The UK has created one of the best tools for mitigating Huawei’s risks. Whether or not the UK Huawei ban stands, its Huawei Cyber Security Evaluation Centre should receive increased funding and support to protect and enhance its interests at home and abroad.
This analytical paper asks, does the One-China policy shape the People’s Republic of China’s foreign policy? This paper begins by briefly defining the One-China policy and situating it in the respective histories of China and its current incarnation as the People’s Republic of China (PRC). Then, after untangling the often muddled classifications of soft, sharp, and hard power, the question is interrogated in the context of each class of power (Nye, 2004; Nye, 2011; Nye, 2018; Raby, 2019; Walker & Ludwig, 2017). This analytical essay concludes that the PRC does employ predominantly sharp and hard power strategies that are heavily influenced by the One-China policy.
In a world where technology continues to vastly grow and improve, IoT devices have increasingly become more and more a part of people’s everyday lives. Although that is the case there is a need to understand how to better use these devices for threat detection. This paper presents early work to understand gaps in this regard using a review of previously used techniques to identify known threats to households. Through the use of smart home device clusters we seek to effectively reduce the amount of false alarms and create a more reliable resource for home residents.
Small office home office networks have become a target for many threat actors, hackers and cyber attackers and hence there is an urgent need to secure the network from such attackers. Most small office home office network users do not see the need to provide enough security to their networks because they assume no one is going to hack them forgetting that the biggest threat of our small home networks today comes from the outside. The challenge of misconfiguration of routers, firewalls and default configurations in our small home networks renders the network vulnerable to attacks such as DDos , phishing attacks , virus and other network attacks hence the need to implement a detection algorithm to help identify flaws in the pattern of the small office network. It turns out that about 75% of existing approaches focused on intrusion detection in 802.11 wireless networks of a SOHO and not the entire network. These approaches do not efficiently secure the network entirely leaving the rest prone to attacks can occur with or without the internet. This paper proposes to add another layer of security to the other preventive measures in a SOHO network by designing, implementing and testing a supervised neural network algorithm to identify attacks on the small home network and also to send a notification to users to keep them informed of the activities on their network. The supervised neural network algorithm will have a dataset representing both attacks and non-attacks which will be used in the training phase. The system should be able to detect and identify the various attacks and anomalies when they occur on the network and help keep the users informed.
The current rapid growth in the computer and internet development has ushered in numerous cybersecurity challenges which are constantly evolving with time. The current cybersecurity solutions are no longer optimal in tackling these emerging cyber threats and attacks. This paper proposes the creation of a cybersecurity dataset to be used for a hybrid machine learning (ML) approach of supervised and unsupervised learning for an effective intrusion detection system. The proposed model entails a five-stage process which starts at the setup of a simulated network environment of network attacks to generate a dataset which feeds into the data normalization stage and then to data dimension reduction stage using the principal component analysis as a feature extraction method after which the data of reduced dimension is clustered using the k-Means method to bring about a new data set with fewer features. This new dataset is afterward classified using the enhanced support vector machine (ESVM). The proposed model is expected to provide a high-quality dataset and an efficient intrusion detection system in terms of intrusion detection accuracy of 99.5%, short train time of 3seconds and a low false-positive rate of 0.4%.
This research focuses on two fundamental aspects of hot spot policing that have been widely neglected by previous scholarly research. These aspects include the adequate concentration of crime at a smaller geographical unit to be considered a crime hot spot, and the cost-benefit implication of focusing limited police resources on such a smaller place in an effort to prevent criminal activities. Substantial limitations in call-t- service data from police departments raise concern on the purported concentration of crime at places that warrant such strategy in the first place. We will examine data from the Cincinnati Police Department and propose guidelines on adopting a threshold when designating places as crime hot spots, using time and cost-benefit analysis as key determinants.
Cyberspace is one of the most complex systems ever built by humans. The utilization of cybertechnology resources are used ubiquitously by many, but sparsely understood by the majority of the users. In the past, cyberattacks were usually orchestrated in a random pattern of attack to lure unsuspecting targets. However, the cyber virtual environment is an ecosystem that provided a platform for an organized and sophisticated approach to launch an attack against a specific target group or organization by nefarious actors. In 2019, the average cost of cyber-attack in the US was about $1.6 million. This paper proposes a 3D framework to signal new threat alert before the actual occurrence of the threat on the surface web to alert cybersecurity experts and law enforcement agencies in preventive measures or means of mitigating the severity of damage caused by cyberattacks. The methodology combines information extracted from the deep web through a smart web crawler with socio-personal and technical indicators from twitter which is mapped with OTX (Open Threat Exchange). The OTX is an open-source cyber threat platform managed by security experts. The OTX endpoint security tool(OTX python SDK) will be used to identify a new type of cyber threats. The effectiveness of the framework will be tested using the machine learning algorithm precision-recall rate.
