Biblio

In this research work, we attempted to predict the creditworthiness of smartphone users in Indonesia during the COVID-19 pandemic using machine learning. Principal Component Analysis (PCA) and Kmeans algorithms are used for the prediction of creditworthiness with the used a dataset of 1050 respondents consisting of twelve questions to smartphone users in Indonesia during the COVID-19 pandemic. The four different classification algorithms (Logistic Regression, Support Vector Machine, Decision Tree, and Naive Bayes) were tested to classify the creditworthiness of smartphone users in Indonesia. The tests carried out included testing for accuracy, precision, recall, F1-score, and Area Under Curve Receiver Operating Characteristics (AUCROC) assesment. Logistic Regression algorithm shows the perfect performances whereas Naïve Bayes (NB) shows the least. The results of this research also provide new knowledge about the influential and non-influential variables based on the twelve questions conducted to the respondents of smartphone users in Indonesia during the COVID-19 pandemic.

Many organisations responded to the recent global pandemic by moving operations online. This has led to increased exposure to information security-related risks. There is thus an increased need to ensure organisational information security awareness programs are up to date and relevant to the needs of the intended target audience. The advent of online educational providers has similarly placed increased pressure on the formal educational sector to ensure course content is updated to remain relevant. Such processes of academic reflection and review should consider formal curriculum standards and guidelines in order to ensure wide relevance. This paper presents a case study of the review of an Introduction to Information Security course. This review is informed by the Information Security and Assurance knowledge area of the ACM/IEEE Computer Science 2013 curriculum standard. The paper presents lessons learned during this review process to serve as a guide for future reviews of this nature. The authors assert that these lessons learned can also be of value during the review of organisational information security awareness programs.

Counterfeited products are a significant problem in both developed and developing countries and has become more critical as an aftermath of COVID-19, exclusively for drugs and medical equipment’s. In this paper, an innovative approach is proposed to resist counterfeiting which is based on the principles of Synthetic DNA. The proposed encryption approach has employed the distinctive features of synthetic DNA in amalgamation with DNA encryption to provide information security and functions as an anticounterfeiting method that ensures usability. The scheme’s security analysis and proof of concept are detailed. Scyther is used to carry out the formal analysis of the scheme, and all of the modeled assertions are verified without any attacks.

Technology plays a vital role in our lives to meet basic hygiene necessities. Currently, the whole world is facing an epidemic situation and the practice of using sanitizers is common nowadays. Sanitizers are used by people to sanitize their hands and bodies. It is also used for sanitizing objects that come into contact with the machine. While sanitizing a small area, people manage to sanitize via pumps, but it becomes difficult to sanitize the same area every day. One of the most severe sanitation concerns is a simple, economic and efficient method to adequately clean the indoor and outdoor environments. In particular, effective sanitization is required for people working in a clinical environment. Recently, some commonly used sanitizer techniques include electric sanitizer spray guns, electric sanitizer disinfectants, etc. However, these sanitizers are not automated, which means a person is required to roam personally with the device to every place to spray the disinfectant or sanitize an area. Therefore, a novel, cost-effective automatic sanitizing machine (ASM) named ASMBoT is designed that can dispense the sanitizer effectively by solving the aforementioned problems.

Currently, the Dark Web is one key platform for the online trading of illegal products and services. Analysing the .onion sites hosting marketplaces is of interest for law enforcement and security researchers. This paper presents a study on 123k listings obtained from 6 different Dark Web markets. While most of current works leverage existing datasets, these are outdated and might not contain new products, e.g., those related to the 2020 COVID pandemic. Thus, we build a custom focused crawler to collect the data. Being able to conduct analyses on current data is of considerable importance as these marketplaces continue to change and grow, both in terms of products offered and users. Also, there are several anti-crawling mechanisms being improved, making this task more difficult and, consequently, reducing the amount of data obtained in recent years on these marketplaces. We conduct a data analysis evaluating multiple characteristics regarding the products, sellers, and markets. These characteristics include, among others, the number of sales, existing categories in the markets, the origin of the products and the sellers. Our study sheds light on the products and services being offered in these markets nowadays. Moreover, we have conducted a case study on one particular productive and dynamic drug market, i.e., Cannazon. Our initial goal was to understand its evolution over time, analyzing the variation of products in stock and their price longitudinally. We realized, though, that during the period of study the market suffered a DDoS attack which damaged its reputation and affected users' trust on it, which was a potential reason which lead to the subsequent closure of the market by its operators. Consequently, our study provides insights regarding the last days of operation of such a productive market, and showcases the effectiveness of a potential intervention approach by means of disrupting the service and fostering mistrust.

