Usability of Risk-based Implicit Authentication


Projektbeschreibung:

Das Forschungsprojekt URIA beschäftigt sich mit der weit verbreiteten passwortbasierten Authentifizierung – sei es bei E-Mail-Diensten, Online-Shops oder Online-Banking. Wohl jeder kennt die Qual gute Passwörter zu wählen und vor allem zu behalten. Darüber hinaus bergen passwortgesicherte Systeme hohe Sicherheitsrisiken, da sie schnell zu „knacken“ sind. Passwortbasierte Authentifizierung hat daher nicht nur Schwächen in der Usability sondern auch in der Sicherheit. Risikobasierte Authentifizierung hat hingegen das Potential die Sicherheit zu erhöhen ohne die Usability zu beeinträchtigen.

Projektdauer: April 2018 - April 2021

Projektmitarbeiter:

Luigi Lo Iacono

Luigi Lo Iacono

Professor

Raum:
ZW 10-4
Telefon:
+49 221-8275-2527
luigi.lo_iacono@th-koeln.de
Stephan Wiefling

Stephan Wiefling

Wissenschaftlicher Mitarbeiter

Raum:
ZW 10-23
Telefon:
+49 221-8275-4233
stephan.wiefling@th-koeln.de

Fördermittelgeber:

Das Projekt URIA ist eines der sieben Forschungstandems des landesweiten Graduiertenkollegs "Human Centered Systems Security – North Rhine Westphalian Experts on Research in Digitalization" (NERD NRW) und wird vom Ministerium für Kultur und Wissenschaft des Landes Nordrhein-Westfalen gefördert.

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Publikationen:


@inproceedings{conf/nordsec2019/wiefling,
	title = {Even {Turing} {Should} {Sometimes} {Not} {Be} {Able} {To} {Tell}: {Mimicking} {Humanoid} {Usage} {Behavior} for {Exploratory} {Studies} of {Online} {Services}},
	booktitle = {24th {Nordic} {Conference} on {Secure} {IT} {Systems} ({NordSec} 2019)},
	series = {{Lecture} {Notes} in {Computer} {Science}},
	author = {S. Wiefling and N. Gruschka and L. Lo Iacono},
	volume = {11875},
	isbn = {978-3-030-35055-0},
	doi = {10.1007/978-3-030-35055-0_12},
	publisher = {Springer Nature},
	location = {Aalborg, Denmark},
	month = nov,
	year = {2019},
	url = {https://epb.bibl.th-koeln.de/files/1422/Wiefling_HOSIT_NordSec2019.pdf},
	abstract = {Online services such as social networks, online shops, and search engines deliver different content to users depending on their location, browsing history, or client device. Since these services have a major influence on opinion forming, understanding their behavior from a social science perspective is of greatest importance. In addition, technical aspects of services such as security or privacy are becoming more and more relevant for users, providers, and researchers. Due to the lack of essential data sets, automatic black box testing of online services is currently the only way for researchers to investigate these services in a methodical and reproducible manner. However, automatic black box testing of online services is difficult since many of them try to detect and block automated requests to prevent bots from accessing them.

	In this paper, we introduce a testing tool that allows researchers to create and automatically run experiments for exploratory studies of online services. The testing tool performs programmed user interactions in such a manner that it can hardly be distinguished from a human user. To evaluate our tool, we conducted - among other things - a large-scale research study on Risk-based Authentication (RBA), which required human-like behavior from the client. We were able to circumvent the bot detection of the investigated online services with the experiments. As this demonstrates the potential of the presented testing tool, it remains to the responsibility of its users to balance the conflicting interests between researchers and service providers as well as to check whether their research programs remain undetected.}
}

@inproceedings{conf/ifipsec2019/wiefling,
	title = {Is {This} {Really} {You}? {An} {Empirical} {Study} on {Risk}-{Based} {Authentication} {Applied} in the {Wild}},
	booktitle = {34th {IFIP} {TC}-11 {International} {Conference} on {Information} {Security} and {Privacy} {Protection} ({IFIP} {SEC} 2019)},
	series = {{IFIP} {Advances} in {Information} and {Communication} {Technology}},
	author = {S. Wiefling and L. Lo Iacono and M. Dürmuth},
	volume = {562},
	pages = {134--148},
	isbn = {978-3-030-22311-3},
	doi = {10.1007/978-3-030-22312-0_10},
	publisher = {Springer International Publishing},
	location = {Lisbon, Portugal},
	month = jun,
	year = {2019},
	abstract = {Risk-based authentication (RBA) is an adaptive security measure to strengthen password-based authentication. RBA monitors additional implicit features during password entry such as device or geolocation information, and requests additional authentication factors if a certain risk level is detected. RBA is recommended by the NIST digital identity guidelines, is used by several large online services, and offers protection against security risks such as password database leaks, credential stuffing, insecure passwords and large-scale guessing attacks. Despite its relevance, the procedures used by RBA-instrumented online services are currently not disclosed. Consequently, there is little scientific research about RBA, slowing down progress and deeper understanding, making it harder for end users to understand the security provided by the services they use and trust, and hindering the widespread adoption of RBA.

	In this paper, with a series of studies on eight popular online services, we (i) analyze which features and combinations/classifiers are used and are useful in practical instances, (ii) develop a framework and a methodology to measure RBA in the wild, and (iii) survey and discuss the differences in the user interface for RBA. Following this, our work provides a first deeper understanding of practical RBA deployments and helps fostering further research in this direction.},
	url = {https://epb.bibl.th-koeln.de/files/1369/Risk-based_Authentication_Study_Final.pdf}
}