Scalable RFID security framework and protocol supporting Internet of Things

Radio-frequency identification (RFID) is seen as one of the requirements for the implementation of the Internet-of-Things (IoT). However, an RFID system has to be equipped with a holistic security framework for a secure and scalable operation. Although much work has been done to provide privacy and anonymity, little focus has been given to performance, scalability and customizability issues to support robust implementation of IoT. Also, existing protocols suffer from a number of deficiencies such as insecure or inefficient identification techniques, throughput delay and inadaptability. In this paper, we propose a novel identification technique based on a hybrid approach (group-based approach and collaborative approach) and security check handoff (SCH) for RFID systems with mobility. The proposed protocol provides customizability and adaptability as well as ensuring the secure and scalable deployment of an RFID system to support a robust distributed structure such as the IoT. The protocol has an extra fold of protection against malware using an incorporated malware detection technique. We evaluated the protocol using a randomness battery test and the results show that the protocol offers better security, scalability and customizability than the existing protocols. © 2014 Elsevier B.V. All rights reserved.

Iterative construction of hierarchical classifiers for phishing website detection

This article is devoted to a new iterative construction of hierarchical classifiers in SimpleCLI for the detection of phishing websites. Our new construction of hierarchical systems creates ensembles of ensembles in SimpleCLI by iteratively linking a top-level ensemble to another middle-level ensemble instead of a base classifier so that the top-level ensemble can generate a large multilevel system. This new construction makes it easy to set up and run such large systems in SimpleCLI. The present article concentrates on the investigation of performance of the iterative construction of such classifiers for the example of detection of phishing websites. We carried out systematic experiments evaluating several essential ensemble techniques as well as more recent approaches and studying their performance as parts of the iterative construction of hierarchical classifiers. The results presented here demonstrate that the iterative construction of hierarchical classifiers performed better than the base classifiers and standard ensembles. This example of application to the classification of phishing websites shows that the new iterative construction combining diverse ensemble techniques into the iterative construction of hierarchical classifiers can be applied to increase the performance in situations where data can be processed on a large computer. © 2014 ACADEMY PUBLISHER.

Large iterative multitier ensemble classifiers for security of big data

This paper introduces and investigates large iterative multitier ensemble (LIME) classifiers specifically tailored for big data. These classifiers are very large, but are quite easy to generate and use. They can be so large that it makes sense to use them only for big data. They are generated automatically as a result of several iterations in applying ensemble meta classifiers. They incorporate diverse ensemble meta classifiers into several tiers simultaneously and combine them into one automatically generated iterative system so that many ensemble meta classifiers function as integral parts of other ensemble meta classifiers at higher tiers. In this paper, we carry out a comprehensive investigation of the performance of LIME classifiers for a problem concerning security of big data. Our experiments compare LIME classifiers with various base classifiers and standard ordinary ensemble meta classifiers. The results obtained demonstrate that LIME classifiers can significantly increase the accuracy of classifications. LIME classifiers performed better than the base classifiers and standard ensemble meta classifiers.

vPELS : An e-learning social environment for VLSI design with content security using DRM

This article provides a proposal for personal e-learning system (vPELS [where 'v' stands for VLSI: very large scale integrated circuit])) architecture in the context of social network environment for VLSI Design. The main objective of vPELS is to develop individual skills on a specific subject - say, VLSI - and share resources with peers. The authors' system architecture defines the organisation and management of the personal learning environment in such a way as to aid in creating, verifying and sharing learning artefacts and making money at the same time. The authors also focus in their research on one of the most interesting arenas in digital content or document management - Digital Rights Management (DRM) - and its application to e-learning.

Automatic generation of meta classifiers with large levels for distributed computing and networking

This paper is devoted to a case study of a new construction of classifiers. These classifiers are called automatically generated multi-level meta classifiers, AGMLMC. The construction combines diverse meta classifiers in a new way to create a unified system. This original construction can be generated automatically producing classifiers with large levels. Different meta classifiers are incorporated as low-level integral parts of another meta classifier at the top level. It is intended for the distributed computing and networking. The AGMLMC classifiers are unified classifiers with many parts that can operate in parallel. This make it easy to adopt them in distributed applications. This paper introduces new construction of classifiers and undertakes an experimental study of their performance. We look at a case study of their effectiveness in the special case of the detection and filtering of phishing emails. This is a possible important application area for such large and distributed classification systems. Our experiments investigate the effectiveness of combining diverse meta classifiers into one AGMLMC classifier in the case study of detection and filtering of phishing emails. The results show that new classifiers with large levels achieved better performance compared to the base classifiers and simple meta classifiers classifiers. This demonstrates that the new technique can be applied to increase the performance if diverse meta classifiers are included in the system.

