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Register a new userData Exfiltration via Multipurpose RFID Cards and Countermeasures
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Added by
Zheng Zhou
Publication date
2019
fields
Informatics Engineering
and research's language is
English
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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.
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Attacks based on power analysis have been long existing and studied, with some recent works focused on data exfiltration from victim systems without using conventional communications (e.g., WiFi). Nonetheless, prior works typically rely on intrusive direct power measurement, either by implanting meters in the power outlet or tapping into the power cable, thus jeopardizing the stealthiness of attacks. In this paper, we propose NoDE (Noise for Data Exfiltration), a new system for stealthy data exfiltration from enterprise desktop computers. Specifically, NoDE achieves data exfiltration over a buildings power network by exploiting high-frequency voltage ripples (i.e., switching noises) generated by power factor correction circuits built into todays computers. Located at a distance and even from a different room, the receiver can non-intrusively measure the voltage of a power outlet to capture the high-frequency switching noises for online information decoding without supervised training/learning. To evaluate NoDE, we run experiments on seven different computers from top-vendors and using top brand power supply units. Our results show that for a single transmitter, NoDE achieves a rate of up to 28.48 bits/second with a distance of 90 feet (27.4 meters) without the line of sight, demonstrating a practically stealthy threat. Based on the orthogonality of switching noise frequencies of different computers, we also demonstrate simultaneous data exfiltration from four computers using only one receiver. Finally, we present a few possible defenses, such as installing noise filters, and discuss their limitations.
Increasing numbers of mobile computing devices, user-portable, or embedded in vehicles, cargo containers, or the physical space, need to be aware of their location in order to provide a wide range of commercial services. Most often, mobile devices obtain their own location with the help of Global Navigation Satellite Systems (GNSS), integrating, for example, a Global Positioning System (GPS) receiver. Nonetheless, an adversary can compromise location-aware applications by attacking the GNSS-based positioning: It can forge navigation messages and mislead the receiver into calculating a fake location. In this paper, we analyze this vulnerability and propose and evaluate the effectiveness of countermeasures. First, we consider replay attacks, which can be effective even in the presence of future cryptographic GNSS protection mechanisms. Then, we propose and analyze methods that allow GNSS receivers to detect the reception of signals generated by an adversary, and then reject fake locations calculated because of the attack. We consider three diverse defense mechanisms, all based on knowledge, in particular, own location, time, and Doppler shift, receivers can obtain prior to the onset of an attack. We find that inertial mechanisms that estimate location can be defeated relatively easy. This is equally true for the mechanism that relies on clock readings from off-the-shelf devices; as a result, highly stable clocks could be needed. On the other hand, our Doppler Shift Test can be effective without any specialized hardware, and it can be applied to existing devices.
The perpetual opposition between antiviruses and malware leads both parties to evolve continuously. On the one hand, antiviruses put in place solutions that are more and more sophisticated and propose more complex detection techniques in addition to the classic signature analysis. This sophistication leads antiviruses to leave more traces of their presence on the machine they protect. To remain undetected as long as possible, malware can avoid executing within such environments by hunting down the modifications left by the antiviruses. This paper aims at determining the possibilities for malware to detect the antiviruses and then evaluating the efficiency of these techniques on a panel of antiviruses that are the most used nowadays. We then collect samples showing this kind of behavior and propose to evaluate a countermeasure that creates false artifacts, thus forcing malware to evade.
We consider a problem, which we call secure grouping, of dividing a number of parties into some subsets (groups) in the following manner: Each party has to know the other members of his/her group, while he/she may not know anything about how the remaining parties are divided (except for certain public predetermined constraints, such as the number of parties in each group). In this paper, we construct an information-theoretically secure protocol using a deck of physical cards to solve the problem, which is jointly executable by the parties themselves without a trusted third party. Despite the non-triviality and the potential usefulness of the secure grouping, our proposed protocol is fairly simple to describe and execute. Our protocol is based on algebraic properties of conjugate permutations. A key ingredient of our protocol is our new techniques to apply multiplication and inverse operations to hidden permutations (i.e., those encoded by using face-down cards), which would be of independent interest and would have various potential applications.
This work provides the community with a timely comprehensive review of backdoor attacks and countermeasures on deep learning. According to the attackers capability and affected stage of the machine learning pipeline, the attack surfaces are recognized to be wide and then formalized into six categorizations: code poisoning, outsourcing, pretrained, data collection, collaborative learning and post-deployment. Accordingly, attacks under each categorization are combed. The countermeasures are categorized into four general classes: blind backdoor removal, offline backdoor inspection, online backdoor inspection, and post backdoor removal. Accordingly, we review countermeasures, and compare and analyze their advantages and disadvantages. We have also reviewed the flip side of backdoor attacks, which are explored for i) protecting intellectual property of deep learning models, ii) acting as a honeypot to catch adversarial example attacks, and iii) verifying data deletion requested by the data contributor.Overall, the research on defense is far behind the attack, and there is no single defense that can prevent all types of backdoor attacks. In some cases, an attacker can intelligently bypass existing defenses with an adaptive attack. Drawing the insights from the systematic review, we also present key areas for future research on the backdoor, such as empirical security evaluations from physical trigger attacks, and in particular, more efficient and practical countermeasures are solicited.
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