| 1. |
- Afzal, Zeeshan, 1991-, et al.
(författare)
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Using Features of Encrypted Network Traffic to Detect Malware
- 2021
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Ingår i: 25th Nordic Conference on Secure IT Systems, NordSec 2020. - Cham : Springer Science and Business Media Deutschland GmbH. ; , s. 37-53
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Konferensbidrag (refereegranskat)abstract
- Encryption on the Internet is as pervasive as ever. This has protected communications and enhanced the privacy of users. Unfortunately, at the same time malware is also increasingly using encryption to hide its operation. The detection of such encrypted malware is crucial, but the traditional detection solutions assume access to payload data. To overcome this limitation, such solutions employ traffic decryption strategies that have severe drawbacks. This paper studies the usage of encryption for malicious and benign purposes using large datasets and proposes a machine learning based solution to detect malware using connection and TLS metadata without any decryption. The classification is shown to be highly accurate with high precision and recall rates by using a small number of features. Furthermore, we consider the deployment aspects of the solution and discuss different strategies to reduce the false positive rate.
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| 2. |
- Wairimu, Samuel, 1988-, et al.
(författare)
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Privacy Analysis of COVID-19 Contact Tracing Apps in the EU
- 2021
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Ingår i: Secure IT Systems. - Cham : Springer. - 9783030708528 ; , s. 213-228
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Konferensbidrag (refereegranskat)abstract
- This paper presents results from a privacy analysis of COVID-19 contact tracing apps developed within the EU. Though these apps have been termed advantageous, concerns regarding privacy have become an issue that has led to their slow adoption. In this empirical study, we perform both static and dynamic analysis to judge apps’ privacy-preserving behavior together with the analysis of the privacy and data protection goals to deduce their transparency and intervenability. From the results, we discover that while the apps aim to be privacy-preserving, not all adhere to this as we observe one tracks users’ location, while the other violates the principle of least privilege, data minimisation and transparency, which puts the users’ at risk by invading their privacy.
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| 3. |
- Wairimu, Samuel, et al.
(författare)
-
Privacy Analysis of COVID-19 Contact Tracing Apps in the EU
- 2021
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Ingår i: Lecture Notes in Computer Science LNCS 12556. - Cham : Springer. - 9783030708511 ; , s. 213-228
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Konferensbidrag (refereegranskat)abstract
- This paper presents results from a privacy analysis of COVID-19 contact tracing apps developed within the EU. Though these apps have been termed advantageous, concerns regarding privacy have become an issue that has led to their slow adoption. In this empirical study, we perform both static and dynamic analysis to judge apps’ privacy-preserving behavior together with the analysis of the privacy and data protection goals to deduce their transparency and intervenability. From the results, we discover that while the apps aim to be privacy-preserving, not all adhere to this as we observe one tracks users’ location, while the other violates the principle of least privilege, data minimisation and transparency, which puts the users’ at risk by invading their privacy.© 2021, Springer Nature Switzerland AG.
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