Given the COVID-19 pandemic, this paper aims at providing a full-process information system to support the detection of pathogens for a large range of populations, satisfying the requirements of light weight, low cost, high concurrency, high reliability, quick response, and high security. The project includes functional modules such as sample collection, sample transfer, sample reception, laboratory testing, test result inquiry, pandemic analysis, and monitoring. The progress and efficiency of each collection point as well as the status of sample transfer, reception, and laboratory testing are all monitored in real time, in order to support the comprehensive surveillance of the pandemic situation and support the dynamic deployment of pandemic prevention resources in a timely and effective manner. Deployed on a cloud platform, this system can satisfy ultra-high concurrent data collection requirements with 20 million collections per day and a maximum of 5 million collections per hour, due to its advantages of high concurrency, elasticity, security, and manageability. This system has also been widely used in Jiangsu, Shaanxi provinces, for the prevention and control of COVID-19 pandemic. Over 100 million NAT data have been collected nationwide, providing strong informational support for scientific and reasonable formulation and execution of COVID-19 prevention plans.

In recent years, business environments are undergoing disruptive changes across sectors [1]. Globalization and technological advances, such as artificial intelligence and the internet of things, have completely redesigned business activities, bringing to light an ever-increasing interest and attention towards the customer [2], especially in healthcare sector. In this context, researchers is paying more and more attention to the introduction of new technologies capable of meeting the patients’ needs [3, 4] and the Covid-19 pandemic has contributed and still contributes to accelerate this phenomenon [5]. Therefore, emerging technologies (i.e., AI-enabled solutions, service robots, conversational agents) are proving to be effective partners in improving medical care and quality of life [6]. Conversational agents, often identified in other ways as “chatbots”, are AI-enabled service robots based on the use of text [7] and capable of interpreting natural language and ensuring automation of responses by emulating human behavior [8, 9, 10]. Their introduction is linked to help institutions and doctors in the management of their patients [11, 12], at the same time maintaining the negligible incremental costs thanks to their virtual aspect [13–14]. However, while the utilization of these tools has significantly increased during the pandemic [15, 16, 17], it is unclear what benefits they bring to service delivery. In order to identify their contributions, there is a need to find out which activities can be supported by conversational agents.This paper takes a grounded approach [18] to achieve contextual understanding design and to effectively interpret the context and meanings related to conversational agents in healthcare interactions. The study context concerns six chatbots adopted in the healthcare sector through semi-structured interviews conducted in the health ecosystem. Secondary data relating to these tools under consideration are also used to complete the picture on them. Observation, interviewing and archival documents [19] could be used in qualitative research to make comparisons and obtain enriched results due to the opportunity to bridge the weaknesses of one source by compensating it with the strengths of others. Conversational agents automate customer interactions with smart meaningful interactions powered by Artificial Intelligence, making support, information provision and contextual understanding scalable. They help doctors to conduct the conversations that matter with their patients. In this context, conversational agents play a critical role in making relevant healthcare information accessible to the right stakeholders at the right time, defining an ever-present accessible solution for patients’ needs. In summary, conversational agents cannot replace the role of doctors but help them to manage patients. By conveying constant presence and fast information, they help doctors to build close relationships and trust with patients.

Childcare, a critical infrastructure, played an important role to create community resiliency during the COVID-19 pandemic. By finding pathways to remain open, or rapidly return to operations, the adaptive capacity of childcare providers to offer care in the face of unprecedented challenges functioned to promote societal level mitigation of the COVID-19 pandemic impacts, to assist families in their personal financial recoveries, and to provide consistent, caring, and meaningful educational experiences for society's youngest members. This paper assesses the operational adaptations of childcare centers as a key resource and critical infrastructure during the COVID-19 pandemic in the Greater Rochester, NY metropolitan region. Our findings evaluate the policy, provider mitigation, and response actions documenting the challenges they faced and the solutions they innovated. Implications for this research extend to climate-induced disruptions, including fires, water shortages, electric grid cyberattacks, and other disruptions where extended stay-at-home orders or service critical interventions are implemented.

