SLEC: A Novel Serverless RFID Authentication Protocol Based on Elliptic Curve Cryptography

Internet of Things (IoT) is a new paradigm that has been evolving into the wireless sensor networks to expand the scope of networked devices (or things). This evolution drives communication engineers to design secure and reliable communication at a low cost for many network applications such as radio frequency identification (RFID). In the RFID system, servers, readers, and tags communicate wirelessly. Therefore, mutual authentication is necessary to ensure secure communication. Normally, a central server supports the authentication of readers and tags by distributing and managing the credentials. Recent lightweight RFID authentication protocols have been proposed to satisfy the security features of RFID networks. Using a serverless RFID system is an alternative solution to using a central server. In this model, both the reader and the tag perform mutual authentication without the need for the central server. However, many security challenges arise from implementing lightweight authentication protocols in serverless RFID systems. We propose a new secure serverless RFID authentication protocol based on the famous elliptic curve cryptography (ECC). The protocol also maintains the confidentiality and privacy of the messages, tag information, and location. Although most of the current serverless protocols assume secure channels in the setup phase, we assume an insecure environment during the setup phase between the servers, readers, and tags. We ensure that the credentials can be renewed by any checkpoint server in the mobile RFID network. Thus, we implement ECC in the setup phase (renewal phase), to transmit and store the communication credentials of the server to multiple readers so that the tags can perform the mutual authentication successfully while far from the server. The proposed protocol is compared with other serverless frameworks proposed in the literature in terms of computation cost and attacks resistance.http://dx.doi.org/10.3390/electronics810116

SLEC: A Novel Serverless RFID Authentication Protocol Based on Elliptic Curve Cryptography

Radio Frequency Identification (RFID) is one of the leading technologies in the Internet of Things (IoT) to create an efficient and reliable system to securely identify objects in many environments such as business, health, and manufacturing areas. Since the RFID server, reader, and tag communicate via insecure channels, mutual authentication between the reader and the tag is necessary for secure communication. The central database server supports the authentication of the reader and the tag by storing and managing the network data. Recent lightweight RFID authentication protocols have been proposed to satisfy the security features of RFID communication. A serverless RFID system is a new promising solution to alternate the central database for mobile RFID models. In this model, the reader and the tag perform the mutual authentication without the support of the central database server. However, many security challenges arise from implementing the lightweight RFID authentication protocols in the serverless RFID network. We propose a new robust serverless RFID authentication protocol based on the Elliptic Curve Cryptography (ECC) to prevent the security attacks on the network and maintain the confidentiality and the privacy of the authentication messages and tag information and location. While most of the current protocols assume a secure channel in the setup phase to transmit the communication data, we consider in our protocol an insecure setup phase between the server, reader, and tag to ensure that the data can be renewed from any checkpoint server along with the route of the mobile RFID network. Thus, we implemented the elliptic curve cryptography in the setup phase (renewal phase) to transmit and store the data and the public key of the server to any reader or tag so that the latter can perform the mutual authentication successfully. The proposed model is compared under the classification of the serverless model in term of computation cost and security resistance

Session-based security enhancement of RFID systems for emerging open-loop applications

Radio frequency identification (RFID) is an important technique used for automatic identification and data capture. In recent years, low-cost RFID tags have been used in many open-loop applications beyond supply chain management, such as the tagging of the medicine, clothes, and belongings after the point of sales. At the same time, with the development of semiconductor industry, handheld terminals and mobile phones are becoming RFID-enabled. Unauthorized mobile RFID readers could be abused by the malicious hackers or curious common people. Even for authorized RFID readers, the ownership of the reader can be transferred and the owners of the authorized mobile reader may not be always reliable. The authorization and authentication of the mobile RFID readers need to take stronger security measures to address the privacy or security issues that may arise in the emerging open-loop applications. In this paper, the security demands of RFID tags in emerging open-loop applications are summarized, and two example protocols for authorization, authentication and key establishment based on symmetric cryptography are presented. The proposed protocols adopt a timed-session-based authorization scheme, and all reader-to-tag operations are authorized by a trusted third party using a newly defined class of timed sessions. The output of the tags is randomized to prevent unauthorized tracking of the RFID tags. An instance of the protocol A is implemented in 0.13-μm CMOS technology, and the functions are verified by field programmable gate array. The baseband consumes 44.0 μW under 1.08 V voltage and 1.92 MHz frequency, and it has 25,067 gate equivalents. The proposed protocols can successfully resist most security threats toward open-loop RFID systems except physical attacks. The timing and scalability of the two protocols are discussed in detail

Optimum parameter machine learning classification and prediction of Internet of Things (IoT) malwares using static malware analysis techniques

Application of machine learning in the field of malware analysis is not a new concept, there have been lots of researches done on the classification of malware in android and windows environments. However, when it comes to malware analysis in the internet of things (IoT), it still requires work to be done. IoT was not designed to keeping security/privacy under consideration. Therefore, this area is full of research challenges. This study seeks to evaluate important machine learning classifiers like Support Vector Machines, Neural Network, Random Forest, Decision Trees, Naive Bayes, Bayesian Network, etc. and proposes a framework to utilize static feature extraction and selection processes highlight issues like over-fitting and generalization of classifiers to get an optimized algorithm with better performance. For background study, we used systematic literature review to find out research gaps in IoT, presented malware as a big challenge for IoT and the reasons for applying malware analysis targeting IoT devices and finally perform classification on malware dataset. The classification process used was applied on three different datasets containing file header, program header and section headers as features. Preliminary results show the accuracy of over 90% on file header, program header, and section headers. The scope of this document just discusses these results as initial results and still require some issues to be addressed which may effect on the performance measures