Digest: A Biometric Authentication Protocol in Wireless Sensor Network

Since the security of biometric information may be threatened by network attacks, presenting individual’s information without a suitable protection is not suitable for authorization. In traditional cryptographic systems, security was done using individual’s password(s) or driving some other data from primary information as secret key(s). However, encryption and decryption algorithms are slow and contain time-consuming operations for transferring data in network. Thus, it is better that we have no need to decrypt an encrypted trait of an enrolled person, and the system can encrypt the user trait with the user’s passwords and then compare the results with the enrolled persons’ encrypted data stored in database. In this chapter, by considering wireless sensor networks and authenticating server, we introduce a new concept called “digest” and deal with its efficiency in dealing with the security problem. A “digest” can be derived from any kind of information trait through which nobody can capture any information of primary biometric traits. We show that this concept leads to the increase of the accuracy and accessibility of a biometric system

From Bonehead to @realDonaldTrump : A Review of Studies on Online Usernames

In many online services, we are identified by self-chosen usernames, also known as nicknames or pseudonyms. Usernames have been studied quite extensively within several academic disciplines, yet few existing literature reviews or meta-analyses provide a comprehensive picture of the name category. This article addresses this gap by thoroughly analyzing 103 research articles with usernames as their primary focus. Despite the great variety of approaches taken to investigate usernames, three main types of studies can be identified: (1) qualitative analyses examining username semantics, the motivations for name choices, and how the names are linked to the identities of the users; (2) experiments testing the communicative functions of usernames; and (3) computational studies analyzing large corpora of usernames to acquire information about the users and their behavior. The current review investigates the terminology, objectives, methods, data, results, and impact of these three study types in detail. Finally, research gaps and potential directions for future works are discussed. As this investigation will demonstrate, more research is needed to examine naming practices in social media, username-related online discrimination and harassment, and username usage in conversations.Peer reviewe

A Survey of Protocol-Level Challenges and Solutions for Distributed Energy Resource Cyber-Physical Security

The increasing proliferation of distributed energy resources (DERs) on the smart grid has made distributed solar and wind two key contributors to the expanding attack surface of the network; however, there is a lack of proper understanding and enforcement of DER communications security requirements. With vendors employing proprietary methods to mitigate hosts of attacks, the literature currently lacks a clear organization of the protocol-level vulnerabilities, attacks, and solutions mapped to each layer of the logical model such as the OSI stack. To bridge this gap and pave the way for future research by the authors in determining key DER security requirements, this paper conducts a comprehensive review of the key vulnerabilities, attacks, and potential solutions for solar and wind DERs at the protocol level. In doing so, this paper serves as a starting point for utilities, vendors, aggregators, and other industry stakeholders to develop a clear understanding of the DER security challenges and solutions, which are key precursors to comprehending security requirements

Cyber-storms come from clouds:Security of cloud computing in the IoT era

The Internet of Things (IoT) is rapidly changing our society to a world where every “thing” is connected to the Internet, making computing pervasive like never before. This tsunami of connectivity and data collection relies more and more on the Cloud, where data analytics and intelligence actually reside. Cloud computing has indeed revolutionized the way computational resources and services can be used and accessed, implementing the concept of utility computing whose advantages are undeniable for every business. However, despite the benefits in terms of flexibility, economic savings, and support of new services, its widespread adoption is hindered by the security issues arising with its usage. From a security perspective, the technological revolution introduced by IoT and Cloud computing can represent a disaster, as each object might become inherently remotely hackable and, as a consequence, controllable by malicious actors. While the literature mostly focuses on the security of IoT and Cloud computing as separate entities, in this article we provide an up-to-date and well-structured survey of the security issues of cloud computing in the IoT era. We give a clear picture of where security issues occur and what their potential impact is. As a result, we claim that it is not enough to secure IoT devices, as cyber-storms come from Clouds

IoT Protocols And Security

During the past years, there has been an exponential growth of internet connected devices all over the world. In future the growth of these devices is expected to grow at the higher rate. There are some studies estimating that Internet of Things (IoT) will be able to connects 500 billion devices by 2030. IoT smart devices are remotely accessible and are possible to control using existing network infrastructure. At present, the usage of Internet of Things has increased rapidly. IoT is a dynamic global network between smart objects or things connected over the internet. IoT wireless network can connect anyone with anything at any place. With the rapid growth of IoT, security threats and vulnerabilities of the linked objects are also increasing continuously. Now, IoT security has become the most paramount technological research work over the world. The main objective of all IoT applications is to maintaining privacy and secure data transmission between devices. Due to the heterogeneous characteristics and constrained devices it is challenging to deploy security mechanisms in IoT compare to traditional network. In this thesis, we highlight the importance of security in the IoT sector by studying a wide range of IoT security issues. Furthermore, we described several challenges derived from the existing IoT protocols and the security features of IoT protocols are also explained. In addition, implementation of UDP communication protocol and MQTT protocol using Contiki OS and Zolertia RE-Mote devices are added to the work. Cryptographic methods AES [1] and ECC [2] are described in the thesis and the implementation of AES-128 to secure device communication and ECC key generation process are also added to the thesis work