A competitive three-level pruning technique for information security

The reduction of size of ensemble classifiers is important for various security applications. The majority of known pruning algorithms belong to the following three categories: ranking based, clustering based, and optimization based methods. The present paper introduces and investigates a new pruning technique. It is called a Three-Level Pruning Technique, TLPT, because it simultaneously combines all three approaches in three levels of the process. This paper investigates the TLPT method combining the state-of-the-art ranking of the Ensemble Pruning via Individual Contribution ordering, EPIC, the clustering of the K-Means Pruning, KMP, and the optimisation method of Directed Hill Climbing Ensemble Pruning, DHCEP, for a phishing dataset. Our new experiments presented in this paper show that the TLPT is competitive in comparison to EPIC, KMP and DHCEP, and can achieve better outcomes. These experimental results demonstrate the effectiveness of the TLPT technique in this example of information security application.

RFID tag ownership transfer protocol for a closed loop system

In the RFID system a tag is attached to an object which might own by a number of people during its life cycle. As a result, the RFID system requires to transfer ownership of the tag. The ownership transfer has to protect privacy of current and new owner. There are number of ownership transfer protocol proposed to achieve secure ownership transfer. However, most of them are impractical or insecure to implement on current passive RFID tags. We are presenting an ownership transfer protocol using timer based shared secret for closed loop RFID systems. The protocol will ensure security and privacy of involved parties in the idle circumstances. Our comparison shows that the proposed protocol is more secure and practical than existing similar ones.

A framework for mobile elearning (mLearning) with analytical decision model

Mobile eLearning (mLearning) can create a revolution in eLearning with the popularity of smart mobile devices and Application. However, contents are the king to make this revolution happen. Moreover, for an effective mLearning system, analytical aspects such as, quality of contents, quality of results, performance of learners, needs to be addressed. This paper presents a framework for personal mLearning. In this paper, we have used graph-based model called bipartite graph for content authentication and identification of the quality of results. Furthermore, we have used statistical estimation process for trustworthiness of weights in the bipartite graph using confidence interval and hypothesis test as analytical decision model tool.

PELS: a noble architecture and framework for a personal E-learning system (PELS)

This article presents a personal e-learning system architecture in the context of a social network environment. The main objective of a personal e-learning system is to develop individual skills on a specific subject and share resources with peers. The authors' system architecture defines the organisation and management of a personal learning environment that aids in creating, verifying and sharing learning artefacts, and making money at the same time. In their research, they also focus on one of the most interesting arenas in digital content or document management - digital rights management - and its application to e-learning.

PUF-based secure checker protocol for Networked RFID Systems

Radio Frequency Identification (RFID) system is anemerging technology for automating object identification. TheNetworked RFID System (NRS) is a component of a distributedobject identification network which facilitates automated supplychain management. It also makes the Internet of Things (IoT)concept a reality. To increase the business feasibility of NRSimplementation, the system should be able to ensure the visibilityand traceability of the object throughout the chain using achecker protocol. By doing so, the protocol will check thegenuineness of the object and the genuineness of the object’sprevious travel path on-site. While doing so, the protocol needs toensure the security requirement of the system. To this end, wepropose a secure checker protocol for NRS which will use a PUF(Physically Unclonable Function) and simple cryptographicprimitives. The protocol provides security (protect privacy of thepartners, injection of fake objects, non-repudiation, andunclonability), visibility and traceability for NRS. It is alsosuitable for passive tags.

Impact of Assurance of Learning (AOL) in programming course for novices

One of the aims of any higher education institution is to align its curriculum with program learning goals. Programs which ensure proper learning have positive effects on students, instructors, departments and also on the higher education institution itself. This paper discusses the implementation and effects of Assurance Of Learning (AOL) processes on introductory programming (IP) courses. It elaborates five stages of AOL to align program learning goals with IP curriculum. Then, it discusses how the AOL process identifies shortcomings in the assessment methods of IP courses. Furthermore, it enlightens how the assessment findings, as a result of the AOL process, provide mechanisms to address the drawbacks during the delivery of such courses. Feedback on

Scenarios for an RFID tag ownership transfer protocol for a closed loop system

In RFID system a tag is attached to an object which might own by a number of owners during its life time. This requires the RFID system to transfer ownership of the tag to its new owner. The ownership transfer has to protect privacy of current and new owner. Many ownership tag ownership transfer exists in the literature, however, most of them are impractical or insecure to implement on current passive RFID tags. We are proposing a timer based ownership transfer protocol for closed loop RFID systems. The proposal in this paper includes two implement scenario to cover diverse tags type. The protocol will ensure security and privacy of involved parties in the idle circumstances. Our comparison shows that the proposed protocol is more secure and practical than existing similar ones.