Re: Data Cash Sound Radix Auto Align V1.5.0 Incl Keygen R2r 18 10
Clotilde Wilks
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The full Proceedings published by USENIX for the symposium are available for download below. Individual papers can also be downloaded from their respective presentation pages. Copyright to the individual works is retained by the author[s].
ZigBee is a popular wireless communication standard for Internet of Things (IoT) networks. Since each ZigBee network uses hop-by-hop network-layer message authentication based on a common network key, it is highly vulnerable to packet-injection attacks, in which the adversary exploits the compromised network key to inject arbitrary fake packets from any spoofed address to disrupt network operations and consume the network/device resources. In this paper, we present PhyAuth, a PHY hop-by-hop message authentication framework to defend against packet-injection attacks in ZigBee networks. The key idea of PhyAuth is to let each ZigBee transmitter embed into its PHY signals a PHY one-time password (called POTP) derived from a device-specific secret key and an efficient cryptographic hash function. An authentic POTP serves as the transmitter's PHY transmission permission for the corresponding packet. PhyAuth provides three schemes to embed, detect, and verify POTPs based on different features of ZigBee PHY signals. In addition, PhyAuth involves lightweight PHY signal processing and no change to the ZigBee protocol stack. Comprehensive USRP experiments confirm that PhyAuth can efficiently detect fake packets with very low false-positive and false-negative rates while having a negligible negative impact on normal data transmissions.
Due to its suitability for wireless ranging, Ultra-Wide Band (UWB) has gained traction over the past years. UWB chips have been integrated into consumer electronics and considered for security-relevant use cases, such as access control or contactless payments. However, several publications in the recent past have shown that it is difficult to protect the integrity of distance measurements on the physical layer. In this paper, we identify transceiver clock imperfections as a new, important parameter that has been widely ignored so far. We present Mix-Down and Stretch-and-Advance, two novel attacks against the current (IEEE 802.15.4z) and the upcoming (IEEE 802.15.4ab) UWB standard, respectively. We demonstrate Mix-Down on commercial chips and achieve distance reductions from 10 m to 0 m. For the Stretch-and-Advance attack, we show analytically that the current proposal of IEEE 802.15.4ab allows reductions of over 90 m. To prevent the attack, we propose and analyze an effective countermeasure.
Bluetooth Low Energy (BLE) is the mainstream Bluetooth standard and BLE Secure Connections (BLC-SC) pairing is a protocol that authenticates two Bluetooth devices and derives a shared secret key between them. Although BLE-SC pairing employs well-studied cryptographic primitives to guarantee its security, a recent study revealed a logic flaw in the protocol.
In this paper, we develop the first comprehensive formal model of the BLE-SC pairing protocol. Our model is compliant with the latest Bluetooth specification version 5.3 and covers all association models in the specification to discover attacks caused by the interplay between different association models. We also partly loosen the perfect cryptography assumption in traditional symbolic analysis approaches by designing a low-entropy key oracle to detect attacks caused by the poorly derived keys. Our analysis confirms two existing attacks and discloses a new attack. We propose a countermeasure to fix the flaws found in the BLE-SC pairing protocol and discuss the backward compatibility. Moreover, we extend our model to verify the countermeasure, and the results demonstrate its effectiveness in our extended model.
Sophie Stephenson and Majed Almansoori, University of Wisconsin--Madison; Pardis Emami-Naeini, Duke University; Danny Yuxing Huang, New York University; Rahul Chatterjee, University of Wisconsin--Madison
Rosanna Bellini, Cornell University; Kevin Lee, Princeton University; Megan A. Brown, Center for Social Media and Politics, New York University; Jeremy Shaffer, Cornell University; Rasika Bhalerao, Northeastern University; Thomas Ristenpart, Cornell Tech
Digital technologies play a growing role in exacerbating financial abuse for survivors of intimate partner violence (IPV). While abusers of IPV rarely employ advanced technological attacks that go beyond interacting via standard user interfaces, scant research has examined how consumer-facing financial technologies can facilitate or obstruct IPV-related attacks on a survivor's financial well-being. Through an audit of 13 mobile banking and 17 peer-to-peer payment smartphone applications and their associated usage policies, we simulated both close-range and remote attacks commonly used by IPV adversaries. We discover that mobile banking and peer-to-peer payment applications are generally ill-equipped to deal with user-interface bound (UI-bound) adversaries, permitting unauthorized access to logins, surreptitious surveillance, and, harassing messages and system prompts.
