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© Tobias Ebelshäuser
Forschungsgruppe

Stock

Secure Web Applications Group

Die Secure Web Applications Group (kurz: SWAG) forscht in allen Bereichen, die mit der Web-Sicherheit zusammenhängen. Ein besonderer Schwerpunkt ist die Forschung im Bereich der clientseitigen Sicherheit, bei der Erkennung, Analyse und Entschärfung von Angriffen im Zusammenhang mit JavaScript. Darüber hinaus erforschen wir, wie entdeckte Schwachstellen am besten an die betroffenen Betreiber:innen kommuniziert werden können. Darüber hinaus untersuchen wir, wie bösartiges JavaScript die Nutzer:innen im Web beeinträchtigen kann, und erforschen sowohl neue Wege zur Erkennung solcher Skripte als auch Angriffe auf bestehende Abwehrlösungen. Möchten Sie bei uns arbeiten? Die Details für Doktoranden, Diplomanden und studentische Hilfskräfte finden Sie auf unserer Jobseite (our jobs page).

Gruppenleiter:in

Ben Stock

E-Mail

Adresse

Kaiserstraße 21
66386 St. Ingbert (Germany)

Neueste Veröffentlichungen

Jahr 2024

Konferenz / Medium

Usenix Security Symposium (USENIX-Security)

Konferenz / Medium

ACM ASIA Conference on Computer and Communications Security (AsiaCCS)

PROJECTS

CURRENT PROJECTS

The Web is arguably the most popular platform for information exchange today. To allow for a better user experience, much functionality is shifted towards the client. This shift also increases the complexity of client-side code and hence the attack surface (Stock et al. 2017). This can be exhibited in increased vulnerabilities such as Client-Side Cross-Site Scripting (Lekies, Stock, and Johns 2013). We therefore try to better understand these issues (Stock et al. 2015; Steffens et al. 2019) and develop and evaluate potential solutions (Stock et al. 2014; Musch et al. 2019). In general, our research investigates all types of client-side Web security, including areas such as CSP (Roth et al. 2020; Roth, Backes, and Stock 2020) and framing control (Calzavara et al. 2020).

Although detection of many types of web-based flaws has been in the focus of researchers over the previous years, notifying affected parties barely got any attention. For this project, we try to identify potential channels for notification and evaluate their effectiveness (Stock et al. 2016). Also, we try to improve not only on technical measures like avoiding spam filters, but also try to understand the human aspects of a notification, such as how different wording might influence the success of a notification. (Stock et al. 2018)

With its prevalence in the browser, JavaScript also makes for a prime target for attackers. Therefore, our group researches new ways of detecting malicious JavaScript in the wild. Specifically, this subsumes work in which we automatically generate signatures for exploit kits, alleviating the burden of malware analysists (Stock, Livshits, and Zorn 2016). In addition, our work focusses on detection of malicious JavaScript in general through methods of machine learning (Fass et al. 2018; Fass, Backes, and Stock 2019) and novel ways of bypassing existing static analysis tools (Fass, Backes, and Stock 2019).