White Rabbit in Mobile

Park, Shinjo; Shaik, Altaf; Borgaonkar, Ravishankar; Seifert, Jean-Pierre

doi:10.1145/2994459.2994465

Cited by 13 publications

(7 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is possible to detect the presence of these rogue base stations, although some may consider this technology still in developmental stages. Mobile applications can be used to detect the presence of rogue base stations, although using specific chipsets on older device models are often required [35]. Different approaches can be used for detection purposes.…”

Section: Rogue Base Station Detectionmentioning

confidence: 99%

Guide to LTE security

Cichonski¹,

Franklin²,

Bartock³

2017

View full text Add to dashboard Cite

AuthorityThis publication has been developed by NIST in accordance with its statutory responsibilities under the Federal Information Security Modernization Act (FISMA) of 2014, 44 U.S.C. § 3551 et seq., Public Law (P.L.) 113-283. NIST is responsible for developing information security standards and guidelines, including minimum requirements for federal information systems, but such standards and guidelines shall not apply to national security systems without the express approval of appropriate federal officials exercising policy authority over such systems. This guideline is consistent with the requirements of the Office of Management and Budget (OMB) Circular A-130.Nothing in this publication should be taken to contradict the standards and guidelines made mandatory and binding on federal agencies by the Secretary of Commerce under statutory authority. Nor should these guidelines be interpreted as altering or superseding the existing authorities of the Secretary of Commerce, Director of the OMB, or any other federal official. This publication may be used by nongovernmental organizations on a voluntary basis and is not subject to copyright in the United States. Attribution would, however, be appreciated by NIST. Certain commercial entities, equipment, or materials may be identified in this document in order to describe an experimental procedure or concept adequately. Such identification is not intended to imply recommendation or endorsement by NIST, nor is it intended to imply that the entities, materials, or equipment are necessarily the best available for the purpose.There may be references in this publication to other publications currently under development by NIST in accordance with its assigned statutory responsibilities. The information in this publication, including concepts and methodologies, may be used by federal agencies even before the completion of such companion publications. Thus, until each publication is completed, current requirements, guidelines, and procedures, where they exist, remain operative. For planning and transition purposes, federal agencies may wish to closely follow the development of these new publications by NIST.Organizations are encouraged to review all draft publications during public comment periods and provide feedback to NIST. Many NIST cybersecurity publications, other than the ones noted above, are available at https://csrc.nist.gov/publications. Comments on this publication may be submitted to:National NIST SP 800-187This publication is available free of charge from: https://doi.org/10.6028/NIST.SP.800-187 Reports on Computer Systems TechnologyThe Information Technology Laboratory (ITL) at the National Institute of Standards and Technology (NIST) promotes the U.S. economy and public welfare by providing technical leadership for the Nation's measurement and standards infrastructure. ITL develops tests, test methods, reference data, proof of concept implementations, and technical analyses to advance the development and productive use of information technology. ITL's respon...

show abstract

Section: Rogue Base Station Detectionmentioning

confidence: 99%

Guide to LTE security

Cichonski¹,

Franklin²,

Bartock³

2017

View full text Add to dashboard Cite

show abstract

“…Other activities potentially leading to mobile phone exploitation include the use of smartwatches and wearable accessories, which expose mobile devices to vulnerabilities [101,112]. Furthermore, people frequently hold mobile phone devices with essential data assets [107].…”

Section: Smartphone User Activities Based On Physical Modalitymentioning

confidence: 99%

A Systematic Literature Review and a Conceptual Framework Proposition for Advanced Persistent Threats (APT) Detection for Mobile Devices Using Artificial Intelligence Techniques

2023

View full text Add to dashboard Cite

Advanced persistent threat (APT) refers to a specific form of targeted attack used by a well-organized and skilled adversary to remain undetected while systematically and continuously exfiltrating sensitive data. Various APT attack vectors exist, including social engineering techniques such as spear phishing, watering holes, SQL injection, and application repackaging. Various sensors and services are essential for a smartphone to assist in user behavior that involves sensitive information. Resultantly, smartphones have become the main target of APT attacks. Due to the vulnerability of smartphone sensors, several challenges have emerged, including the inadequacy of current methods for detecting APTs. Nevertheless, several existing APT solutions, strategies, and implementations have failed to provide comprehensive solutions. Detecting APT attacks remains challenging due to the lack of attention given to human behavioral factors contributing to APTs, the ambiguity of APT attack trails, and the absence of a clear attack fingerprint. In addition, there is a lack of studies using game theory or fuzzy logic as an artificial intelligence (AI) strategy for detecting APT attacks on smartphone sensors, besides the limited understanding of the attack that may be employed due to the complex nature of APT attacks. Accordingly, this study aimed to deliver a systematic review to report on the extant research concerning APT detection for mobile sensors, applications, and user behavior. The study presents an overview of works performed between 2012 and 2023. In total, 1351 papers were reviewed during the primary search. Subsequently, these papers were processed according to their titles, abstracts, and contents. The resulting papers were selected to address the research questions. A conceptual framework is proposed to incorporate the situational awareness model in line with adopting game theory as an AI technique used to generate APT-based tactics, techniques, and procedures (TTPs) and normal TTPs and cognitive decision making. This framework enhances security awareness and facilitates the detection of APT attacks on smartphone sensors, applications, and user behavior. It supports researchers in exploring the most significant papers on APTs related to mobile sensors, services, applications, and detection techniques using AI.

show abstract

“…Several techniques to corrupt the date and time of a smartphone have been identified (see Park et al [23] for detailed information). In this section, we describe four of them.…”

Section: Time-traveling Phonesmentioning

confidence: 99%

“…Most likely, 2G will phase out from smartphones way later than the virus. If not, rogue 3G/4G base stations can be made too, but it would require to bypass authentication (at least, on phones which use it [23]).…”

Section: Fig 6 Pictures Of Immuni Raising An Alertmentioning

confidence: 99%

On the Effectiveness of Time Travel to Inject COVID-19 Alerts

Iovino

Vaudenay

Vuagnoux³

2021

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Digital contact tracing apps allow to alert people who have been in contact with people who may be contagious. The Google/Apple Exposure Notification (GAEN) system is based on Bluetooth proximity estimation. It has been adopted by many countries around the world. However, many possible attacks are known. The goal of some of them is to inject a false alert on someone else's phone. This way, an adversary can eliminate a competitor in a sport event or a business in general. Political parties can also prevent people from voting. In this report, we review several methods to inject false alerts. One of them requires to corrupt the clock of the smartphone of the victim. For that, we build a time-traveling machine to be able to remotely set up the clock on a smartphone and experiment our attack. We show how easy this can be done. We successfully tested several smartphones with either the Swiss or the Italian app (SwissCovid or Immuni). We confirm it also works on other GAEN-based apps: NHS COVID-19 (in England and Wales), Corona-Warn-App (in Germany), and Coronalert (Belgium). The time-machine can also be used in active attack to identify smartphones. We can recognize smartphones that we have passively seen in the past. We can passively recognize in the future smartphones that we can see in present. We can also make smartphones identify themselves with a unique number. Finally, we report a simpler attack which needs no time machine but relies on the existence of still-valid keys reported on the server. We observed the case in several countries. The attack is made trivial in Austria,

show abstract

White Rabbit in Mobile

Cited by 13 publications

References 6 publications

Guide to LTE security

Guide to LTE security

A Systematic Literature Review and a Conceptual Framework Proposition for Advanced Persistent Threats (APT) Detection for Mobile Devices Using Artificial Intelligence Techniques

On the Effectiveness of Time Travel to Inject COVID-19 Alerts

Contact Info

Product

Resources

About