2,596 research outputs found

    Combining exposure indicators and predictive analytics for threats detection in real industrial IoT sensor networks

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    We present a framework able to combine exposure indicators and predictive analytics using AI-tools and big data architectures for threats detection inside a real industrial IoT sensors network. The described framework, able to fill the gaps between these two worlds, provides mechanisms to internally assess and evaluate products, services and share results without disclosing any sensitive and private information. We analyze the actual state of the art and a possible future research on top of a real case scenario implemented into a technological platform being developed under the H2020 ECHO project, for sharing and evaluating cybersecurity relevant informations, increasing trust and transparency among different stakeholders

    Detection and Prevention of Unknown Vulnerabilities on Enterprise IP Networks

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    Computer networks have long become the backbone of Enterprise Information System. The substantial share of the security problems are still encountered in Enterprise Network. Cyber espionage can effect Ethical, Military, Political and Economic interest anywhere. To provide secure computer networks, it is necessary to measure the relative effectiveness of security solution in the network. A network security metric enable a direct measurement and comparison of the amounts of security provided by different security solutions .In this paper we propose a novel security metric Zero Day Vulnerability Prevention Framework consists of bunches of algorithms. The above framework detects and prevents unknown vulnerabilities in Enterprise IP networks. It also protects the behavior of the sessions performed by the user from the huge range of attacks. It helps in monitoring database requests and prevents the attacks. The proposed framework also implements worm and virus detection to evaluate malware from the data. The system also presents scoring to the vulnerabilities and finally it performs security analysis with the help of Topological Vulnerability Analysis (TVA) tool. DOI: 10.17762/ijritcc2321-8169.15028

    Predictive Cyber-security Analytics Framework: A non-homogenous Markov model for Security Quantification

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    Numerous security metrics have been proposed in the past for protecting computer networks. However we still lack effective techniques to accurately measure the predictive security risk of an enterprise taking into account the dynamic attributes associated with vulnerabilities that can change over time. In this paper we present a stochastic security framework for obtaining quantitative measures of security using attack graphs. Our model is novel as existing research in attack graph analysis do not consider the temporal aspects associated with the vulnerabilities, such as the availability of exploits and patches which can affect the overall network security based on how the vulnerabilities are interconnected and leveraged to compromise the system. Gaining a better understanding of the relationship between vulnerabilities and their lifecycle events can provide security practitioners a better understanding of their state of security. In order to have a more realistic representation of how the security state of the network would vary over time, a nonhomogeneous model is developed which incorporates a time dependent covariate, namely the vulnerability age. The daily transition-probability matrices are estimated using Frei's Vulnerability Lifecycle model. We also leverage the trusted CVSS metric domain to analyze how the total exploitability and impact measures evolve over a time period for a given network.Comment: 16 pages, 6 Figures in International Conference of Security, Privacy and Trust Management 201

    An Approach to Select Cost-Effective Risk Countermeasures Exemplified in CORAS

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    Risk is unavoidable in business and risk management is needed amongst others to set up good security policies. Once the risks are evaluated, the next step is to decide how they should be treated. This involves managers making decisions on proper countermeasures to be implemented to mitigate the risks. The countermeasure expenditure, together with its ability to mitigate risks, is factors that affect the selection. While many approaches have been proposed to perform risk analysis, there has been less focus on delivering the prescriptive and specific information that managers require to select cost-effective countermeasures. This paper proposes a generic approach to integrate the cost assessment into risk analysis to aid such decision making. The approach makes use of a risk model which has been annotated with potential countermeasures, estimates for their cost and effect. A calculus is then employed to reason about this model in order to support decision in terms of decision diagrams. We exemplify the instantiation of the generic approach in the CORAS method for security risk analysis.Comment: 33 page

    Security risk assessment in cloud computing domains

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    Cyber security is one of the primary concerns persistent across any computing platform. While addressing the apprehensions about security risks, an infinite amount of resources cannot be invested in mitigation measures since organizations operate under budgetary constraints. Therefore the task of performing security risk assessment is imperative to designing optimal mitigation measures, as it provides insight about the strengths and weaknesses of different assets affiliated to a computing platform. The objective of the research presented in this dissertation is to improve upon existing risk assessment frameworks and guidelines associated to different key assets of Cloud computing domains - infrastructure, applications, and users. The dissertation presents various informal approaches of performing security risk assessment which will help to identify the security risks confronted by the aforementioned assets, and utilize the results to carry out the required cost-benefit tradeoff analyses. This will be beneficial to organizations by aiding them in better comprehending the security risks their assets are exposed to and thereafter secure them by designing cost-optimal mitigation measures --Abstract, page iv
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