The ongoing COVID-19 virus pandemic has resulted in a global tragedy due to its lethal spread. The population's vulnerability grows as a result of a lack of effective helping agents and vaccines against the virus. The spread of viruses can be mitigated by minimizing close connections between people. Social distancing is a critical containment tool for COVID-19 prevention. In this paper, the social distancing violations that are being made by the people when they are in public places are detected. As per CDC (Centers for Disease Control and Prevention) minimum distance that should be maintained by people is 2-3 meters to prevent the spread of COVID- 19, the proposed tool will be used to detect the people who are maintaining less than 2-3 meters of distance between themselves and record them as a violation. As a result, the goal of this work is to develop a deep learning-based system for object detection and tracking models in social distancing detection. For object detection models, You Only Look Once, Version 3 (YOLO v3) is used in conjunction with deep sort algorithms to balance speed and accuracy. To recognize persons in video segments, the approach applies the YOLOv3 object recognition paradigm. An efficient computer vision-based approach centered on legitimate continuous tracking of individuals is presented to determine supportive social distancing in public locations by creating a model to generate a supportive climate that contributes to public safety and detect violations through camera.

Public Key Infrastructure (PKI) as a techno-policy ecosystem for establishing electronic trust has survived for several decades and evolved as the de-facto model for centralized trust in electronic transactions. In this paper, we study the PKI ecosystem that are prevailing in the South Asian and Oceanic countries and brief them. We also look at how PKI has coped up with the rapid technological changes and how policies have been realigned or formulated to strengthen the PKI ecosystem in these countries.

As the COVID-19 pandemic scattered businesses and their workforces into new scales of remote work, vital security concerns arose surrounding remote access. Bring Your Own Device (BYOD) also plays a growing role in the ability of companies to support remote workforces. As more enterprises embrace concepts of zero trust in their network security posture, access control policy management problems become a more significant concern as it relates to BYOD security enforcement. This BYOD security policy must enable work from home, but enterprises have a vested interest in maintaining the security of their assets. Therefore, the BYOD security policy must strike a balance between access, security, and privacy, given the personal device use. This paper explores the challenges and opportunities of enabling zero trust in BYOD use cases. We present a BYOD policy specification to enable the zero trust access control known as BYOZ. Accompanying this policy specification, we have designed a network architecture to support enterprise zero trust BYOD use cases through the novel incorporation of continuous authentication & authorization enforcement. We evaluate our architecture through a demo implementation of BYOZ and demonstrate how it can meet the needs of existing enterprise networks using BYOD.

Cyber-security incidents have grown significantly in modern networks, far more diverse and highly destructive and disruptive. According to the 2021 Cyber Security Statistics Report [1], cybercrime is up 600% during this COVID pandemic, the top attacks are but are not confined to (a) sophisticated phishing emails, (b) account and DNS hijacking, (c) targeted attacks using stealth and air gap malware, (d) distributed denial of services (DDoS), (e) SQL injection. Additionally, 95% of cyber-security breaches result from human error, according to Cybint Report [2]. The average time to identify a breach is 207 days as per Ponemon Institute and IBM, 2022 Cost of Data Breach Report [3]. However, various preventative controls based on cyber-security risk estimation and awareness results decrease most incidents, but not all. Further, any incident detection delay and passive actions to cyber-security incidents put the organizational assets at risk. Therefore, the cyber-security incident management system has become a vital part of the organizational strategy. Thus, the authors propose a framework to converge a "Security Operation Center" (SOC) and a "Network Operations Center" (NOC) in an "Integrated Network Security Operation Center" (INSOC), to overcome cyber-threat detection and mitigation inefficiencies in the near-real-time scenario. We applied the People, Process, Technology, Governance and Compliance (PPTGC) approach to develop the INSOC conceptual framework, according to the requirements we formulated for its operation [4], [5]. The article briefly describes the INSOC conceptual framework and its usefulness, including the central area of the PPTGC approach while designing the framework.

