Publikationer från Malmö universitet
Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Combining Anomaly- and Signaturebased Algorithms for IntrusionDetection in CAN-bus: A suggested approach for building precise and adaptiveintrusion detection systems to controller area networks
Malmö universitet, Fakulteten för teknik och samhälle (TS).
2021 (engelsk)Independent thesis Basic level (degree of Bachelor), 10 poäng / 15 hpOppgave
Abstract [en]

With the digitalization and the ever more computerization of personal vehicles, new attack surfaces are introduced, challenging the security of the in-vehicle network. There is never such a thing as fully securing any computer system, nor learning all the methods of attack in order to prevent a break-in into a system. Instead, with sophisticated methods, we can focus on detecting and preventing attacks from being performed inside a system. The current state of the art of such methods, named intrusion detection systems (IDS), is divided into two main approaches. One approach makes its models very confident of detecting malicious activity, however only on activities that has been previously learned by this model. The second approach is very good at constructing models for detecting any type of malicious activity, even if never studied by the model before, but with less confidence. In this thesis, a new approach is suggested with a redesigned architecture for an intrusion detection system called Multi-mixed IDS. Where we take a middle ground between the two standardized approaches, trying to find a combination of both sides strengths and eliminating its weaknesses. This thesis aims to deliver a proof of concept for a new approach in the current state of the art in the CAN-bus security research field. This thesis also brings up some background knowledge about CAN and intrusion detection systems, discussing their strengths and weaknesses in further detail. Additionally, a brief overview from a handpick of research contributions from the field are discussed. Further, a simple architecture is suggested, three individual detection models are trained and combined to be tested against a CAN-bus dataset. Finally, the results are examined and evaluated. The results from the suggested approach shows somewhat poor results compared to other suggested algorithms within the field. However, it also shows some good potential, if better decision methods between the individual algorithms that constructs the model can be found. 

sted, utgiver, år, opplag, sider
2021. , s. 41
Emneord [en]
CAN, Controller Area Network, IDS, Intrusion detection, personal vehicles, machine learning, hybrid, proof of concept, embeded systems, software architecture, malicious, security
HSV kategori
Identifikatorer
URN: urn:nbn:se:mau:diva-43450OAI: oai:DiVA.org:mau-43450DiVA, id: diva2:1566210
Utdanningsprogram
TS Systemutvecklare
Presentation
, Malmö (engelsk)
Veileder
Examiner
Tilgjengelig fra: 2021-06-28 Laget: 2021-06-14 Sist oppdatert: 2021-07-06bibliografisk kontrollert

Open Access i DiVA

fulltext(3418 kB)559 nedlastinger
Filinformasjon
Fil FULLTEXT02.pdfFilstørrelse 3418 kBChecksum SHA-512
7388db71058209b735aa936cf49937b66ba2712c241a3414eecc003c73be7300770bb25db614d213ca1e0781d6b9803c0c92b0b1fb8e94fbaecd1de35f0f3ac5
Type fulltextMimetype application/pdf

Av organisasjonen

Søk utenfor DiVA

GoogleGoogle Scholar
Totalt: 559 nedlastinger
Antall nedlastinger er summen av alle nedlastinger av alle fulltekster. Det kan for eksempel være tidligere versjoner som er ikke lenger tilgjengelige

urn-nbn

Altmetric

urn-nbn
Totalt: 709 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf