Готові списки джерел за темами / Metric Security

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Опубліковано: 6 вересня 2023

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Статті в журналах з теми "Metric Security"

1

Neto, Afonso Araújo, and Marco Vieira. "Benchmarking Untrustworthiness." International Journal of Dependable and Trustworthy Information Systems 1, no. 2 (April 2010): 32–54. http://dx.doi.org/10.4018/jdtis.2010040102.

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Benchmarking security is hard and, although there are many proposals of security metrics in the literature, no consensual quantitative security metric has been previously proposed. A key difficulty is that security is usually more influenced by what is unknown about a system than by what is known. In this paper, the authors propose the use of an untrustworthiness metric for benchmarking security. This metric, based on the idea of quantifying and exposing the trustworthiness relationship between a system and its owner, represents a powerful alternative to traditional security metrics. As an example, the authors propose a benchmark for Database Management Systems (DBMS) that can be easily used to assess and compare alternative database configurations based on minimum untrustworthiness, which is a low-cost and high-reward trust-based metric. The practical application of the benchmark in four real large database installations shows that untrustworthiness is a powerful metric for administrators to make informed security decisions by taking into account the specifics needs and characteristics of the environment being managed.
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Shan, Chun, Benfu Jiang, Jingfeng Xue, Fang Guan, and Na Xiao. "An Approach for Internal Network Security Metric Based on Attack Probability." Security and Communication Networks 2018 (2018): 1–11. http://dx.doi.org/10.1155/2018/3652170.

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A network security metric may provide quantifiable evidence to assist security practitioners in securing computer networks. However, research on security metrics based on attack graph is not applicable to the characteristics of internal attack; therefore we propose an internal network security metric method based on attack probability. Our approach has the following benefits: it provides the method of attack graph simplification with monitoring event node which could solve the attack graph exponential growth with the network size, while undermining the disguise of internal attacks and improving the efficiency of the entire method; the method of attack probability calculation based on simplified attack graph can simplify the complexity of internal attacks and improve the accuracy of the approach.
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Díaz, Álvaro, Javier González-Bayon, and Pablo Sánchez. "Security Estimation in Wireless Sensor Network Simulator." Journal of Circuits, Systems and Computers 25, no. 07 (April 22, 2016): 1650067. http://dx.doi.org/10.1142/s0218126616500675.

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Sensor nodes are low-power and low-cost devices with the requirement of a long autonomous lifetime. Therefore, the nodes have to use the available power carefully and avoid expensive computations or radio transmissions. In addition, as some wireless sensor networks (WSNs) process sensitive data, selecting a security protocol is vital. Cryptographic methods used in WSNs should fulfill the constraints of sensor nodes and should be evaluated for their security and power consumption. WSN engineers use several metrics to obtain estimations prior to network deployment. These metrics are usually related to power and execution time estimation. However, security is a feature that cannot be estimated and it is either “active” or “inactive”, with no possibility of introducing intermediate security levels. This lack of flexibility is a disadvantage in real deployments where different operation modes with different security and power specifications are often needed. This paper proposes including a new security estimation metric in a previously proposed framework for WSN simulation and embedded software (SW) performance analysis. This metric is called Security Estimation Metric (SEM) and it provides information about the security encryption used in WSN transmissions. Results show that the metric improves flexibility, granularity and execution time compared to other cryptographic tests.
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4

Yee, George O. M. "Designing Sound Security Metrics." International Journal of Systems and Software Security and Protection 10, no. 1 (January 2019): 1–21. http://dx.doi.org/10.4018/ijsssp.2019010101.

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Анотація:
This article begins with an introduction to security metrics, describing the need for security metrics, followed by a discussion of the nature of security metrics, including the challenges found with some security metrics used in the past. The article then discusses what makes a sound security metric and proposes a rigorous step-by-step method that can be applied to design sound security metrics, and to test existing security metrics to see if they are sound metrics. This is followed by a discussion of the feasibility of having scientifically-based security metrics and whether or not such metrics are sound. Application examples are included to illustrate the design and testing of sound security metrics.
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Shao, Yanli, Jingru Zhao, Xingqi Wang, Weiwei Wu, and Jinglong Fang. "Research on Cross-Company Defect Prediction Method to Improve Software Security." Security and Communication Networks 2021 (August 24, 2021): 1–19. http://dx.doi.org/10.1155/2021/5558561.

