Improving the Privacy-Preserving of Covid-19 Bluetooth-Based Contact Tracing Applications Against Tracking Attacks

Bluetooth is an essential wireless standard for short-distance and low-power wireless networks. Health departments’ contact-tracing applications depended on Bluetooth technology to prevent infectious diseases from spreading, especially COVID-19. The security threats of the Bluetooth-based contact-tracing applications increased because an adversary can use them as surveillance tools that violate the user’s privacy and revealpersonal information. The Bluetooth standard mainly depends on the device address in its authenticated pairing mechanism (Secure Simple Pairing), which can collect with off-the-shelf hardware and software and leads to a tracking attack. To avoid the risk of tracking based on this security vulnerability in the Bluetooth protocol, we suggest a novel authentication protocol based on a noninteractive zero-knowledge scheme to substitute the authentication protocol used in the Bluetooth standard. The new protocol can replace the authentication protocol in the Bluetooth stack without any modification in the device pairing flow. Finally, we prove the security of our proposed scheme against the man-in-themiddle attack and tracking attack. A performance comparison with the authentication algorithm in the BLE standard shows that our method mitigates the tracking attack with low communication messages. Our results help enhance the contact-tracing application’s security in which Bluetooth access is available.

Circuit Break Connect Monitoring to 5G Mobile Application

Along by a continuous improvement to composite electronic devices, a safety to technicians takes additionally become the matter to good concern, as a result to technicians' lives is in jeopardy while their work through shutting down circuit breakers, even that even once the breaker takes been switched off, someone will inadvertently flip to while a technician remains working. That should be a system to guarantee safety that technicians. Also, individuals do not love switching all the time toward turn on / off appliances like fans/lighting/air conditioners. It ends in wasted energy thanks to unnecessarily placing the instrument. To address these issues, we tend to come up through the system through mobile app-controlled circuit breakers that degrade wireless management to home appliances to hunt down a golem app. That replaces a traditional breaker through the mobile app-controlled system in the on / off system, where no one will activate the breaker, while not the word. The remote of home appliances helps a user to save electricity. That enhances a quality of life and luxury. Additionally, a system includes the home security mechanism against drone intrusion using the mobile app-controlled door lock system besides the mechanism that sleuthing dangerous gas leaks. A formation of the system subtracts the degree of victim associate ESP 32 microcontroller, the Bluetooth module, matrix 4x4 keyboards, and the paraffin gas detector associate with a golem mobile application. The entire system is usually compact systems.

Blockchain Technology in Agriculture: A Case Study of Blockchain Start-Up Companies

Agriculture is one of the areas where blockchain technology could bring a revolution by solving the existing problem of Agri-product fraud, its traceability, price manipulation, and lack of customer trust in the product. This paper aims to demonstrate the potential application of blockchain technology in the agriculture industry and how it could address the existing issues by surveying the existing paper and following case studies of the blockchain start-up companies. Blockchain technology shows a promising approach to fostering a safer, better, more sustainable, and dependable agri-foods system in the future. While the application of blockchain in agriculture is in the initial phase and faces various issues like cost of implementation, privacy, security scalability, performance, and infancy, it can bring a revolution in the agriculture industry.

The BitcoinHeist: Classifications of Ransomware Crime Families

Ransomware attacks are on the rise and attackers are hijacking valuable information from different critical infrastructures and businesses requiring ransom payments to release the encrypted files. Payments in cryptocurrencies are designed to evade tracing the transactions and the recipients. With anonymity being paramount, tracing cryptocurrencies payments due to malicious activity and criminal transactions is a complicated process. Therefore, the need to identify these transactions and label them is crucial to categorize them as legitimate digital currency trade and exchange or malicious activity operations. Machine learning techniques are utilized to train the machine to recognize specific transactions and trace them back to malicious transactions or benign ones. I propose to work on the Bitcoin Heist data set to classify the different malicious transactions. The different transactions features are analyzed to predict a classifier label among the classifiers that have been identified as ransomware or associated with malicious activity. I use decision tree classifiers and ensemble learning to implement a random forest classifier. Results are assessed to evaluate accuracy, precision, and recall. I limit the study design to known ransomware identified previously and made available under the Bitcoin transaction graph from January 2009 to December 2018.

Information Retrieval based on Cluster Analysis Approach

The huge volume of text documents available on the internet has made it difficult to find valuable information for specific users. In fact, the need for efficient applications to extract interested knowledge from textual documents is vitally important. This paper addresses the problem of responding to user queries by fetching the most relevant documents from a clustered set of documents. For this purpose, a cluster-based information retrieval framework was proposed in this paper, in order to design and develop a system for analysing and extracting useful patterns from text documents. In this approach, a preprocessing step is first performed to find frequent and high-utility patterns in the data set. Then a Vector Space Model (VSM) is performed to represent the dataset. The system was implemented through two main phases. In phase 1, the clustering analysis process is designed and implemented to group documents into several clusters, while in phase 2, an information retrieval process was implemented to rank clusters according to the user queries in order to retrieve the relevant documents from specific clusters deemed relevant to the query. Then the results are evaluated according to evaluation criteria. Recall and Precision (P@5, P@10) of the retrieved results. P@5 was 0.660 and P@10 was 0.655.