To assess our discoveries, we interviewed 12 financial professionals who offer or oversee frontline services for vulnerable customers. While professionals expressed an interest in implementing mitigation strategies, they also highlight barriers to institutional approaches to intimate threats, and question professional responsibilities for digital safety. We conclude by providing recommendations for how digital financial service providers may better address UI-bound threats, and offer broader considerations for professional auditing and evaluation approaches to technology-facilitated abuse.
Victim-survivors of intimate partner violence (IPV) are facing a new technological threat: Abusers are leveraging IoT devices such as smart thermostats, hidden cameras, and GPS trackers to spy on and harass victim-survivors. Though prior work provides a foundation of what IoT devices can be involved in intimate partner violence, we lack a detailed understanding of the factors which contribute to this IoT abuse, the strategies victim-survivors use to mitigate IoT abuse, and the barriers they face along the way. Without this information, it is challenging to design effective solutions to stop IoT abuse.
To fill this gap, we interviewed 20 participants with firsthand or secondhand experience with IoT abuse. Our interviews captured 39 varied instances of IoT abuse, from surveillance with hidden GPS trackers to harassment with smart thermostats and light bulbs. They also surfaced 21 key barriers victim-survivors face while coping with IoT abuse. For instance, victim-survivors struggle to find proof of the IoT abuse they experience, which makes mitigations challenging. Even with proof, victim-survivors face barriers mitigating the abuse; for example, mitigation is all but impossible for victim-survivors living with an abusive partner. Our findings pinpoint several solutions to combat IoT abuse, including increased transparency of IoT devices, updated IoT access control protocols, and raising awareness of IoT abuse.
Recent anecdotal evidence suggests that abusers have begun to use covert spy devices such as nanny cameras, item trackers, and audio recorders to spy on and stalk their partners. Currently, it is difficult to combat this type of intimate partner surveillance (IPS) because we lack an understanding of the prevalence and characteristics of commercial spy devices. Additionally, it is unclear whether existing devices, apps, and tools designed to detect covert devices are effective. We observe that many spy devices and detectors can be found on mainstream retailers. Thus, in this work, we perform a systematic survey of spy devices and detection tools sold through popular US retailers. We gather 2,228 spy devices, 1,313 detection devices, and 51 detection apps, then study a representative sample through qualitative analysis as well as in-lab evaluations.
Our results show a bleak picture of the IPS ecosystem. Not only can commercial spy devices easily be used for IPS, but many of them are advertised for use in IPS and other covert surveillance. On the other hand, commercial detection devices and apps are all but defective, and while recent academic detection systems show promise, they require much refinement before they can be useful to survivors. We urge the security community to take action by designing practical, usable detection tools to detect hidden spy devices.
A large collection of research literature has identified the privacy risks of keystroke inference attacks that use statistical models to extract content typed onto a keyboard. Yet existing attacks cannot operate in realistic settings, and rely on strong assumptions of labeled training data, knowledge of keyboard layout, carefully placed sensors or data from other side-channels. This paper describes experiences developing and evaluating a general, video-based keystroke inference attack that operates in common public settings using a single commodity camera phone, with no pretraining, no keyboard knowledge, no local sensors, and no side-channels. We show that using a self-supervised approach, noisy finger tracking data from a video can be processed, labeled and filtered to train DNN keystroke inference models that operate accurately on the same video. Using IRB approved user studies, we validate attack efficacy across a variety of environments, keyboards, and content, and users with different typing behaviors and abilities. Our project website is located at:
In various scenarios from system login to writing emails, documents, and forms, keyboard inputs carry alluring data such as passwords, addresses, and IDs. Due to commonly existing non-alphabetic inputs, punctuation, and typos, users' natural inputs rarely contain only constrained, purely alphabetic keys/words. This work studies how to reveal unconstrained keyboard inputs using auditory interfaces.
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