Forensically-sound analysis of security risks of using local password managers

Conference item


Gray, Joshua, Franqueira, Virginia N. L. and Yu, Yijun 2016. Forensically-sound analysis of security risks of using local password managers. IEEE Computer Society. https://doi.org/10.1109/REW.2016.034
AuthorsGray, Joshua, Franqueira, Virginia N. L. and Yu, Yijun
Abstract

Password managers address the usability challenge of authentication, i.e., to manage the effort in creating, memorising, and entering complex passwords for an end-user. Offering features such as creating strong passwords, managing increasing number of complex passwords, and auto-filling of passwords for variable contexts, their security is as critical as the assets being protected by the passwords. Previous security risk analyses have focused primarily on cloud- and browser-based password managers, whilst the security risks of local password managers were left under-explored. Taking a systematic forensic analysis approach, this paper reports on a case study of three popular local password managers: KeePass (v2.28), Password Safe (v3.35.1) and RoboForm (v7.9.12). It revealed risks that either the master password or the content of the password database could be found unencrypted in Temp folders, Page files or Recycle bin, even after applications had been closed. As a consequence, an attacker or a malware with access to the computer on which the password managers were running may be able to steal sensitive information, even though these password managers are meant to keep the databases encrypted and protected at all times. These findings point to directions to mitigate the identified risks.

Password managers address the usability challenge of authentication, i.e., to manage the effort in creating, memorising, and entering complex passwords for an end-user. Offering features such as creating strong passwords, managing increasing number of complex passwords, and auto-filling of passwords for variable contexts, their security is as critical as the assets being protected by the passwords. Previous security risk analyses have focused primarily on cloud- and browser-based password managers, whilst the security risks of local password managers
were left under-explored. Taking a systematic forensic analysis approach, this paper reports on a case study of three popular local password managers: KeePass (v2.28), Password Safe
(v3.35.1) and RoboForm (v7.9.12). It revealed risks that either the master password or the content of the password database could be found unencrypted in Temp folders, Page files or Recycle bin, even after applications had been closed. As a consequence, an attacker or a malware with access to the computer on which the password managers were running may be able to steal sensitive information, even though these password managers are meant to keep the databases encrypted and protected at all times. These findings point to directions to mitigate the identified risks.

KeywordsPassword managers; Authentication; Security risk; Digital forensics
Year2016
PublisherIEEE Computer Society
Digital Object Identifier (DOI)https://doi.org/10.1109/REW.2016.034
Web address (URL)http://hdl.handle.net/10545/620627
hdl:10545/620627
ISBN9781509036943
File
File Access Level
Open
File
File Access Level
Open
Publication dates12 Sep 2016
Publication process dates
Deposited17 Oct 2016, 15:49
License

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

ContributorsUniversity of Derby and The Open University
Permalink -

https://repository.derby.ac.uk/item/94576/forensically-sound-analysis-of-security-risks-of-using-local-password-managers

Download files


File
license.txt
File access level: Open

  • 60
    total views
  • 48
    total downloads
  • 0
    views this month
  • 0
    downloads this month