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Анотація:
As the scale and complexity of software increase, software security issues have become the focus of society. Software defect prediction (SDP) is an important means to assist developers in discovering and repairing potential defects that may endanger software security in advance and improving software security and reliability. Currently, cross-project defect prediction (CPDP) and cross-company defect prediction (CCDP) are widely studied to improve the defect prediction performance, but there are still problems such as inconsistent metrics and large differences in data distribution between source and target projects. Therefore, a new CCDP method based on metric matching and sample weight setting is proposed in this study. First, a clustering-based metric matching method is proposed. The multigranularity metric feature vector is extracted to unify the metric dimension while maximally retaining the information contained in the metrics. Then use metric clustering to eliminate metric redundancy and extract representative metrics through principal component analysis (PCA) to support one-to-one metric matching. This strategy not only solves the metric inconsistent and redundancy problem but also transforms the cross-company heterogeneous defect prediction problem into a homogeneous problem. Second, a sample weight setting method is proposed to transform the source data distribution. Wherein the statistical source sample frequency information is set as an impact factor to increase the weight of source samples that are more similar to the target samples, which improves the data distribution similarity between the source and target projects, thereby building a more accurate prediction model. Finally, after the above two-step processing, some classical machine learning methods are applied to build the prediction model, and 12 project datasets in NASA and PROMISE are used for performance comparison. Experimental results prove that the proposed method has superior prediction performance over other mainstream CCDP methods.
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6

Tjirare, D., and F. Bhunu Shava. "Developing Security Metrics to Evaluate Employee Awareness: a Case of a Ministry in Namibia." Namibian Journal for Research, Science and Technology 1, no. 1 (October 16, 2020): 11–18. http://dx.doi.org/10.54421/njrst.v1i1.5.

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Employees that lack security awareness may cause a threat to an organisation unintentionally. A mixed research method was used to conduct a case study to evaluate the security awareness levels of employees in one ministry to reduce the risk associated with security threats.A survey using a questionnaire was carried out with the ministry employees from four different departments. Collected data was quantitatively analysed to gauge the security risk of the organisation. Analysed survey results were used todevelop security metrics using the Goal Question Metric approach and security objectives as measurements. The metric evaluated the security awareness level of employees at the ministry. Employees’ responses were validated using helpdesk statistics on incident reporting and antivirus statistics. The security metrics aim to assist the Information Technology department to detect security breaches early, and then develop a security awareness program and policies to promote security best practices. On the other hand the metrics can be used to encourage top management to get involved. The results show that employees’ awareness level was mostly low or elevated. Security standards and best practices are recommended based on the findings of risk rating per securitycategory.
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7

Jouini, Mouna, and Latifa Ben Arfa Rabai. "A Security Risk Management Metric for Cloud Computing Systems." International Journal of Organizational and Collective Intelligence 4, no. 3 (July 2014): 1–21. http://dx.doi.org/10.4018/ijoci.2014070101.

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Анотація:
Cloud computing is a growing technology used by several organizations because it presents a cost effective policy to manage and control Information Technology (IT). It delivers computing services as a public utility rather than a personal one. However, despite these benefits, it presents many challenges including access control and security problems. In order to assess security risks, the paper gives an overview of security risk management metrics. Then, it illustrates the use of a cyber security measure to describe an economic security model for cloud computing system. Moreover, it proposes a cloud provider business model for security issues. Finally, the paper shows a solution related to the vulnerabilities in cloud systems using a new quantitative metric to reduce the probability that an architectural components fails. The main aim of this article is to quantify security threats in cloud computing environments due to security breaches using a new security metric.
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Le, Ngoc Thuy, and Doan B. Hoang. "A Threat Computation Model using a Markov Chain and Common Vulnerability Scoring System and its Application to Cloud Security." Journal of Telecommunications and the Digital Economy 7, no. 1 (March 30, 2019): 37–56. http://dx.doi.org/10.18080/jtde.v7n1.181.

