Radio-frequency identification(RFID) technology is widely applied in daily human life. The RFID cards are seen everywhere, from entrance guard to consumption. The information security of RFID cards, such as data confidentiality, tag anonymity, mutual authentication etc, has been fully studied. In the paper, using the RFID cards in MIFARE Classic and DESFire families, a bidirectional covert channel via multipurpose RFID cards between service providers is built to leak sensitive data between two simulation systems. Furthermore, by calculations and experiments, the daily channel capacity to leak data of the channel is obtained. Although the storage capacity of a single RFID card is very small, a large user base can still bring about a considerable amount to leak data. Then, the reasons for the existence of such channels are discussed. To eliminate this type of covert channels, a new authentication protocol between RFID cards and card readers are proposed. Our experimental results show a significant security improvement in prevention of such covert communications while keeping user convenience.
In 2007, Heydt- @cite_0 found the vulnerabilities in the first-generation RFID-enabled credit cards. Garcia @cite_40 showed that MIFARE Classic, a widely used smart card, was vulnerable to reverse engineering and man-in-the-middle attack in 2009. D " u zenli @cite_5 proposed a novel approach that applies neural network forecasting to security for closed-loop prepaid cards based on low-cost technologies such as RFID and 1-Wire in 2015. In 2017, @cite_24 proposed a security solution of an RFID card through cryptography. @cite_19 proposed a side-channel attack on a protected RFID card in 2018.
@cite_0 @cite_19 @cite_40 @cite_24 @cite_5
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This paper proposes a novel approach that applies neural network forecasting to security for closed-loop prepaid cards based on low-cost technologies such as RFID and 1-Wire. The security vulnerability of low-cost RFID closed-loop prepaid card systems originates mostly from the card itself. Criminal organizations counterfeit or clone card data. Although high-security prepaid cards exist, they are often too expensive for transport ticketing, and even their security is not guaranteed for a well-defined period of time. Therefore, data encryption systems are used widely against counterfeiting with success. However, it has not been possible to develop countermeasures with comparable success against cloning. Our proposed security application uses neural network forecasting to determine the recharge day behavioral characteristics of the cardholder and predict the next time the cardholder will recharge their card. Based on the prediction for the recharge time, the expiration date of the low-cost RFID prepaid card is defined, which is a good countermeasure against cloning. FTDNN, LRNN, and NARX network architectures with one hidden layer are considered in this research. The effects of the network architecture, the number of neurons, the training algorithm, and the prediction performance function on the recharge day forecast are investigated. Experimental results confirm the accuracy of the recharge time forecast and confirm countermeasures against cloning. Our proposed security approach with neural network forecasting is applied with success to the Turkish public transport without an online backend system. The Mifare Classic is the most widely used contactless smartcard on the market.The stream cipher CRYPTO1 used by the Classic has recently been reverse engineered and serious attacks have been proposed. The most serious of them retrieves a secret key in under a second. In order to clone a card, previously proposed attacks require that the adversary either has access to an eavesdropped communication session or executes a message-by-message man-in-the-middle attack between the victim and a legitimate reader. Although this is already disastrous from a cryptographic point of view, system integrators maintain that these attacks cannot be performed undetected.This paper proposes four attacks that can be executed by an adversary having only wireless access to just a card (and not to a legitimate reader). The most serious of them recovers a secret key in less than a second on ordinary hardware. Besides the cryptographic weaknesses, we exploit other weaknesses in the protocol stack. A vulnerability in the computation of parity bits allows an adversary to establish a side channel. Another vulnerability regarding nested authentications provides enough plaintext for a speedy known-plaintext attack. Modern smartcards, capable of sophisticated cryptography, provide a high assurance of tamper resistance and are thus commonly used in payment applications. Although extracting secrets out of smartcards requires resources beyond the means of many would-be thieves, the manner in which they are used can be exploited for fraud. Cardholders authorize financial transactions by presenting the card and disclosing a PIN to a terminal without any assurance as to the amount being charged or who is to be paid, and have no means of discerning whether the terminal is authentic or not. Even the most advanced smartcards cannot protect customers from being defrauded by the simple relaying of data from one location to another. We describe the development of such an attack, and show results from live experiments on the UK's EMV implementation, Chip & PIN. We discuss previously proposed defences, and show that these cannot provide the required security assurances. A new defence based on a distance bounding protocol is described and implemented, which requires only modest alterations to current hardware and software. As far as we are aware, this is the first complete design and implementation of a secure distance bounding protocol. Future smartcard generations could use this design to provide cost-effective resistance to relay attacks, which are a genuine threat to deployed applications. We also discuss the security-economics impact to customers of enhanced authentication mechanisms. In recent years, defending adversarial perturbations to natural examples in order to build robust machine learning models trained by deep neural networks (DNNs) has become an emerging research field in the conjunction of deep learning and security. In particular, MagNet consisting of an adversary detector and a data reformer is by far one of the strongest defenses in the black-box oblivious attack setting, where the attacker aims to craft transferable adversarial examples from an undefended DNN model to bypass an unknown defense module deployed on the same DNN model. Under this setting, MagNet can successfully defend a variety of attacks in DNNs, including the high-confidence adversarial examples generated by the Carlini and Wagner's attack based on the @math distortion metric. However, in this paper, under the same attack setting we show that adversarial examples crafted based on the @math distortion metric can easily bypass MagNet and mislead the target DNN image classifiers on MNIST and CIFAR-10. We also provide explanations on why the considered approach can yield adversarial examples with superior attack performance and conduct extensive experiments on variants of MagNet to verify its lack of robustness to @math distortion based attacks. Notably, our results substantially weaken the assumption of effective threat models on MagNet that require knowing the deployed defense technique when attacking DNNs (i.e., the gray-box attack setting).