4,838 research outputs found
Economic Factors of Vulnerability Trade and Exploitation
Cybercrime markets support the development and diffusion of new attack
technologies, vulnerability exploits, and malware. Whereas the revenue streams
of cyber attackers have been studied multiple times in the literature, no
quantitative account currently exists on the economics of attack acquisition
and deployment. Yet, this understanding is critical to characterize the
production of (traded) exploits, the economy that drives it, and its effects on
the overall attack scenario. In this paper we provide an empirical
investigation of the economics of vulnerability exploitation, and the effects
of market factors on likelihood of exploit. Our data is collected
first-handedly from a prominent Russian cybercrime market where the trading of
the most active attack tools reported by the security industry happens. Our
findings reveal that exploits in the underground are priced similarly or above
vulnerabilities in legitimate bug-hunting programs, and that the refresh cycle
of exploits is slower than currently often assumed. On the other hand,
cybercriminals are becoming faster at introducing selected vulnerabilities, and
the market is in clear expansion both in terms of players, traded exploits, and
exploit pricing. We then evaluate the effects of these market variables on
likelihood of attack realization, and find strong evidence of the correlation
between market activity and exploit deployment. We discuss implications on
vulnerability metrics, economics, and exploit measurement.Comment: 17 pages, 11 figures, 14 table
Methodologies to develop quantitative risk evaluation metrics
The goal of this work is to advance a new methodology to measure a severity cost for each host using the Common Vulnerability Scoring System (CVSS) based on base, temporal and environmental metrics by combining related sub-scores to produce a unique severity cost by modeling the problem's parameters in to a mathematical framework. We build our own CVSS Calculator using our equations to simplify the calculations of the vulnerabilities scores and to benchmark with other models. We design and develop a new approach to represent the cost assigned to each host by dividing the scores of the vulnerabilities to two main levels of privileges, user and root, and we classify these levels into operational levels to identify and calculate the severity cost of multi steps vulnerabilities. Finally we implement our framework on a simple network, using Nessus scanner as tool to discover known vulnerabilities and to implement the results to build and represent our cost centric attack graph
An Analytical Evaluation of Network Security Modelling Techniques Applied to Manage Threats
The current ubiquity of information coupled with
the reliance on such data by businesses has led to a great
deal of resources being deployed to ensure the security of this
information. Threats can come from a number of sources and the
dangers from those insiders closest to the source have increased
significantly recently. This paper focuses on techniques used to
identify and manage threats as well as the measures that every
organisation should consider to put into action. A novel game-based
onion skin model has been proposed, combining techniques
used in theory-based and hardware-based hardening strategies
Estimating ToE Risk Level using CVSS
Security management is about calculated risk and requires continuous evaluation to ensure cost, time and resource effectiveness. Parts of which is to make future-oriented, cost-benefit investments in security. Security investments must adhere to healthy business principles where both security and financial aspects play an important role. Information on the current and potential risk level is essential to successfully trade-off security and financial aspects. Risk level is the combination of the frequency and impact of a potential unwanted event, often referred to as a security threat or misuse. The paper presents a risk level estimation model that derives risk level as a conditional probability over frequency and impact estimates. The frequency and impact estimates are derived from a set of attributes specified in the Common Vulnerability Scoring System (CVSS). The model works on the level of vulnerabilities (just as the CVSS) and is able to compose vulnerabilities into service levels. The service levels define the potential risk levels and are modelled as a Markov process, which are then used to predict the risk level at a particular time
Tiresias: Predicting Security Events Through Deep Learning
With the increased complexity of modern computer attacks, there is a need for
defenders not only to detect malicious activity as it happens, but also to
predict the specific steps that will be taken by an adversary when performing
an attack. However this is still an open research problem, and previous
research in predicting malicious events only looked at binary outcomes (e.g.,
whether an attack would happen or not), but not at the specific steps that an
attacker would undertake. To fill this gap we present Tiresias, a system that
leverages Recurrent Neural Networks (RNNs) to predict future events on a
machine, based on previous observations. We test Tiresias on a dataset of 3.4
billion security events collected from a commercial intrusion prevention
system, and show that our approach is effective in predicting the next event
that will occur on a machine with a precision of up to 0.93. We also show that
the models learned by Tiresias are reasonably stable over time, and provide a
mechanism that can identify sudden drops in precision and trigger a retraining
of the system. Finally, we show that the long-term memory typical of RNNs is
key in performing event prediction, rendering simpler methods not up to the
task
CRAC: Confidentiality Risk Assessment and IT-Architecture Comparison
CRAC is an IT-architecture-based method for assessing and comparing confidentiality risks of distributed IT systems. The method determines confidentiality risks by taking into account the effects of the leakage of confidential information (e.g. industrial secrets), and the paths that may be followed by different attackers (e.g. insider and outsider). We evaluate its effectiveness by applying it to a real-world outsourcing case
A novel risk assessment and optimisation model for a multi-objective network security countermeasure selection problem
Budget cuts and the high demand in strengthening the security of computer systems and services constitute a challenge. Poor system knowledge and inappropriate selection of security measures may lead to unexpected
financial and data losses. This paper proposes a novel Risk Assessment and Optimisation Model (RAOM) to solve a security countermeasure selection problem, where variables such as financial cost and risk may affect a final decision. A Multi-Objective Tabu Search (MOTS) algorithm has been developed to construct an efficient frontier of non-dominated solutions, which can satisfy organisational security needs in a cost-effective
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