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Securing cyber infrastructures has become critical because they are increasingly exposed to attackers while accommodating a huge number of IoT devices and supporting numerous sophisticated emerging applications. Security metrics are essential for assessing the security risks and making effective decisions concerning system security. Many security metrics rely on mathematical models, but are mainly based on empirical data, qualitative methods, or compliance checking, and this renders the outcome far from satisfactory. Computing the probability of an attack, or more precisely a threat that materialises into an attack, forms an essential basis for a quantitative security metric. This paper proposes a novel approach to compute the probability distribution of cloud security threats based on a Markov chain and Common Vulnerability Scoring System. Moreover, the paper introduces the method to estimate the probability of security attacks. The use of the new security threat model and its computation is demonstrated through their application to estimating the probabilities of cloud threats and types of attacks.
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9

Kim, Kisoo, Sangho Lee, Yeowung Yun, Jaemin Choi, and Hyungjin Mun. "Security Evaluation Metric of Windows-Based Information Security Products." Indian Journal of Science and Technology 8, S8 (April 1, 2015): 10. http://dx.doi.org/10.17485/ijst/2015/v8is8/71501.

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Ito, Kosuke, Shuji Morisaki, and Atsuhiro Goto. "IoT Security-Quality-Metrics Method and Its Conformity with Emerging Guidelines." IoT 2, no. 4 (December 15, 2021): 761–85. http://dx.doi.org/10.3390/iot2040038.

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This study proposes a security-quality-metrics method tailored for the Internet of things (IoT) and evaluates conformity of the proposed approach with pertinent cybersecurity regulations and guidelines for IoT. Cybersecurity incidents involving IoT devices have recently come to light; consequently, IoT security correspondence has become a necessity. The ISO 25000 series is used for software; however, the concept of security as a quality factor has not been applied to IoT devices. Because software vulnerabilities were not the device vendors’ responsibility as product liability, most vendors did not consider the security capability of IoT devices as part of their quality control. Furthermore, an appropriate IoT security-quality metric for vendors does not exist; instead, vendors have to set their security standards, which lack consistency and are difficult to justify by themselves. To address this problem, the authors propose a universal method for specifying IoT security-quality metrics on a globally accepted scale, inspired by the goal/question/metric (GQM) method. The method enables vendors to verify their products to conform to the requirements of existing baselines and certification programs and to help vendors to tailor their quality requirements to meet the given security requirements. The IoT users would also be able to use these metrics to verify the security quality of IoT devices.
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Дисертації з теми "Metric Security"

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Khan, Moazzam. "Security metric based risk assessment." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/47527.

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Modern day computer networks have become very complex and attackers have benefited due to this complexity and have found vulnerabilities and loopholes in the network architecture. In order to identify the attacks from an attacker all aspects of network architecture needs to be carefully examined such as packet headers, network scans, versions of applications, network scans, network anomalies etc. and after the examination attributes playing a significant impact on the security posture of the organization needs to be highlighted so that resources and efforts are directed towards those attributes. In this work we extensively look at network traffic at dormitory network of a large campus and try to identify the attributes that play a significant role in the infection of a machine. Our scheme is to collect as much attributes from the network traffic applying the heuristic of network infection and then devise a scheme called decision centric rank ordering of security metric that gives the priority to the security metrics so that network administrators can channel their efforts in the right direction. Another aspect of this research is to identify the probability of an attack on a communication infrastructure. A communication infrastructure becomes prone to attack if certain elements exist in it, such as vulnerabilities in the comprising elements of the system, existence of an attacker and motivation for him to attack. Focus of this study is on vulnerability assessment and security metrics such as user behavior, operating systems, user applications, and software updates. To achieve a quantified value of risk, a set of machines is carefully observed for the security metrics. Statistical analysis is applied on the data collected from compromised machines and the quantified value of risk is achieved.
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2

Owusu-Kesseh, Daniel. "The Relative Security Metric of Information Systems: Using AIMD Algorithms." University of Cincinnati / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1462278857.

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3

Karabey, Bugra. "Attack Tree Based Information Technology Security Metric Integrating Enterprise Objectives With Vulnerabilities." Phd thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12614100/index.pdf.