The rapid shift towards smart cities, particularly in the era of pandemics, necessitates the employment of e-learning, remote learning systems, and hybrid models. Building adaptive and personalized education becomes a requirement to mitigate the downsides of distant learning while maintaining high levels of achievement. Explainable artificial intelligence (XAI), machine learning (ML), and the internet of behaviour (IoB) are just a few of the technologies that are helping to shape the future of smart education in the age of smart cities through Customization and personalization. This study presents a paradigm for smart education based on the integration of XAI and IoB technologies. The research uses data acquired on students' behaviours to determine whether or not the current education systems respond appropriately to learners' requirements. Despite the existence of sophisticated education systems, they have not yet reached the degree of development that allows them to be tailored to learners' cognitive needs and support them in the absence of face-to-face instruction. The study collected data on 41 learner's behaviours in response to academic activities and assessed whether the running systems were able to capture such behaviours and respond appropriately or not; the study used evaluation methods that demonstrated that there is a change in students' academic progression concerning monitoring using IoT/IoB to enable a relative response to support their progression.

The COVID19 pandemic situation has opened a wide range of opportunities for cyber-criminals, who take advantage of the anxiety generated and the time spent on the Internet, to undertake massive phishing campaigns. Although companies are adopting protective measures, the psychological traits of the victims are still considered from a very generic perspective. In particular, current literature determines that the model proposed in the Big-Five personality traits (i.e., Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism) might play an important role in human behaviour to counter cybercrime. However, results do not provide unanimity regarding the correlation between phishing susceptibility and neuroticism. With the aim to understand this lack of consensus, this article provides a comprehensive literature review of papers extracted from relevant databases (IEEE Xplore, Scopus, ACM Digital Library, and Web of Science). Our results show that there is not a well-established psychological theory explaining the role of neuroticism in the phishing context. We sustain that non-representative samples and the lack of homogeneity amongst the studies might be the culprits behind this lack of consensus on the role of neuroticism on phishing susceptibility.

Contact tracing is a particularly important part of health care and is often overlooked or forgotten up until right when it is needed the most. With the wave of technological achievements in the last decade, a digital perspective for aid in contact tracing was a natural development from traditional contact tracing. When COVID-19 was categorized as a pandemic, the need for modernized contact tracing solutions became apparent, and highly sought after. Solutions using the Bluetooth protocol and/or Global Positioning System data (GPS) were hastily made available to the public in nations all over the world. These solutions quickly became criticized by privacy experts as being potential tools for tracking.

The 2020 Corona pandemic has shown that on-line real-time multimedia communication is of vital importance when regular face-to-face meetings are not possible. One popular choice for conducting these meetings is the open standard WebRTC which is implemented in every major web browser. Even though this technology has found widespread use, there are still open issues with how different congestion control (CC) algorithms of Media- and DataChannels interact. In 2018 we have shown that the issue of self-inflicted queuing delay can be mitigated by introducing a CC coupling mechanism called FSE-NG. Originally, this solution was only capable of linking DataChannel flows controlled by TCP-style CCs and MediaChannels controlled by NADA CC. Standardization has progressed and along with NADA, IETF has also standardized the RTP CC SCReAM. This work extends the FSE-NG algorithm to also incorporate flows controlled by the latter algorithm. By means of simulation, we show that our approach is capable of drastically reducing end-to-end delays while also increasing RTP throughput and thus enabling WebRTC communication in scenarios where it has not been applicable before.

Background: Most of the people are not medically qualified for studying or understanding the extremity of their diseases or symptoms. This is the place where natural language processing plays a vital role in healthcare. These chatbots collect patients' health data and depending on the data, these chatbot give more relevant data to patients regarding their body conditions and recommending further steps also. Purposes: In the medical field, AI powered healthcare chatbots are beneficial for assisting patients and guiding them in getting the most relevant assistance. Chatbots are more useful for online search that users or patients go through when patients want to know for their health symptoms. Methods: In this study, the health assistant system was developed using Dialogflow application programming interface (API) which is a Google's Natural language processing powered algorithm and the same is deployed on google assistant, telegram, slack, Facebook messenger, and website and mobile app. With this web application, a user can make health requests/queries via text message and might also get relevant health suggestions/recommendations through it. Results: This chatbot acts like an informative and conversational chatbot. This chatbot provides medical knowledge such as disease symptoms and treatments. Storing patients personal and medical information in a database for further analysis of the patients and patients get real time suggestions from doctors. Conclusion: In the healthcare sector AI-powered applications have seen a remarkable spike in recent days. This covid crisis changed the whole healthcare system upside down. So this NLP powered chatbot system reduced office waiting, saving money, time and energy. Patients might be getting medical knowledge and assisting ourselves within their own time and place.

