  
Data Set ◽  
Limited Bandwidth ◽  
Comparative Review ◽  
Iot Devices

The rapid development of Internet of Things (IoT) systems has led to the problem of managing and analyzing the large volumes of data that they generate. Traditional approaches that involve collection of data from IoT devices into one centralized repository for further analysis are not always applicable due to the large amount of collected data, the use of communication channels with limited bandwidth, security and privacy requirements, etc. Federated learning (FL) is an emerging approach that allows one to analyze data directly on data sources and to federate the results of each analysis to yield a result as traditional centralized data processing. FL is being actively developed, and currently, there are several open-source frameworks that implement it. This article presents a comparative review and analysis of the existing open-source FL frameworks, including their applicability in IoT systems. The authors evaluated the following features of the frameworks: ease of use and deployment, development, analysis capabilities, accuracy, and performance. Three different data sets were used in the experiments—two signal data sets of different volumes and one image data set. To model low-power IoT devices, computing nodes with small resources were defined in the testbed. The research results revealed FL frameworks that could be applied in the IoT systems now, but with certain restrictions on their use.

scholarly journals A Forensic Analysis of Home Automation Devices (FAHAD) Model: Kasa Smart Light Bulb and Eufy Floodlight Camera as Case Studies

2021 ◽  
Vol 1 (1-3) ◽  
pp. 18-26
Author(s):  
Fahad E. Salamh
Keyword(s):  
Forensic Analysis ◽  
Sensor Data ◽  
Smart Devices ◽  
Sensitive Information ◽  
Home Automation ◽  
Security Assessment ◽  
Light Bulb ◽  
Sensitive Data ◽  
Data Set ◽  
Iot Devices

The adoption of Internet of Things (IoT) devices is rapidly increasing with the advancement of network technology, these devices carry sensitive data that require adherence to minimum security practices. The adoption of smart devices to migrate homeowners from traditional homes to smart homes has been noticeable. These smart devices share value with and are of potential interest to digital forensic investigators, as well. Therefore, in this paper, we conduct comprehensive security and forensic analysis to contribute to both fields—targeting a security enhancement of the selected IoT devices and assisting the current IoT forensics approaches. Our work follows several techniques such as forensic analysis of identifiable information, including connected devices and sensor data. Furthermore, we perform security assessment exploring insecure communication protocols, plain text credentials, and sensitive information. This will include reverse engineering some binary files and manual analysis techniques. The analysis includes a data-set of home automation devices provided by the VTO labs: (1) the eufy floodlight camera, and (2) the Kasa smart light bulb. The main goal of the technical experiment in this research is to support the proposed model.

scholarly journals Learning to Describe: A New Approach to Computer Vision Based Ancient Coin Analysis

Sci ◽  
2020 ◽  
Vol 2 (1) ◽  
pp. 8 ◽  
Author(s):  
Jessica Cooper ◽  
Ognjen Arandjelović
Keyword(s):  
Computer Vision ◽  
Real World ◽  
Research Effort ◽  
Practical Interest ◽  
Semantic Content ◽  
Real World Data ◽  
Data Set ◽  
Novel Approach ◽  
Semantic Concepts ◽  
Ancient Coin

In recent years, a range of problems under the broad umbrella of computer vision based analysis of ancient coins have been attracting an increasing amount of attention. Notwithstanding this research effort, the results achieved by the state of the art in published literature remain poor and far from sufficiently well performing for any practical purpose. In the present paper we present a series of contributions which we believe will benefit the interested community. We explain that the approach of visual matching of coins, universally adopted in existing published papers on the topic, is not of practical interest because the number of ancient coin types exceeds by far the number of those types which have been imaged, be it in digital form (e.g., online) or otherwise (traditional film, in print, etc.). Rather, we argue that the focus should be on understanding the semantic content of coins. Hence, we describe a novel approach—to first extract semantic concepts from real-world multimodal input and associate them with their corresponding coin images, and then to train a convolutional neural network to learn the appearance of these concepts. On a real-world data set, we demonstrate highly promising results, correctly identifying a range of visual elements on unseen coins with up to 84% accuracy.

scholarly journals Privacy-Preserving IoT Data Aggregation Based on Blockchain and Homomorphic Encryption

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2452
Author(s):  
Faiza Loukil ◽  
Chirine Ghedira-Guegan ◽  
Khouloud Boukadi ◽  
Aïcha-Nabila Benharkat
Keyword(s):  
Data Aggregation ◽  
Homomorphic Encryption ◽  
Privacy Preserving ◽  
Security And Privacy ◽  
Sensitive Information ◽  
Aggregation Process ◽  
Smart Contract ◽  
Reasonable Choice ◽  
Iot Devices

Data analytics based on the produced data from the Internet of Things (IoT) devices is expected to improve the individuals’ quality of life. However, ensuring security and privacy in the IoT data aggregation process is a non-trivial task. Generally, the IoT data aggregation process is based on centralized servers. Yet, in the case of distributed approaches, it is difficult to coordinate several untrustworthy parties. Fortunately, the blockchain may provide decentralization while overcoming the trust problem. Consequently, blockchain-based IoT data aggregation may become a reasonable choice for the design of a privacy-preserving system. To this end, we propose PrivDA, a Privacy-preserving IoT Data Aggregation scheme based on the blockchain and homomorphic encryption technologies. In the proposed system, each data consumer can create a smart contract and publish both terms of service and requested IoT data. Thus, the smart contract puts together into one group potential data producers that can answer the consumer’s request and chooses one aggregator, the role of which is to compute the group requested result using homomorphic computations. Therefore, group-level aggregation obfuscates IoT data, which complicates sensitive information inference from a single IoT device. Finally, we deploy the proposal on a private Ethereum blockchain and give the performance evaluation.

Sign in / Sign up

Export Citation Format

Share Document