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Security is one of the key concerns in the domain of Information Technology systems. Maintaining the confidentiality, integrity and availability of such systems, mandates a rigorous prior analysis of the security risks that confront these systems. In order to analyze, mitigate and recover from these risks a metrics based methodology is essential in prioritizing the response strategies to these risks and also this approach is required for resource allocation schedules to mitigate such risks. In addition to that the Enterprise Objectives must be focally integrated in the definition, impact calculation and prioritization stages of this analysis to come up with metrics that are useful both for the technical and managerial communities within an organization. Also this inclusion will act as a preliminary filter to overcome the real life scalability issues inherent with such threat modeling efforts. Within this study an attack tree based approach will be utilized to offer an IT Security Risk Evaluation Method and Metric called TEOREM (Tree based Enterprise Objectives Risk Evaluation Method and Metric) that integrates the Enterprise Objectives with the Information Asset vulnerability analysis within an organization. Applicability of the method has been analyzed within a real life setting and the findings are discussed as well within this study.
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4

Erturk, Volkan. "A Framework Based On Continuous Security Monitoring." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12610139/index.pdf.

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Continuous security monitoring is the process of following up the IT systems by collecting measurements, reporting and analysis of the results for comparing the security level of the organization on continuous time axis to see how organizational security is progressing in the course of time. In the related literature there is very limited work done to continuously monitor the security of the organizations. In this thesis, a continuous security monitoring framework based on security metrics is proposed. Moreover, to decrease the burden of implementation a software tool called SecMon is introduced. The implementation of the framework in a public organization shows that the proposed system is successful for building an organizational memory and giving insight to the security stakeholders about the IT security level in the organization.
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5

Rich, Ronald P., and Jonathan S. Holmgren. "Metric methodology for the creation of environments and processes to certify a component : specifically the Naval Research Laboratory Pump." Thesis, Monterey, California. Naval Postgraduate School, 2003. http://hdl.handle.net/10945/1102.

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This thesis was completed in cooperation with the Cebrowski Institute for Information Innovation and Superiority.
Approved for public release; distribution is unlimited
A of the NP, but the key requirement for Certification and Accreditation is the creation of a Protection Profile and an understanding of the DITSCAP requirements and process. This thesis creates a Protection Profile for the NP along with a draft Type SSAA for Certification and Accreditation of the NP.
Lieutenant, United States Navy
Lieutenant, United States Navy
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6

Zhou, Luyuan. "Security Risk Analysis based on Data Criticality." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-93055.

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Nowadays, security risk assessment has become an integral part of network security as everyday life has become interconnected with and dependent on computer networks. There are various types of data in the network, often with different criticality in terms of availability or confidentiality or integrity of information. Critical data is riskier when it is exploited. Data criticality has an impact on network security risks. The challenge of diminishing security risks in a specific network is how to conduct network security risk analysis based on data criticality. An interesting aspect of the challenge is how to integrate the security metric and the threat modeling, and how to consider and combine the various elements that affect network security during security risk analysis. To the best of our knowledge, there exist no security risk analysis techniques based on threat modeling that consider the criticality of data. By extending the security risk analysis with data criticality, we consider its impact on the network in security risk assessment. To acquire the corresponding security risk value, a method for integrating data criticality into graphical attack models via using relevant metrics is needed. In this thesis, an approach for calculating the security risk value considering data criticality is proposed. Our solution integrates the impact of data criticality in the network by extending the attack graph with data criticality. There are vulnerabilities in the network that have potential threats to the network. First, the combination of these vulnerabilities and data criticality is identified and precisely described. Thereafter the interaction between the vulnerabilities through the attack graph is taken into account and the final security metric is calculated and analyzed. The new security metric can be used by network security analysts to rank security levels of objects in the network. By doing this, they can find objects that need to be given additional attention in their daily network protection work. The security metric could also be used to help them prioritize vulnerabilities that need to be fixed when the network is under attack. In general, network security analysts can find effective ways to resolve exploits in the network based on the value of the security metric.
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Holmgren, Jonathan S. Rich Ronald P. "Metric methodology for the creation of environments and processes to certify a component : specifically the Naval Research Laboratory Pump /." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2003. http://library.nps.navy.mil/uhtbin/hyperion-image/03Mar%5FHolmgren.pdf.

