Forskningsoutput per år
Forskningsoutput per år
Sammanfattning
New software vulnerabilities are published daily.
Prioritizing vulnerabilities according to their relevance to the collection of software an organization uses is a costly and slow process.
While recommender systems were earlier proposed to address this issue, they ignore the security of the vulnerability prioritization data.
As a result, a malicious operator or a third party adversary can collect vulnerability prioritization data to identify the security assets in the enterprise deployments of client organizations.
To address this, we propose a solution that leverages isolated execution to protect the privacy of vulnerability profiles without compromising data integrity.
To validate an implementation of the proposed solution we integrated it with an existing recommender system for software vulnerabilities.
The evaluation of our implementation shows that the proposed solution can effectively complement existing recommender systems for software vulnerabilities.
Prioritizing vulnerabilities according to their relevance to the collection of software an organization uses is a costly and slow process.
While recommender systems were earlier proposed to address this issue, they ignore the security of the vulnerability prioritization data.
As a result, a malicious operator or a third party adversary can collect vulnerability prioritization data to identify the security assets in the enterprise deployments of client organizations.
To address this, we propose a solution that leverages isolated execution to protect the privacy of vulnerability profiles without compromising data integrity.
To validate an implementation of the proposed solution we integrated it with an existing recommender system for software vulnerabilities.
The evaluation of our implementation shows that the proposed solution can effectively complement existing recommender systems for software vulnerabilities.
| Originalspråk | engelska |
|---|---|
| Titel på värdpublikation | The 17th IEEE International Conference on Dependable, Autonomic and Secure Computing (DASC 2019) |
| Förlag | IEEE - Institute of Electrical and Electronics Engineers Inc. |
| ISBN (elektroniskt) | 978-1-7281-3024-8 |
| DOI | |
| Status | Published - 2019 |
| Evenemang | 2019 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress - Fukuoka, Japan Varaktighet: 2019 aug. 5 → 2019 aug. 8 |
Konferens
| Konferens | 2019 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress |
|---|---|
| Land/Territorium | Japan |
| Ort | Fukuoka |
| Period | 2019/08/05 → 2019/08/08 |
Ämnesklassifikation (UKÄ)
- Programvaruteknik
Fingeravtryck
Utforska forskningsämnen för ”Privacy-enabled Recommendations for Software Vulnerabilities”. Tillsammans bildar de ett unikt fingeravtryck.Forskningsoutput
- 1 Doktorsavhandling (sammanläggning)
-
Contributions to Preventive Measures in Cyber Security
Karlsson, L., 2019 sep. 30, Department of Electrical and Information Technology, Lund University. 205 s.Forskningsoutput: Avhandling › Doktorsavhandling (sammanläggning)
Öppen tillgångFil
Projekt
- 1 Avslutade
-
SMARTY: Säkra mjukvaruuppdateringar för den smarta staden
Hell, M., Magnusson, B., Gehrmann, C., Paladi, N., Karlsson, L., Sönnerup, J., Johnsson, B. A., Hedin, G., Nordahl, M., Pagnin, E., Kundu, R. & Åkesson, A.
Stiftelsen för Strategisk Forskning, SSF
2018/03/01 → 2023/02/28
Projekt: Forskning