Examining Modern Data Security and Privacy Protocols in Autonomous Vehicles

A fully automated, self-driving car can perceive its environment, determine the optimal route, and drive unaided by human intervention for the entire journey. Connected autonomous vehicles (CAVs) have the potential to drastically reduce accidents, travel time, and the environmental impact of road travel. Such technology includes the use of several sensors, various algorithms, interconnected network connections, and multiple auxiliary systems. CAVs have been subjected to attacks by malicious users to gain/deny control of one or more of its various systems. Data security and data privacy is one such area of CAVs that has been targeted via different types of attacks. The scope of this study is to present a good background knowledge of issues pertaining to different attacks in the context of data security and privacy, as well present a detailed review and analysis of eight very recent studies on the broad topic of security and privacy related attacks. Methodologies including Blockchain, Named Data Networking, Intrusion Detection System, Cognitive Engine, Adversarial Objects, and others have been investigated in the literature and problem- and context-specific models have been proposed by their respective authors.

Online Learning During Covid-19 Pandemic, and Possibility of Adopting Computer-Based Test

COVID-19 pandemic has impacted the educational institutions in Australia and New Zealand, thus online learning was a significant option for education to be smoothly continued. This could possibly enhance the Computer-based exams (CBEs) to be used in various courses, such as schools, universities and other training centres. As there are many educational institutions which have chosen to convert from paper test system to computer- based exam. However, adopting computer tests may lead to some difficulties for the students, which relates to technical defects and lake of computer skills of some students when they are applying the computer based exams. The purpose of the paper was to discuss online learning during Covid19 and the possibility of adopting (CBEs), then to determine negative and positive effects on the students of using computer-based exams and focus on some of suggesting solutions to the negative effects. Computer test which could cause negative effects on students due to various levels of skills to use a computer and some technical problems was examined. The design of the computer examination system requires careful planning and study from several aspects before becoming officially accepted, the computer-based exams still have a few problems which may lead to difficulties in using computer exams. Then the many benefits which could be gained by using computer-based exams, such as the student will be more independent with computer test were described. In addition, the students have access to the exams through the internet network. Finally, the effectiveness of certain strategy to solve the negative effects of computer-based exams were argued. Developing the solutions of the technical problems are required for computer test, where improving the input methods questions and corrections. It was resulted that online learning has considered as a better option during COVID-19 pandemic, and the computer exam, with adjustments, is more suitable for students.

Cloak-Reduce Load Balancing Strategy for Mapreduce

The advent of Big Data has seen the emergence of new processing and storage challenges. These challenges are often solved by distributed processing. Distributed systems are inherently dynamic and unstable, so it is realistic to expect that some resources will fail during use. Load balancing and task scheduling is an important step in determining the performance of parallel applications. Hence the need to design load balancing algorithms adapted to grid computing. In this paper, we propose a dynamic and hierarchical load balancing strategy at two levels: Intrascheduler load balancing, in order to avoid the use of the large-scale communication network, and interscheduler load balancing, for a load regulation of our whole system. The strategy allows improving the average response time of CLOAK-Reduce application tasks with minimal communication. We first focus on the three performance indicators, namely response time, process latency and running time of MapReduce tasks.

Web Scraper Utilizes Google Street View Images to Power a University Tour

Due to the outbreak of the Covid-19 pandemic, college tours are no longer available, so many students have lost the opportunity to see their dream school’s campus. To solve this problem, we developed a product called “Virtourgo,” a university virtual tour website that uses Google Street View images gathered from a web scraper allowing students to see what college campuses are like even when tours are unavailable during the pandemic. The project consists of 3/4 parts: the web scraper script, the GitHub server, the Google Domains DNS Server, and the HTML files. Some challenges we met include scraping repeated pictures and letting the HTML dropdown menu jump to the correct location. We solved these by implementing Python and Javascript functions that specifically target such challenges. Finally, after experimenting with all the functions of the web scraper and website, we confirmed that it works as expected and can scrape and deliver tours of any university campus or public buildings we want.

Fabric Defect Detection based on Improved Object as Point

In the field of fabric manufacturing, many factories still utilise the traditional manual detection method. It requires a lot of labour, resulting in high error rates and low efficiency. In this paper, we represent a realtime automated detection method based on object as point. This work makes three attributions. First, we build a fabric defects database and augment the data to training the intelligence model. Second, we provide a real-time fabric defects detection algorithm, which have potential to be applied in manufacturing. Third, we figure out CenterNet with soft NMS will improved the performance in fabric defect detection area, which is considered an NMS-free algorithm. Experiment results indicated that our lightweight network based method can effectively and efficiently detect five different fabric defects.