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Thesis (M.S. in Information Technology Management)--Naval Postgraduate School, March 2003.
Thesis advisor(s): George Dinolt, Craig Rasmussen. Includes bibliographical references (p. 155-157). Also available online.
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8

Homer, John. "A comprehensive approach to enterprise network security management." Diss., Manhattan, Kan. : Kansas State University, 2009. http://hdl.handle.net/2097/1372.

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Bilal, Muhammad, and Ganesh Sankar. "Trust & Security issues in Mobile banking and its effect on Customers." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3166.

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Context: The invention of mobile phones makes the human life easier. The purpose of this study is to identify security risks in mobile banking and to provide an authentication method for mobile banking transaction by using bio-metric mechanism. Objectives: Current mobile banking authentication is challenging and identified as a major security risk. Literature review shows that customer distrusts mobile banking due to security issues. The authors discuss security risks in current authentication methods in mobile banking. Methods: There are different methods and approaches to handle authentication in mobile banking. In this thesis, we propose a new approach of authentication in mobile banking. The strengths and weaknesses of existing approaches of authentication are identified with the help of Literature Review and interviews. The authors present basic transaction model and include security risks. By Literature Review it is found that finger print mechanism is a suitable method for authentication. Authors focus on authentication method and present a biometric scanning device which can identify the customer’s finger print thus enabling the customer to access mobile banking facility. Results: An authentication model is proposed through design process. The proposed biometric design was validated by conducting a workshop. The analysis of the workshop’s results showed that customer’s trust in security for mobile banking will be increased by finger print mechanism. To promote mobile banking, it is necessary to improve customer trust in terms of security. Conclusions: The authors concluded that, only authorized person will be able to use mobile banking services by incorporating bio-metric finger-print mechanism. By literature review and interview it was found that finger-print mechanism is more suitable than other ordinary mechanisms like login and password mechanism, SMS etc.
Using mobile phones for mobile banking, customers can push or pull the details like Funds transfer, Bill payment, Share trade, Check order and also inquiries like Account balance, Account statement and Check status Transaction history etc. It means that the customer is interacting with the files, databases etc., of the bank . Database at the server end is sensitive in terms of security. Customers distrust mobile devices to transfer money or for making any transactions. The reason is that security is a major concern for the customer’s fulfillment. Customer’s main concern in using mobile devices for mobile banking is the authentication method used to ensure that the right person is accessing the services like transaction etc.The authors made a basic model for mobile banking transaction. All security risks were included in the transaction model. Then the authors focused on authentication method. By literature review and interview it was concluded that security can be improved by bio metric methods. The authors focused on different bio-metric mechanism and concluded that fingerprint mechanism is more suitable as it requires less storage capacity in database and identifies the uniqueness of customers. The authors suggest a possible solution by proposing finger-print mechanism model and designed a bio-metric scanning device as a solution through which customer can interact with banking system using their finger-print. The result of workshop shows that bio-metric finger print mechanism is more suitable and secure then other authentication methods for mobile banking.
004531847791
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Taylor, Christopher P. "A Security Framework for Logic Locking Through Local and Global Structural Analysis." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1587681912604658.

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Книги з теми "Metric Security"

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Wang, Lingyu, Sushil Jajodia, and Anoop Singhal. Network Security Metrics. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66505-4.

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Security metrics: A beginner's guide. New York: McGraw-Hill, 2012.

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3

Metrics and methods for security risk management. Amsterdam: Syngress/Elsevier, 2010.

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4

Complete Guide to Security and Privacy Metrics. London: Taylor and Francis, 2007.

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5

IT security governance guidebook with security program metrics on CD-ROM. Boca Raton, FL: Auerbach Publications/Taylor & Francis, 2007.

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6

IT security metrics: A practical framework for measuring security & protecting data. New York: McGraw Hill, 2010.

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7

P, Halibozek Edward, ed. Security metrics management: How to measure the costs and benefits of security. Burlington, MA: Butterworth-Heinemann, 2006.

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8

Brotby, W. Krag. Information security management metrics: A definitive guide to effective security monitoring and measurement. Boca Raton: Auerbach Publications, 2009.

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9

Continuous authentication using biometrics: Data, models, and metrics. Hershey, PA: Information Science Reference, 2012.

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10

Sademies, Anni. Process approach to information security metrics in Finnish industry and state institutions. Espoo [Finland]: VTT Technical Research Centre of Finland, 2004.