This paper looks at the opportunities and challenges of implementing blockchain technology across the medical sector and provides a clear view which can enable blockchain for more extents. After a notable research on underlying blockchain technology which offers distributed governance, immutable audit trail, provenance of data, robustness and privacy, we contrasted blockchain innovations and identified prominent applications of it in historically decentralized healthcare sectors. As the healthcare industry faces many challenges like unauthorised data sharing, lack of data transparency, ransomware, data breaches and cyber crimes, blockchain is one of the best ways to enhance data sharing and to mitigate prominent cyber crimes. By proper designing of a decentralized and immutable blockchain network where the data is dispersed among credentialed social insurance experts guarantees that cybercriminals cannot touch single patient’s confidential data, which facilitates encryption or cryptography of personal data where no patient’s emergency data is at extreme hazard. Blockchain trust-worthy cloud is one of the most powerful and secure ways of storing high confidential data. After analysing Blockchain implementations and identifying its potential in healthcare, we conclude with several promising directions for future research.
National Institute of Standards and Technology (NIST) recommends that organizations perform cyber risk assessments regularly to identify security vulnerabilities and to control levels of exposure to threats. We discuss a method to customize the ranking of cyber threats based on the organization’s maturity level of implementing NIST controls and we use FAIR model’s LEF component as a measure of the severity of cyber threats. The methodology integrates NIST maturity levels to calculate the resistance strength factor and produce the LEF values for each threat. The LEF value is then used to represent the severity level of the threat to the specific organization. This hybrid risk assessment approach will help stakeholders make data-informed decisions on improving security measures and provide accurate values that represent the current security state of their organization.
Signature-based intrusion detection methods report high accuracy with a low false alarm rate. However, they do not perform well when faced with new or emerging threats. This work focuses on anomaly-based data driven methods to identify potential zero-day-attacks using a specific class of neural networks known as the autoencoder.
Previous studies have offered a variety of explanations on the relationship between democracy and the internet. Some argue that with free access to information, knowledge sharing without any constraint, and the spread of political knowledge, the internet will help change people’s political attitudes and spread democracy. Other studies found that authoritarian regimes by censoring the internet, tracking the political activist, prosecuting the dissidents, and using the internet to spread their propaganda limit the democratization. Also, some studies explored the effects of diffusion of false news through the internet and especially via social media. However, most of these studies concentrate on regions, specific states or authoritarian regimes. No study has investigated the influence of the internet in partly free countries defined by the Freedom House. Moreover, very little is known about the effects of online censorship on the development, stagnation, or decline of democracy. To fully understand the impact of the internet and online censorship on democratization in partly free countries, we must explore these relationships in these countries. Drawing upon the International Telecommunication Union, Freedom House, and World Bank reports and using machine learning methods, this study sheds new light on the effects of the internet on democratization in partly free countries. The findings suggest that internet penetration and online censorship both have a negative impact on democracy scores and the internet’s effect on democracy scores is conditioned by online censorship. Moreover, results from random forest suggest that online censorship is the most important variable followed by governance index and education on democracy scores.
The paper focuses on exploring the social networks of technology caregivers and caregivees and also work on learning their preferred mode of information exchange. Responses from the participants of the study will throw light on the relationships between different efficacies (discussed in detail in the paper) that may have an impact on an individual’s decision. Participant’s responses are recorded through well constructed surveys that have been distributed around by word of mouth or specific social media platforms which will also prove if being a power user has any effect on the end result. The responses will be analyzed and the various efficacy constructs such as self efficacy, community collective efficacy will be kept in mind.
This study proposes enhanced oversight of smart homes by leveraging the social networks of homeowners to co-monitor for emergencies, while being mindful of privacy preserving features necessary for adoption. A pilot co-design workshop was conducted to determine features for co-monitoring. A group of four participants provided early findings and informed modifications to study design, and new insights for user behavior were emphasized. By refining the study design, we hope to better target users’ tacit knowledge in future workshops. Early findings include the users’ need for more than simply sharing access to a camera during an emergency in the home; users desired control over the microphone, the camera video stream, and the length of time. We believe this work will contribute to a broader understanding of features that better meet the needs and goals of smart device owners to enable co-monitoring.
This is an ongoing research project focused on creating a framework for capturing various artifacts concerning Internet of Things devices. Research has shown a severe lack of frameworks focusing on collecting data from and about IoT devices. Mozilla’s WebThings Gateway focuses on collecting this information from the devices. This project expects to find methods of IoT data collection through a proposed test-bed utilizing the WebThings Gateway.