Emerging blockchain and cryptocurrency-based technologies are redefining the way we conduct business in cyberspace. Today, a myriad of blockchain and cryp-tocurrency systems, applications, and technologies are widely available to companies, end-users, and even malicious actors who want to exploit the computational resources of regular users through cryptojacking malware. Especially with ready-to-use mining scripts easily provided by service providers (e.g., Coinhive) and untraceable cryptocurrencies (e.g., Monero), cryptojacking malware has become an indispensable tool for attackers. Indeed, the banking industry, major commercial websites, government and military servers (e.g., US Dept. of Defense), online video sharing platforms (e.g., Youtube), gaming platforms (e.g., Nintendo), critical infrastructure resources (e.g., routers), and even recently widely popular remote video conferencing/meeting programs (e.g., Zoom during the Covid-19 pandemic) have all been the victims of powerful cryptojacking malware campaigns. Nonetheless, existing detection methods such as browser extensions that protect users with blacklist methods or antivirus programs with different analysis methods can only provide a partial panacea to this emerging crypto-jacking issue as the attackers can easily bypass them by using obfuscation techniques or changing their domains or scripts frequently. Therefore, many studies in the literature proposed cryptojacking malware detection methods using various dynamic/behavioral features. However, the literature lacks a systemic study with a deep understanding of the emerging cryptojacking malware and a comprehensive review of studies in the literature. To fill this gap in the literature, in this SoK paper, we present a systematic overview of cryptojacking malware based on the information obtained from the combination of academic research papers, two large cryptojacking datasets of samples, and 45 major attack instances. Finally, we also present lessons learned and new research directions to help the research community in this emerging area.

Research has been conducted on wireless network single link failures. However, cascaded link failures due to fraudulent attacks have not received enough attention, whereas this requires solutions. This research developed an enhanced genetic algorithm (EGA) focused on capacity efficiency and fast restoration to rapidly resolve link-link failures. On complex nodes network, this fault-tolerant model was tested for such failures. Optimal alternative routes and the bandwidth required for quick rerouting of video traffic were generated by the proposed model. Increasing cascaded link failures increases bandwidth usage and causes transmission delay, which slows down video traffic routing. The proposed model outperformed popular Dijkstra models, in terms of time complexity. The survived solution paths demonstrate that the proposed model works well in maintaining connectivity despite cascaded link failures and would therefore be extremely useful in pandemic periods on emergency matters. The proposed technology is feasible for current business applications that require high-speed broadband networks.

The advancement of AI enables the evolution of machines from relatively simple automation to completely autonomous systems that augment human capabilities with improved quality and productivity in work and life. The singularity is near! However, humans are still vulnerable. The COVID-19 pandemic reminds us of our limited knowledge about nature. The recent accidents involving Boeing 737 Max passengers ring the alarm again about the potential risks when using human-autonomy symbiosis technologies. A key challenge of safe and effective human-autonomy teaming is enabling “trust” between the human-machine team. It is even more challenging when we are facing insufficient data, incomplete information, indeterministic conditions, and inexhaustive solutions for uncertain actions. This calls for the imperative needs of appropriate design guidance and scientific methodologies for developing safety-critical autonomous systems and AI functions. The question is how to build and maintain a safe, effective, and trusted partnership between humans and autonomous systems. This talk discusses a context-based and interaction-centred design (ICD) approach for developing a safe and collaborative partnership between humans and technology by optimizing the interaction between human intelligence and AI. An associated trust model IMPACTS (Intention, Measurability, Performance, Adaptivity, Communications, Transparency, and Security) will also be introduced to enable the practitioners to foster an assured and calibrated trust relationship between humans and their partner autonomous systems. A real-world example of human-autonomy teaming in a military context will be explained to illustrate the utility and effectiveness of these trust enablers.