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Частини книг з теми "Metric Security"

1

Whyte, Mark, and Chris Johnson. "Security and counter-terrorism." In Metric Handbook, 36–1. 7th ed. London: Routledge, 2021. http://dx.doi.org/10.4324/9781003052586-39.

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Whyte, Mark, and Chris Johnson. "Security and counter-terrorism." In Metric Handbook, 683–95. Sixth edition. | New York: Routledge, 2018.: Routledge, 2018. http://dx.doi.org/10.4324/9781315230726-37.

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JØsang, Audun. "A subjective metric of authentication." In Computer Security — ESORICS 98, 329–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0055873.

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Ganesan, Rajesh, Ankit Shah, Sushil Jajodia, and Hasan Cam. "A Novel Metric for Measuring Operational Effectiveness of a Cybersecurity Operations Center." In Network Security Metrics, 177–207. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66505-4_8.

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Pham, Dang Vinh, and Dogan Kesdogan. "A Combinatorial Approach for an Anonymity Metric." In Information Security and Privacy, 26–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02620-1_3.

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Bouyahia, Tarek, Muhammad Sabir Idrees, Nora Cuppens-Boulahia, Frédéric Cuppens, and Fabien Autrel. "Metric for Security Activities Assisted by Argumentative Logic." In Data Privacy Management, Autonomous Spontaneous Security, and Security Assurance, 183–97. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-17016-9_12.

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Yahya, Fara, Robert J. Walters, and Gary B. Wills. "Using Goal-Question-Metric (GQM) Approach to Assess Security in Cloud Storage." In Enterprise Security, 223–40. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54380-2_10.

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Casola, Valentina, Alessandra De Benedictis, Massimiliano Rak, and Umberto Villano. "A Security Metric Catalogue for Cloud Applications." In Advances in Intelligent Systems and Computing, 854–63. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61566-0_81.

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Wang, Lingyu, Tania Islam, Tao Long, Anoop Singhal, and Sushil Jajodia. "An Attack Graph-Based Probabilistic Security Metric." In Lecture Notes in Computer Science, 283–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-70567-3_22.

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Le Thanh Thai, Binh, and Hidema Tanaka. "A Novel Metric for Password Security Risk Against Dictionary Attacks." In Information Security Applications, 291–302. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-25659-2_21.

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Тези доповідей конференцій з теми "Metric Security"

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Preece, Bradley L., and Eric A. Flug. "A wavelet contrast metric for the targeting task performance metric." In SPIE Defense + Security, edited by Gerald C. Holst and Keith A. Krapels. SPIE, 2016. http://dx.doi.org/10.1117/12.2223855.

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Mozhaev, Oleksandr, Heorgii Kuchuk, Nina Kuchuk, Mykhailo Mozhaev, and Mykhailo Lohvynenko. "Multiservice network security metric." In 2017 2nd International Conference on Advanced Information and Communication Technologies (AICT). IEEE, 2017. http://dx.doi.org/10.1109/aiact.2017.8020083.

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Rodes, Benjamin D., John C. Knight, and Kimberly S. Wasson. "A security metric based on security arguments." In the 5th International Workshop. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2593868.2593880.

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McIntosh, John C., and Candace E. Clary. "Bistatic SAR imaging geometry performance metric." In Defense and Security, edited by Edmund G. Zelnio and Frederick D. Garber. SPIE, 2004. http://dx.doi.org/10.1117/12.542114.

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Tupper, Melanie, and A. Nur Zincir-Heywood. "VEA-bility Security Metric: A Network Security Analysis Tool." In 2008 Third International Conference on Availability, Reliability and Security. IEEE, 2008. http://dx.doi.org/10.1109/ares.2008.138.

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Hashemi, Seyed Mahmood, and Jingsha He. "An evolutionary approach for security metric." In 2015 International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 2015. http://dx.doi.org/10.1109/ictc.2015.7354776.

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Chandra, S., and R. A. Khan. "Software security metric identification framework (SSM)." In the International Conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1523103.1523250.

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Šišejković, Dominik, Rainer Leupers, Gerd Ascheid, and Simon Metzner. "A Unifying logic encryption security metric." In SAMOS XVIII: Architectures, Modeling, and Simulation. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3229631.3229636.

