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2019-08-05
Tao, Y., Lei, Z., Ruxiang, P..  2018.  Fine-Grained Big Data Security Method Based on Zero Trust Model. 2018 IEEE 24th International Conference on Parallel and Distributed Systems (ICPADS). :1040-1045.

With the rapid development of big data technology, the requirement of data processing capacity and efficiency result in failure of a number of legacy security technologies, especially in the data security domain. Data security risks became extremely important for big data usage. We introduced a novel method to preform big data security control, which comprises three steps, namely, user context recognition based on zero trust, fine-grained data access authentication control, and data access audit based on full network traffic to recognize and intercept risky data access in big data environment. Experiments conducted on the fine-grained big data security method based on the zero trust model of drug-related information analysis system demonstrated that this method can identify the majority of data security risks.

Ma, S., Zeng, S., Guo, J..  2018.  Research on Trust Degree Model of Fault Alarms Based on Neural Network. 2018 12th International Conference on Reliability, Maintainability, and Safety (ICRMS). :73-77.

False alarm and miss are two general kinds of alarm errors and they can decrease operator's trust in the alarm system. Specifically, there are two different forms of trust in such systems, represented by two kinds of responses to alarms in this research. One is compliance and the other is reliance. Besides false alarm and miss, the two responses are differentially affected by properties of the alarm system, situational factors or operator factors. However, most of the existing studies have qualitatively analyzed the relationship between a single variable and the two responses. In this research, all available experimental studies are identified through database searches using keyword "compliance and reliance" without restriction on year of publication to December 2017. Six relevant studies and fifty-two sets of key data are obtained as the data base of this research. Furthermore, neural network is adopted as a tool to establish the quantitative relationship between multiple factors and the two forms of trust, respectively. The result will be of great significance to further study the influence of human decision making on the overall fault detection rate and the false alarm rate of the human machine system.

Gerard, B., Rebaï, S. B., Voos, H., Darouach, M..  2018.  Cyber Security and Vulnerability Analysis of Networked Control System Subject to False-Data Injection. 2018 Annual American Control Conference (ACC). :992-997.

In the present paper, the problem of networked control system (NCS) cyber security is considered. The geometric approach is used to evaluate the security and vulnerability level of the controlled system. The proposed results are about the so-called false data injection attacks and show how imperfectly known disturbances can be used to perform undetectable, or at least stealthy, attacks that can make the NCS vulnerable to attacks from malicious outsiders. A numerical example is given to illustrate the approach.

Chavan, N. S., Sharma, D..  2018.  Secure Proof of Retrievability System in Cloud for Data Integrity. 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA). :1-5.

Due to expansion of Internet and huge dataset, many organizations started to use cloud. Cloud Computing moves the application software and databases to the centralized large data centers, where the management of the data and services may not be fully trustworthy. Due to this cloud faces many threats. In this work, we study the problem of ensuring the integrity of data storage in Cloud Computing. To reduce the computational cost at user side during the integrity verification of their data, the notion of public verifiability has been proposed. Our approach is to create a new entity names Cloud Service Controller (CSC) which will help us to reduce the trust on the Third Party Auditor (TPA). We have strengthened the security model by using AES Encryption with SHA-S12 & tag generation. In this paper we get a brief introduction about the file upload phase, integrity of the file & Proof of Retrievability of the file.

Mai, H. L., Nguyen, T., Doyen, G., Cogranne, R., Mallouli, W., Oca, E. M. de, Festor, O..  2018.  Towards a security monitoring plane for named data networking and its application against content poisoning attack. NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium. :1–9.

Named Data Networking (NDN) is the most mature proposal of the Information Centric Networking paradigm, a clean-slate approach for the Future Internet. Although NDN was designed to tackle security issues inherent to IP networks natively, newly introduced security attacks in its transitional phase threaten NDN's practical deployment. Therefore, a security monitoring plane for NDN is indispensable before any potential deployment of this novel architecture in an operating context by any provider. We propose an approach for the monitoring and anomaly detection in NDN nodes leveraging Bayesian Network techniques. A list of monitored metrics is introduced as a quantitative measure to feature the behavior of an NDN node. By leveraging the hypothesis testing theory, a micro detector is developed to detect whenever the metric significantly changes from its normal behavior. A Bayesian network structure that correlates alarms from micro detectors is designed based on the expert knowledge of the NDN specification and the NFD implementation. The relevance and performance of our security monitoring approach are demonstrated by considering the Content Poisoning Attack (CPA), one of the most critical attacks in NDN, through numerous experiment data collected from a real NDN deployment.

