Sökning: (WFRF:(Brunström Anna 1967 )) >
Using Features of E...
Using Features of Encrypted Network Traffic to Detect Malware
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- Afzal, Zeeshan, 1991- (författare)
- Karlstads universitet,KTH,Nätverk och systemteknik,Karlstad University Karlstad Sweden,Institutionen för matematik och datavetenskap (from 2013),KTH Royal Institute of Technology, Sweden,Datavetenskap, Computer Science
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- Brunström, Anna, 1967- (författare)
- Karlstads universitet,Institutionen för matematik och datavetenskap (from 2013),Datavetenskap, Computer Science
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- Lindskog, Stefan, 1967- (författare)
- Karlstads universitet,Institutionen för matematik och datavetenskap (from 2013),SINTEF Digital, Trondheim, NOR,Datavetenskap, Computer Science
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(creator_code:org_t)
- 2021-03-03
- 2021
- Engelska.
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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
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences (hsv//eng)
Nyckelord
- Large dataset
- Malware
- Turing machines
- False positive rates
- High-precision
- Highly accurate
- Large datasets
- Network traffic
- Payload data
- Protected communications
- Cryptography
- Computer Science
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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