Export as

Related outputs

Deep labeller: automatic bounding box generation for synthetic violence detection datasets
Nadeem, M., Kurugollu, F., Saravi, S., Atlam, H. and Franqueira, V. 2023. Deep labeller: automatic bounding box generation for synthetic violence detection datasets. Multimedia Tools and Applications. pp. 1-18. https://doi.org/10.1007/s11042-023-15621-5
Integration and evaluation of QUIC and TCP-BBR in longhaul science data transfers
Lopes, Raul H. C., Franqueira, Virginia N. L. and Duncan, Rand 2019. Integration and evaluation of QUIC and TCP-BBR in longhaul science data transfers. EPJ Web of Conferences. 214, p. 08026. https://doi.org/10.1051/epjconf/201921408026
Privacy verification of photoDNA based on machine learning
Nadeem, Muhammad Shahroz, Franqueira, Virginia N. L. and Zhai, Xiaojun 2019. Privacy verification of photoDNA based on machine learning. in: The Institution of Engineering and Technology (IET).
Behavioural Digital Forensics Model: Embedding Behavioural Evidence Analysis into the Investigation of Digital Crimes
Al Mutawa, Noora, Bryce, Joanne, Franqueira, Virginia N.L., Marrington, Andrew and Read, Janet C. 2018. Behavioural Digital Forensics Model: Embedding Behavioural Evidence Analysis into the Investigation of Digital Crimes. Digital Investigation. https://doi.org/10.1016/j.diin.2018.12.003
Analytical tools for blockchain: review, taxonomy and open challenges.
Balaskas, Anastasios and Franqueira, Virginia N. L. 2018. Analytical tools for blockchain: review, taxonomy and open challenges. IEE Explore. https://doi.org/10.1109/CyberSecPODS.2018.8560672
Blockchain-Based Distributed Marketplace.
Kabi, Oliver R. and Franqueira, Virginia N. L. 2019. Blockchain-Based Distributed Marketplace. in: Springer Nature.
A survey of deep learning solutions for multimedia visual content analysis.
Nadeem, Muhammad Shahroz, Franqueira, Virginia N. L., Zhai, Xiaojun and Kurugollu, Fatih 2019. A survey of deep learning solutions for multimedia visual content analysis. IEEE Access. https://doi.org/10.1109/ACCESS.2019.DOI
A tutorial introduction to IoT design and prototyping with examples.
Meruje, Manuel, Sumaila, Musa, Franqueira, Virginia N. L., Freire, Mario M. and Inacio, Pedro R. M. 2018. A tutorial introduction to IoT design and prototyping with examples. in: Wiley-IEEE Press.
Cloud forensics and security
Franqueira, Virginia N. L., Lopes, Raul, Jones, Andrew and Storer, Tim 2017. Cloud forensics and security. SpringerOpen.
On Locky ransomware, Al Capone and Brexit
MacRae, John and Franqueira, Virginia N. L. 2018. On Locky ransomware, Al Capone and Brexit. in: Springer.
Man-In-The-Middle attacks in Vehicular Ad-Hoc Networks: Evaluating the impact of attackers’ strategies.
Ahmad, F., Adnane, Asma, Franqueira, Virginia N. L., Kurugollu, Fatih and Liu, Lu 2018. Man-In-The-Middle attacks in Vehicular Ad-Hoc Networks: Evaluating the impact of attackers’ strategies. Sensors. 18 (11), p. 4040. https://doi.org/10.3390/s18114040
TEAM: A trust evaluation and management framework in context-enabled vehicular ad-hoc networks.
Ahmad, F., Franqueira, Virginia N. L. and Adnane, Asma 2018. TEAM: A trust evaluation and management framework in context-enabled vehicular ad-hoc networks. IEEE Access. https://doi.org/10.1109/ACCESS.2018.2837887
Investigation of indecent images of children cases: Challenges and suggestions collected from the trenches.
Franqueira, Virginia N. L., Bryce, Joanne, Al Mutawa, Noora and Marrington, Andrew 2017. Investigation of indecent images of children cases: Challenges and suggestions collected from the trenches. Digital Investigation. https://doi.org/10.1016/j.diin.2017.11.002
Parallel Monte Carlo search for Hough Transform.
Lopes, Raul, Franqueira, Virginia N. L., Reid, Ivan D. and Hobson, Peter 2017. Parallel Monte Carlo search for Hough Transform. Journal of Physics: Conference Series. https://doi.org/10.1088/1742-6596/898/7/072052
Verifiable public key encryption scheme with equality test in 5G networks
Xu, Yan, Wang, Ming, Zhong, Hong, Cui, Jie, Liu, Lu and Franqueira, Virginia N. L. 2017. Verifiable public key encryption scheme with equality test in 5G networks. IEEE Access. https://doi.org/10.1109/ACCESS.2017.2716971
Special issue on cyberharassment investigation: Advances and trends
Bryce, Joanne, Franqueira, Virginia N. L. and Marrington, Andrew 2016. Special issue on cyberharassment investigation: Advances and trends. Journal of Digital Forensics, Security and Law (JDFSL).
Faith in vehicles: A set of evaluation criteria for trust management in vehicular ad-hoc network
Ahmad, F., Hall, Jordan, Adnane, Asma and Franqueira, Virginia N. L. 2017. Faith in vehicles: A set of evaluation criteria for trust management in vehicular ad-hoc network. IEEE.
Forensic investigation of cyberstalking cases using Behavioural Evidence Analysis
Al Mutawa, Noora, Bryce, Joanne, Marrington, Andrew and Franqueira, Virginia N. L. 2016. Forensic investigation of cyberstalking cases using Behavioural Evidence Analysis. Digital Investigation. https://doi.org/10.1016/j.diin.2016.01.012
To flip or not to flip: a critical interpretive synthesis of flipped teaching
Franqueira, Virginia N. L. and Tunnicliffe, Peter 2015. To flip or not to flip: a critical interpretive synthesis of flipped teaching. in: Springer International Publishing.
Factors influencing digital forensic investigations: Empirical evaluation of 12 years of Dubai police cases
Al Awadhi, Ibtesam, Read, Janet C., Marrington, Andrew and Franqueira, Virginia N. L. 2015. Factors influencing digital forensic investigations: Empirical evaluation of 12 years of Dubai police cases. Journal of Digital Forensics, Security and Law (JDFSL).
A systematic approach for cyber security in vehicular networks
Ahmad, F., Adnane, Asma and Franqueira, Virginia N. L. 2016. A systematic approach for cyber security in vehicular networks. Journal of Computer and Communications. https://doi.org/10.4236/jcc.2016.416004
Introduction to special issue on risk and trust in embedded critical systems
Rossebø, Judith E. Y., Houmb, Siv H., Georg, Geri, Franqueira, Virginia N. L. and Serpanos, Dimitrios 2014. Introduction to special issue on risk and trust in embedded critical systems. ACM Transactions on Embedded Computing Systems. https://doi.org/10.1145/2659008
Efficient computation of hashes
Lopes, Raul, Franqueira, Virginia N. L. and Hobson, Peter 2014. Efficient computation of hashes. Journal of Physics: Conference Series. https://doi.org/10.1088/1742-6596/513/3/032042
Behavioural evidence analysis applied to digital forensics: An empirical analysis of child pornography cases using P2P networks
Mutawa, Noora Al, Bryce, Joanne, Franqueira, Virginia N. L. and Marrington, Andrew 2015. Behavioural evidence analysis applied to digital forensics: An empirical analysis of child pornography cases using P2P networks. IEEE Computer Society. https://doi.org/10.1109/ARES.2015.49
Challenges of data provenance for cloud forensic investigations
Katilu, Victoria M., Franqueira, Virginia N. L. and Angelopoulou, Olga 2015. Challenges of data provenance for cloud forensic investigations. IEEE Computer Society. https://doi.org/10.1109/ARES.2015.54
Automated analysis of security requirements through risk-based argumentation
Yu, Yijun, Franqueira, Virginia N. L., Than Tun, Thein, Wieringa, Roel J. and Nuseibeh, Bashar 2015. Automated analysis of security requirements through risk-based argumentation. Journal of Systems and Software. https://doi.org/10.1016/j.jss.2015.04.065