Open Government Data (OGD) promotes transparency, innovation, and value creation that makes information gathered by the government about the city and community open to all. The City of Cincinnati Open Government Data Portal allows citizens to access local data as part of a local OGD initiative. Although these datasets are available and are used by a broad audience, little is known about how users engage with this data and the general usability of the platforms. To learn more about this audience, this study is conducted in two steps 1)- a think-aloud activity and 2)- an online survey. Through these activities, we aim to gather information about how the users are interacting with the available data and for what purpose are they interacting with different sections of the portal. After gathering relevant data from the think-aloud activity, we aim to generate a questionnaire by analyzing all the information collected in the previous event at a larger scale. Using a web-based survey shared with individuals via Qualtrics, we will explore the use of the portal to gain more insight and knowledge on user requirements and their suggestions. The endpoint of this study is to develop insights that will help us understand user expectations and how changes could benefit the portal.
Internet of Things (IoT) enabled smart homes to have made our daily lives easier, but these conveniences have also introduced security concerns. IoT devices hold security risks as well as smart home hubs and gateways. Gateways present a centralized point of communication among devices that can create a backdoor into network data for hackers but also present a detection opportunity. Intrusion detection is a common way to detect anomalies in network traffic. This paper introduces early work on an intrusion detection system (IDS) by detecting anomalies in the smart home network using Extreme Learning Machine and Artificial Immune System (AIS ELM). AIS uses the clonal Algorithm for the optimization of the input parameters, and ELM analyzes the input parameter for better convergence in detecting anomalous activity. The larger goal of this work is to apply this approach to a smart home network gateway and combined it with a push notification system that will allow the homeowner to identify any abnormalities in the smart home network and take appropriate action.
Devices in the Internet of Things (IoT) have enhanced our ability to automate functions in smart homes and increased our ability to monitor day to day activities regardless of whether we are in our home. Despite these benefits of IoT devices, it is the case that notifications about threats to our home when we are away are typically only sent to one or two people within the home. We proposed enhanced monitoring of threats by allowing temporary access to IoT devices to extended networks of homeowners in situations where primary IoT device owners are not able to address a smartphone notification quickly.
As incessant cyber-attacks on organizations increase in complexity and destructiveness with the aim
to disrupt services and steal information, proactive measures are critically needed to mitigate these
attacks, cyber security risk assessment tops the list of measures. This study provides an overview of
cybersecurity risk assessment, various types of frameworks, and the difference between qualitative
and quantitative cybersecurity risk assessments. The aim of this early research is the creation of a
hybrid system which integrates an existing cybersecurity risk assessment system based on the NIST framework into the Factor Analysis of Information Risk (FAIR) model, an analytic risk assessment model that enables true quantitative measurement. In this study, we propose a hybrid-assessment tool which will be used to describe and compare the impact of using NIST driven values
as inputs for the resistance strength to determine the Loss Event Frequent (LEF) and Annual Loss
Expectancy (ALE) of a risk scenario as opposed to using experts’ opinion as user inputs for determination of the LEF and ALE values.
A great deal of data is generated every day on social media, although this information is used for marketing purposes regularly, it has the potential to serve other purposes, such as in crisis management. This study focuses on collecting data from social media, specifically Twitter, in order to help 911 telecommunicators (floor supervisors, call takers, and dispatchers) to 1) identify Twitter users requesting assistance during a crisis, 2) identify information that may be useful to incidents that were called into 911, and 3) pass the information to the first responders (police, fire, and emergency medical services). Previous research in this area can be summarized into three stages. First, a set of information requirements has been developed that must be satisfied to dispatch first responders and meet their immediate awareness needs. Second, a coding schema has been presented to identify six types of actionable information. Finally, it proposed automated methods based on previous literature which can be used to implement these methods in the future (Kropczynski et al. 2018). This research concentration is on refining social media data by starting with finding local tweets that contain this information and recognize patterns of how it is used. Next, patterns will be used in the development of automated methods in the future. The contribution of this work is extending the coding schema of the 6Ws and put it on an action, develop an interface to view the data of social media separated by the 6Ws. It will begin with just on of the six Ws (Weapons).
Depression is a common illness that negatively affects feelings, thoughts and behaviors and can harm regular activities like sleeping. It is a leading cause of disability and many other diseases (Choudhury, et al 2013, Mathur et al, 2016, Watkins et al, 2013). According to WHO (World Health Organization) 1 statistics, more than 300 million people over the world are affected in depression and in each country at least 10% are provided treatment. Poor recognition and treatment of depression may aggravate heart failure symptoms, precipitate functional decline, disrupt social and occupational functioning, and lead to an increased risk of mortality (Cully, et al 2009). Early detection of depression is thus necessary. Unfortunately the rates of detecting and treating depression among those with medical illness are quite low (Egede, 2007). This research proposes a solution of using random forest classifier algorithm to detect and predict detection. A mobile application will be developed in order to collect user data and make prediction.