Modern information society depends on reliable functionality of information systems infrastructure, while at the same time the number of cyber-attacks has been increasing over the years and damages have been caused. Furthermore, graphs can be used to show paths than can be exploited by attackers to intrude into systems and gain unauthorized access through vulnerability exploitation. This paper presents a method that builds attack graphs using data supplied from the maritime supply chain infrastructure. The method delivers all possible paths that can be exploited to gain access. Then, a recommendation system is utilized to make predictions about future attack steps within the network. We show that recommender systems can be used in cyber defense by predicting attacks. The goal of this paper is to identify attack paths and show how a recommendation method can be used to classify future cyber-attacks in terms of risk management. The proposed method has been experimentally evaluated and validated, with the results showing that it is both practical and effective.

Purpose This paper aims to address privacy concerns that arise from the use of mobile recommender systems when processing contextual information relating to the user. Mobile recommender systems aim to solve the information overload problem by recommending products or services to users of Web services on mobile devices, such as smartphones or tablets, at any given point in time and in any possible location. They use recommendation methods, such as collaborative filtering or content-based filtering and use a considerable amount of contextual information to provide relevant recommendations. However, because of privacy concerns, users are not willing to provide the required personal information that would allow their views to be recorded and make these systems usable. Design/methodology/approach This work is focused on user privacy by providing a method for context privacy-preservation and privacy protection at user interface level. Thus, a set of algorithms that are part of the method has been designed with privacy protection in mind, which is done by using realistic dummy parameter creation. To demonstrate the applicability of the method, a relevant context-aware data set has been used to run performance and usability tests. Findings The proposed method has been experimentally evaluated using performance and usability evaluation tests and is shown that with a small decrease in terms of performance, user privacy can be protected. Originality/value This is a novel research paper that proposed a method for protecting the privacy of mobile recommender systems users when context parameters are used.

Collaborative recommender systems offer a solution to the information overload problem found in online environments such as e-commerce. The use of collaborative filtering, the most widely used recommendation method, gives rise to potential privacy issues. In addition, the user ratings utilized in collaborative filtering systems to recommend products or services must be protected. The purpose of this research is to provide a solution to the privacy concerns of collaborative filtering users, while maintaining high accuracy of recommendations. This paper proposes a multi-level privacy-preserving method for collaborative filtering systems by perturbing each rating before it is submitted to the server. The perturbation method is based on multiple levels and different ranges of random values for each level. Before the submission of each rating, the privacy level and the perturbation range are selected randomly from a fixed range of privacy levels. The proposed privacy method has been experimentally evaluated with the results showing that with a small decrease of utility, user privacy can be protected, while the proposed approach offers practical and effective results.

Abstract. Modern information society depends on reliable functionality of information systems infrastructure, while at the same time the number of cyberattacks has been increasing over the years and damages have been caused. Furthermore, graphs can be used to show paths than can be exploited by attackers to intrude into systems and gain unauthorized access through vulnerability exploitation. This paper presents a method that builds attack graphs using data supplied from the maritime supply chain infrastructure. The method delivers all possible paths that can be exploited to gain access. Then, a recommendation system is utilized to make predictions about future attack steps within the network. We show that recommender systems can be used in cyber defense by predicting attacks. The goal of this paper is to identify attack paths and show how a recommendation method can be used to classify future cyber-attacks. The proposed method has been experimentally evaluated and it is shown that it is both practical and effective.

This paper identifies the factors that have an impact on mobile recommender systems. Recommender systems have become a technology that has been widely used by various online applications in situations where there is an information overload problem. Numerous applications such as e-Commerce, video platforms and social networks provide personalized recommendations to their users and this has improved the user experience and vendor revenues. The development of recommender systems has been focused mostly on the proposal of new algorithms that provide more accurate recommendations. However, the use of mobile devices and the rapid growth of the internet and networking infrastructure has brought the necessity of using mobile recommender systems. The links between web and mobile recommender systems are described along with how the recommendations in mobile environments can be improved. This work is focused on identifying the links between web and mobile recommender systems and to provide solid future directions that aim to lead in a more integrated mobile recommendation domain. Keywords Mobile recommender systems, Collaborative filtering, Context, PrivacyRecommender systems, depending on the method they employ can be classified in one of the following categories (Bobadilla et al., 2013; Jannach et al., 2010;Shi, Larson, & Hanjalic, 2014;Su & Khoshgoftaar, 2009):Collaborative filtering. These recommenders suggest items to users that other users with a similar rating history have liked in the past.Content-based. These recommenders suggest items to users that are similar to the items that user has liked in the past.Knowledge-based. These recommenders suggest items to users based either on inferences about the preferences of users or by utilizing specific domain knowledge.

Recommender systems evaluation is usually based on predictive accuracy metrics with better scores meaning recommendations of higher quality. However, the comparison of results is becoming increasingly difficult, since there are different recommendation frameworks and different settings in the design and implementation of the experiments. Furthermore, there might be minor differences on algorithm implementation among the different frameworks. In this paper, we compare well known recommendation algorithms, using the same dataset, metrics and overall settings, the results of which point to result differences across frameworks with the exact same settings. Hence, we propose the use of standards that should be followed as guidelines to ensure the replication of experiments and the reproducibility of the results.

Abstract:The digital divide has been mostly affecting the world's poorest primarily due to lack of internet connectivity. A number of e-government services especially built to serve low income citizens do not reach those in need. The rapid expansion of the use of the mobile phone raises hopes that the digital divide can be bridged faster by providing services to people living in rural and remote areas. The authors review successful implementations of secure and trusted e-services available on mobile networks. These could be utilised in addressing the needs of those living in rural areas and are currently digitally marginalised.Keywords: trust; secure mobile services; rural inhabitants; digital divide; mobile payment systems.Reference to this paper should be made as follows: Pimenidis, E., Sideridis, A.B. and Antonopoulou, E. (2009) 'Mobile devices and services: bridging the digital divide in rural areas ', Int. J. Electronic Security and Digital Forensics, Vol. 2, No. 4, Biographical notes: Elias Pimenidis is a Senior Lecturer at the University of East London in the UK. The core of his research work focuses on e-business and e-government development projects. His interest in security issues is on online transactions and e-services and in particular, on the effect, these could have on e-government implementations. Other research interests include the evaluation of web services, knowledge management systems and the use of computer games for educational purposes. He is also a Visiting Lecturer at the Informatics laboratory of the Agricultural University of Athens, Greece. Mobile devices and services: bridging the digital divide in rural areas 425A.B. Sideridis is Professor in Computer Science and Head of the Informatics Laboratory of the Agricultural University of Athens. He earned his first Degree at the University of Athens and his MSc and PhD from Brunel University. He has been the project leader of more than 30 successful national and international projects and has published more than 180 scientific papers in management and decision support systems, computer networking related to local and central administration activities, advanced computational numerical modelling, informatics and impact of computers in society and agricultural informatics.E. Antonopoulou holds a diploma in Plant Science and currently is a PhD candidate in the Informatics Laboratory of the Agricultural University of Athens. Her doctorate research is focusing to Decision Support Systems design for major field crops.

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