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  • 1. Calandriello, G.
    et al.
    Papadimitratos, Panagiotis
    Lioy, A.
    Hubaux, J. -P
    On the Performance of Secure Vehicular Communication Systems2011In: IEEE Transactions on Dependable and Secure Computing, ISSN 1545-5971, E-ISSN 1941-0018Article in journal (Refereed)
  • 2.
    Holm, Hannes
    KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems.
    A Large-Scale Study of the Time Required To Compromise a Computer System2014In: IEEE Transactions on Dependable and Secure Computing, ISSN 1545-5971, E-ISSN 1941-0018, Vol. 11, no 1, p. 6506084-Article in journal (Refereed)
    Abstract [en]

    A frequent assumption in the domain of cybersecurity is that cyberintrusions follow the properties of a Poisson process, i.e., that the number of intrusions is well modeled by a Poisson distribution and that the time between intrusions is exponentially distributed. This paper studies this property by analyzing all cyberintrusions that have been detected across more than 260,000 computer systems over a period of almost three years. The results show that the assumption of a Poisson process model might be unoptimalâthe log-normal distribution is a significantly better fit in terms of modeling both the number of detected intrusions and the time between intrusions, and the Pareto distribution is a significantly better fit in terms of modeling the time to first intrusion. The paper also analyzes whether time to compromise (TTC) increase for each successful intrusion of a computer system. The results regarding this property suggest that time to compromise decrease along the number of intrusions of a system.

  • 3.
    Holm, Hannes
    et al.
    KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems.
    Ekstedt, Mathias
    KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems.
    Andersson, Dennis
    Swedish Defense Research Agency.
    Empirical Analysis of System-Level Vulnerability Metrics through Actual Attacks2012In: IEEE Transactions on Dependable and Secure Computing, ISSN 1545-5971, E-ISSN 1941-0018, Vol. 9, no 6, p. 825-837Article in journal (Refereed)
    Abstract [en]

    The Common Vulnerability Scoring System (CVSS) is a widely used and well-established standard for classifying the severity of security vulnerabilities. For instance, all vulnerabilities in the US National Vulnerability Database (NVD) are scored according to this method. As computer systems typically have multiple vulnerabilities, it is often desirable to aggregate the score of individual vulnerabilities to a system level. Several such metrics have been proposed, but their quality has not been studied. This paper presents a statistical analysis of how 18 security estimation metrics based on CVSS data correlate with the time-to-compromise of 34 successful attacks. The empirical data originates from an international cyber defense exercise involving over 100 participants and were collected by studying network traffic logs, attacker logs, observer logs, and network vulnerabilities. The results suggest that security modeling with CVSS data alone does not accurately portray the time-to-compromise of a system. However, results also show that metrics employing more CVSS data are more correlated with time-to-compromise. As a consequence, models that only use the weakest link (most severe vulnerability) to compose a metric are less promising than those that consider all vulnerabilities.

  • 4.
    Holm, Hannes
    et al.
    KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems.
    Shahzad, Khurram
    KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems.
    Buschle, Markus
    KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems.
    Ekstedt, Mathias
    KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems.
    P2CySeMoL: Predictive, Probabilistic Cyber Security Modeling Language2015In: IEEE Transactions on Dependable and Secure Computing, ISSN 1545-5971, E-ISSN 1941-0018, Vol. 12, no 6, p. 626-639Article in journal (Refereed)
    Abstract [en]

    This paper presents the Predictive, Probabilistic Cyber Security Modeling Language ((PCySeMoL)-Cy-2), an attack graph tool that can be used to estimate the cyber security of enterprise architectures. (PCySeMoL)-Cy-2 includes theory on how attacks and defenses relate quantitatively; thus, users must only model their assets and how these are connected in order to enable calculations. The performance of (PCySeMoL)-Cy-2 enables quick calculations of large object models. It has been validated on both a component level and a system level using literature, domain experts, surveys, observations, experiments and case studies.