Educational Technology and Related Education Conferences for June to December 2015

The 33rd edition of the conference list covers selected events that primarily focus on the use of technology in educational settings and on teaching, learning, and educational administration. Only listings until December 2015 are complete as dates, locations, or Internet addresses (URLs) were not available for a number of events held from January 2016 onward. In order to protect the privacy of individuals, only URLs are used in the listing as this enables readers of the list to obtain event information without submitting their e-mail addresses to anyone. A significant challenge during the assembly of this list is incomplete or conflicting information on websites and the lack of a link between conference websites from one year to the next

Reducing complexity in developing wireless sensor network systems using model-driven development

Wireless Sensor Network (WSN) is a collection of small and low-powered gadgets called sensor nodes (motes), which are capable of sensing the environment, collecting and processing the sensed data, and communicating with each other to accomplish a specific task. Moreover, all sensed and processed data are finally handed over to a central gathering point called a base station (sink), where all collected data are stored and can be reviewed by the user. Most of the current methods concerning WSN development are application or platform-dependent; hence it is not a trivial task to reuse developed applications in another environment. Therefore, WSN application development is a challenging and complex task because of the low-level technical details and programming complexity. Furthermore, most WSN development projects are managed by software engineers, not application field experts or WSN end users. Consequently, WSN solutions are considered expensive, due to the amount of effort that has to be put into these projects. This research project aims to reduce the complexity in developing WSN applications, by abstracting the low-level technical and programming details for average developers and domain experts. In this research, we argue that reducing complexity can be achieved by defining a new Domain-Specific Language (DSL) as a new application development and programming abstraction, which supports multi-levels modelling (i.e. network, group, and node-level). The outcome of this work is the definition of a new language called SenNet, which is an open source DSL programming abstraction that enables application developers to concentrate on the high-level application logic rather than the low-level complex details. SenNet was developed using the principles of Model-Driven Development (MDD) and macro-programming. Developers can use SenNet as a high-level programming abstraction to auto-generate a ready-to-deploy single node nesC code for all sensor nodes that comprise the SenNet application. SenNet gives developers the flexibility they need by offering them a broad range of predefined monitoring tasks and activities, enabling developers to develop different application types such as Sense-Forward (SF), and Event-Triggered (ET); besides providing a set of node-level and in-network data processing tasks. The current SenNet version is configured to generate nesC code, yet SenNet can be set up to produce and generate any programming language such as Java, or C++, by reconfiguring the code generator to produce the new language format, without changing the language design and produced semantics. Various tests and user study have been used to evaluate SenNet’s usability and functional suitability. Evaluation results found that SenNet could save 88.45% of the LOC required to be programmed by a developer, and 87.14% of the required vocabularies. Furthermore, results showed that SenNet could save 92.86% and 96.47% of the program length and volume respectively. Most of the user study participants (96%) found SenNet to be usable and helps to achieve the required WSN application with reduced development effort. Moreover, 82% of the participants believe that SenNet is functionally suitable for WSN application development. Two real-world business case studies developed were used to assess SenNet’s appropriateness to develop WSN real applications, and how it can be used to develop applications related to data processing tasks. Based on the final evaluation results, it can be concluded that our research has been successful in introducing SenNet as a new abstraction to reduce complexity in the WSN application development process

Computational Methods for Medical and Cyber Security

Over the past decade, computational methods, including machine learning (ML) and deep learning (DL), have been exponentially growing in their development of solutions in various domains, especially medicine, cybersecurity, finance, and education. While these applications of machine learning algorithms have been proven beneficial in various fields, many shortcomings have also been highlighted, such as the lack of benchmark datasets, the inability to learn from small datasets, the cost of architecture, adversarial attacks, and imbalanced datasets. On the other hand, new and emerging algorithms, such as deep learning, one-shot learning, continuous learning, and generative adversarial networks, have successfully solved various tasks in these fields. Therefore, applying these new methods to life-critical missions is crucial, as is measuring these less-traditional algorithms' success when used in these fields