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Karabey, Bugra, and Nazife Baykal. "Information security metric integrating enterprise objectives." In 2009 International Carnahan Conference on Security Technology (ICCST). IEEE, 2009. http://dx.doi.org/10.1109/ccst.2009.5335549.

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Neto, Afonso Araujo, and Marco Vieira. "Untrustworthiness: A trust-based security metric." In 2009 Fourth International Conference on Risks and Security of Internet and Systems (CRiSIS 2009). IEEE, 2009. http://dx.doi.org/10.1109/crisis.2009.5411967.

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Звіти організацій з теми "Metric Security"

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Jansen, Wayne. Directions in security metrics research. Gaithersburg, MD: National Institute of Standards and Technology, 2009. http://dx.doi.org/10.6028/nist.ir.7564.

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Swanson, M., N. Bartol, J. Sabato, J. Hash, and L. Graffo. Security metrics guide for information technology systems. Gaithersburg, MD: National Institute of Standards and Technology, 2003. http://dx.doi.org/10.6028/nist.sp.800-55.

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Lippmann, R. P., J. F. Riordan, T. H. Yu, and K. K. Watson. Continuous Security Metrics for Prevalent Network Threats: Introduction and First Four Metrics. Fort Belvoir, VA: Defense Technical Information Center, May 2012. http://dx.doi.org/10.21236/ada565825.

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Monev, Veselin. Enterprise IT security metrics: Classification, examples and characteristics. Institute of Information and Communication Technologies, March 2014. http://dx.doi.org/10.11610/it4sec.0111.

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Wayne F. Boyer and Miles A. McQueen. Primer Control System Cyber Security Framework and Technical Metrics. Office of Scientific and Technical Information (OSTI), May 2008. http://dx.doi.org/10.2172/935471.

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Black, Paul E., Michael Kass, and Elizabeth Fong. Proceedings of workshop on software security assurance tools, techniques, and metrics. Gaithersburg, MD: National Institute of Standards and Technology, 2006. http://dx.doi.org/10.6028/nist.sp.500-265.

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Hoaglund, Robert, and Walter Gazda. Assessment of Performance Measures for Security of the Maritime Transportation Network. Port Security Metrics: Proposed Measurement of Deterrence Capability. Fort Belvoir, VA: Defense Technical Information Center, January 2007. http://dx.doi.org/10.21236/ada471403.

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Williams, Laurie. On the Use of Software Metrics as a Predictor of Software Security Problems. Fort Belvoir, VA: Defense Technical Information Center, January 2013. http://dx.doi.org/10.21236/ada581470.

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Black, Paul E., and Elizabeth N. Fong. Report of the Workshop on Software Measures and Metrics to Reduce Security Vulnerabilities (SwMM-RSV). National Institute of Standards and Technology, November 2016. http://dx.doi.org/10.6028/nist.sp.500-320.

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Perdigão, Rui A. P. Information physics and quantum space technologies for natural hazard sensing, modelling and prediction. Meteoceanics, September 2021. http://dx.doi.org/10.46337/210930.

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Анотація:
Disruptive socio-natural transformations and climatic change, where system invariants and symmetries break down, defy the traditional complexity paradigms such as machine learning and artificial intelligence. In order to overcome this, we introduced non-ergodic Information Physics, bringing physical meaning to inferential metrics, and a coevolving flexibility to the metrics of information transfer, resulting in new methods for causal discovery and attribution. With this in hand, we develop novel dynamic models and analysis algorithms natively built for quantum information technological platforms, expediting complex system computations and rigour. Moreover, we introduce novel quantum sensing technologies in our Meteoceanics satellite constellation, providing unprecedented spatiotemporal coverage, resolution and lead, whilst using exclusively sustainable materials and processes across the value chain. Our technologies bring out novel information physical fingerprints of extreme events, with recently proven records in capturing early warning signs for extreme hydro-meteorologic events and seismic events, and do so with unprecedented quantum-grade resolution, robustness, security, speed and fidelity in sensing, processing and communication. Our advances, from Earth to Space, further provide crucial predictive edge and added value to early warning systems of natural hazards and long-term predictions supporting climatic security and action.
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