  • 5.
    Johnson, Pontus
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Lagerström, Robert
    KTH, School of Electrical Engineering and Computer Science (EECS), Network and Systems engineering.
    Ekstedt, Mathias
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Franke, Ulrik
    SICS.
    Can the Common Vulnerability Scoring System be Trusted?: A Bayesian Analysis2018In: IEEE Transactions on Dependable and Secure Computing, ISSN 1545-5971, E-ISSN 1941-0018, Vol. 15, no 6, p. 1002-1015, article id 7797152Article in journal (Refereed)
    Abstract [en]

    The Common Vulnerability Scoring System (CVSS) is the state-of-the art system for assessing software vulnerabilities. However, it has been criticized for lack of validity and practitioner relevance. In this paper, the credibility of the CVSS scoring data found in five leading databases – NVD, X-Force, OSVDB, CERT-VN, and Cisco – is assessed. A Bayesian method is used to infer the most probable true values underlying the imperfect assessments of the databases, thus circumventing the problem that ground truth is not known. It is concluded that with the exception of a few dimensions, the CVSS is quite trustworthy. The databases are relatively consistent, but some are better than others. The expected accuracy of each database for a given dimension can be found by marginalizing confusion matrices. By this measure, NVD is the best and OSVDB is the worst of the assessed databases.

  • 6. Poturalski, Marcin
    et al.
    Papadimitratos, Panos
    KTH, School of Electrical Engineering (EES), Communication Networks.
    Hubaux, Jean-Pierre
    Formal Analysis of Secure Neighbor Discovery in Wireless Networks2013In: IEEE Transactions on Dependable and Secure Computing, ISSN 1545-5971, E-ISSN 1941-0018, Vol. 10, no 6, p. 355-367Article in journal (Refereed)
    Abstract [en]

    We develop a formal framework for the analysis of security protocols in wireless networks. The framework captures characteristics necessary to reason about neighbor discovery protocols, such as the neighbor relation, device location, and message propagation time. We use this framework to establish general results about the possibility of neighbor discovery. In particular, we show that time-based protocols cannot in general provide secure neighbor discovery. Given this insight, we also use the framework to prove the security of four concrete neighbor discovery protocols, including two novel time-and-location-based protocols. We mechanize the model and some proofs in the theorem prover Isabelle.

  • 7. Shokri, Reza
    et al.
    Theodorakopoulos, George
    Papadimitratos, Panos
    KTH, School of Electrical Engineering (EES).
    Kazemi, Ehsan
    Hubaux, Jean-Pierre
    Hiding in the Mobile Crowd: Location Privacy through Collaboration2014In: IEEE Transactions on Dependable and Secure Computing, ISSN 1545-5971, E-ISSN 1941-0018, Vol. 11, no 3, p. 266-279Article in journal (Refereed)
    Abstract [en]

    Location-aware smartphones support various location-based services (LBSs): users query the LBS server and learn on the fly about their surroundings. However, such queries give away private information, enabling the LBS to track users. We address this problem by proposing a user-collaborative privacy-preserving approach for LBSs. Our solution does not require changing the LBS server architecture and does not assume third party servers; yet, it significantly improves users' location privacy. The gain stems from the collaboration of mobile devices: they keep their context information in a buffer and pass it to others seeking such information. Thus, a user remains hidden from the server, unless all the collaborative peers in the vicinity lack the sought information. We evaluate our scheme against the Bayesian localization attacks that allow for strong adversaries who can incorporate prior knowledge in their attacks. We develop a novel epidemic model to capture the, possibly time-dependent, dynamics of information propagation among users. Used in the Bayesian inference framework, this model helps analyze the effects of various parameters, such as users' querying rates and the lifetime of context information, on users' location privacy. The results show that our scheme hides a high fraction of location-based queries, thus significantly enhancing users' location privacy. Our simulations with real mobility traces corroborate our model-based findings. Finally, our implementation on mobile platforms indicates that it is lightweight and the cost of collaboration is negligible.

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