Biblio

Nowadays the provision of online services by government or business organizations has become a standard and necessary operation. Transferring data including the confidential or sensitive information via Internet or insecure network and exchange of them is also increased day by day. As a result, confidential information leakage and cyber threats are also heightened. Confidential information trading became one of the most profitable businesses. Encrypting the data is a solution to secure the data from being exposed. In this paper, we would like to propose a solution for the secure transfer of data using symmetric encryption, asymmetric encryption technologies and Key Generation Server as a mixed hybrid solution. A Symmetric encryption, modified AES algorithm, is used to encrypt data. Digital certificate is used both for data encryption and digital signing to assure data integrity. Key generation server is used to generate the second secret key from the publicly recognized information of a person and this key is used as a second secret key in the modified AES. The proposed hybrid solution can be utilized in any applications that require high confidentiality, integrity of data and non-repudiation.

Identity management is an essential cornerstone of securing online services. Service provisioning relies on correct and valid attributes of a digital identity. Therefore, the identity provider is a trusted third party with a specific trust requirement towards a verified attribute supply. This trust demand implies a significant dependency on users and service providers. We propose a novel attribute aggregation method to reduce the reliance on one identity provider. Trust in an attribute is modelled as a combined assurance of several identity providers based on probability distributions. We formally describe the proposed aggregation model. The resulting trust model is implemented in a gateway that is used for authentication with self-sovereign identity solutions. Thereby, we devise a service provider specific web of trust that constitutes an intermediate approach bridging a global hierarchical model and a locally decentralized peer to peer scheme.

This paper investigates the suitability of employing various measurable features derived from multiple wearable devices (Apple Watch), for the generation of unique authentication and encryption keys related to the user. This technique is termed as ICMetrics. The ICMetrics technology requires identifying the suitable features in an environment for key generation most useful for online services. This paper presents an evaluation of the feasibility of identifying a unique user based on desirable feature set and activity data collected over short and long term and explores how the number of samples being factored into the ICMetrics system affects uniqueness of the key.

Open data is publicly available data that can be universally and readily accessed, used, and redistributed. Open data holds particular potential in the health and social sectors but, presently, health and social data are often published in a 'closed' format. There are different tools that allow to 'open' data, clean, structure and process them in order to elaborate them and build advanced services but, unfortunately, there is no single tool that can be used to perform all different tasks. We believe that the availability of Open Data in the health and social fields should be greatly increased and a way for creating new health and social services should be provided. In this paper, we present a framework that allows to create health and social Open Data starting from whatever is available on the web and to easily build advanced services based on those data.

The convergence of the Internet and mobile computing enables personalised access to online services anywhere and anytime. This potent access capability creates opportunities for new business models which stimulates vigorous investment and rapid innovation. Unfortunately, this innovation also produces new vulnerabilities and threats, and the new business models also create incentives for attacks, because criminals will always follow the money. Unless the new threats are balanced with appropriate countermeasures, growth in the Internet and mobile services will encounter painful setbacks. Security and trust are two fundamental factors for sustainable development of identity management in online markets and communities. The aim of this study is to present an overview of the central aspects of identity management in the Internet and mobile computing with respect to security and trust.

Denial of Service (DoS) and Distributed Denial of Service (DDoS) attack, exhausts the resources of server/service and makes it unavailable for legitimate users. With increasing use of online services and attacks on these services, the importance of Intrusion Detection System (IDS) for detection of DoS/DDoS attacks has also grown. Detection accuracy & CPU utilization of Data mining based IDS is directly proportional to the quality of training dataset used to train it. Various preprocessing methods like normalization, discretization, fuzzification are used by researchers to improve the quality of training dataset. This paper evaluates the effect of various data preprocessing methods on the detection accuracy of DoS/DDoS attack detection IDS and proves that numeric to binary preprocessing method performs better compared to other methods. Experimental results obtained using KDD 99 dataset are provided to support the efficiency of proposed combination.