The COVID-19 pandemic introduced novel incentives for adversaries to exploit the state of turmoil. As we have witnessed with the increase in for instance phishing attacks and domain name registrations piggybacking the COVID-19 brand name. In this paper, we perform an analysis at Internet-scale of COVID-19 domain name registrations during the early stages of the virus' spread, and investigate the rationales behind them. We leverage the DomainTools COVID-19 Threat List and additional measurements to analyze over 150,000 domains registered between January 1st 2020 and May 1st 2020. We identify two key rationales for covid-related domain registrations. Online marketing, by either redirecting traffic or hosting a commercial service on the domain, and domain parking, by registering domains containing popular COVID-19 keywords, presumably anticipating a profit when reselling the domain later on. We also highlight three public policy take-aways that can counteract this domain registration behavior.

Protection of an organization's assets and information technology infrastructure is always crucial to any business. Securing and protecting businesses from cybersecurity threats became very challenging during the Covid-19 Pandemic. Organizations suddenly shifted towards remote work to maintain continuity and protecting against new cyber threats became a big concern for most business owners. This research looks into the following areas (i) outlining the shift from In-person to online work risks (ii) determine the cyber-attack type based on the list of 10 most prominent cybersecurity threats during the Covid-19 Pandemic (iii) and design a security policy to securing business continuity.

The Covid-19 Pandemic has caused huge losses worldwide and is still affecting people all around the world. Even after rigorous, incessant and dedicated efforts from people all around the world, it keeps mutating and spreading at an alarming rate. In times such as these, it is extremely important to take proper precautionary measures to stay safe and help to contain the spread of the virus. In this paper, we propose an innovative design of one such commonly used public disinfection method, an Automatic Walkthrough Sanitization Tunnel. It is a walkthrough sanitization tunnel which uses sensors to detect the target and automatically disinfects it followed by irradiation using UV-C rays for extra protection. There is a proposition to add an IoT based Temperature sensor and data relay module used to detect the temperature of any person entering the tunnel and in case of any anomaly, contact nearby covid wards to facilitate rapid treatment.

Given that an increasingly larger part of an organization's activity is taking place online, especially in the current situation caused by the COVID-19 pandemic, network log data collected by organizations contain an accurate image of daily activity patterns. In some scenarios, it may be useful to share such data with other parties in order to improve collaboration, or to address situations such as cyber-security incidents that may affect multiple organizations. However, in doing so, serious privacy concerns emerge. One can uncover a lot of sensitive information when analyzing an organization's network logs, ranging from confidential business interests to personal details of individual employees (e.g., medical conditions, political orientation, etc). Our objective is to enable organizations to share information about their network logs, while at the same time preserving data privacy. Specifically, we focus on enabling encrypted search at network flow granularity. We consider several state-of-the-art searchable encryption flavors for this purpose (including hidden vector encryption and inner product encryption), and we propose several customized encoding techniques for network flow information in order to reduce the overhead of applying state-of-the-art searchable encryption techniques, which are notoriously expensive.

The COVID-19 pandemic has impacted the world economy and mainly all activities where social distancing cannot be respected. In order to control this pandemic, screening tests such as PCR have become essential. For example, in the case of a trip, the traveler must carry out a PCR test within 72 hours before his departure and if he is not a carrier of the COVID-19, he can therefore travel by presenting, during check-in and boarding, the negative result sheet to the agent. The latter will then verify the presented sheet by trusting: (a) the medical biology laboratory, (b) the credibility of the traveler for not having changed the PCR result from “positive to negative”. Therefore, this confidence and this verification are made without being based on any mechanism of security and integrity, despite the great importance of the PCR test results to control the COVID-19 pandemic. Consequently, we propose in this paper a blockchain-based decentralized trust architecture that aims to guarantee the integrity, immutability and traceability of COVID-19 test results. Our proposal also aims to ensure the interconnection between several organizations (airports, medical laboratories, cinemas, etc.) in order to access COVID-19 test results in a secure and decentralized manner.