Xia, S., Li, N., Xiaofeng, T., Fang, C..  2018.  Multiple Attributes Based Spoofing Detection Using an Improved Clustering Algorithm in Mobile Edge Network. 2018 1st IEEE International Conference on Hot Information-Centric Networking (HotICN). :242–243.

Information centric network (ICN) based Mobile Edge Computing (MEC) network has drawn growing attentions in recent years. The distributed network architecture brings new security problems, especially the identity security problem. Because of the cloud platform deployed on the edge of the MEC network, multiple channel attributes can be easily obtained and processed. Thus this paper proposes a multiple channel attributes based spoofing detection mechanism. To further reduce the complexity, we also propose an improved clustering algorithm. The simulation results indicate that the proposed spoofing detection method can provide near-optimal performance with extremely low complexity.

Ogundokun, A., Zavarsky, P., Swar, B..  2018.  Cybersecurity assurance control baselining for smart grid communication systems. 2018 14th IEEE International Workshop on Factory Communication Systems (WFCS). :1–6.

Cybersecurity assurance plays an important role in managing trust in smart grid communication systems. In this paper, cybersecurity assurance controls for smart grid communication networks and devices are delineated from the more technical functional controls to provide insights on recent innovative risk-based approaches to cybersecurity assurance in smart grid systems. The cybersecurity assurance control baselining presented in this paper is based on requirements and guidelines of the new family of IEC 62443 standards on network and systems security of industrial automation and control systems. The paper illustrates how key cybersecurity control baselining and tailoring concepts of the U.S. NIST SP 800-53 can be adopted in smart grid security architecture. The paper outlines the application of IEC 62443 standards-based security zoning and assignment of security levels to the zones in smart grid system architectures. To manage trust in the smart grid system architecture, cybersecurity assurance base lining concepts are applied per security impact levels. Selection and justification of security assurance controls presented in the paper is utilizing the approach common in Security Technical Implementation Guides (STIGs) of the U.S. Defense Information Systems Agency. As shown in the paper, enhanced granularity for managing trust both on the overall system and subsystem levels of smart grid systems can be achieved by implementation of the instructions of the CNSSI 1253 of the U.S. Committee of National Security Systems on security categorization and control selection for national security systems.

Headrick, W. J., Dlugosz, A., Rajcok, P..  2018.  Information Assurance in modern ATE. 2018 IEEE AUTOTESTCON. :1–4.

For modern Automatic Test Equipment (ATE) one of the most daunting tasks is now Information Assurance (IA). What was once at most a secondary item consisting mainly of installing an Anti-Virus suite is now becoming one of the most important aspects of ATE. Given the current climate of IA it has become important to ensure ATE is kept safe from any breaches of security or loss of information. Even though most ATE are not on the Internet (or even on a network for many) they are still vulnerable to some of the same attack vectors plaguing common computers and other electronic devices. This paper will discuss some of the processes and procedures which must be used to ensure that modern ATE can continue to be used to test and detect faults in the systems they are designed to test. The common items that must be considered for ATE are as follows: The ATE system must have some form of Anti-Virus (as should all computers). The ATE system should have a minimum software footprint only providing the software needed to perform the task. The ATE system should be verified to have all the Operating System (OS) settings configured pursuant to the task it is intended to perform. The ATE OS settings should include password and password expiration settings to prevent access by anyone not expected to be on the system. The ATE system software should be written and constructed such that it in itself is not readily open to attack. The ATE system should be designed in a manner such that none of the instruments in the system can easily be attacked. The ATE system should insure any paths to the outside world (such as Ethernet or USB devices) are limited to only those required to perform the task it was designed for. These and many other common configuration concerns will be discussed in the paper.

2019-07-01
Clemente, C. J., Jaafar, F., Malik, Y..  2018.  Is Predicting Software Security Bugs Using Deep Learning Better Than the Traditional Machine Learning Algorithms? 2018 IEEE International Conference on Software Quality, Reliability and Security (QRS). :95–102.

Software insecurity is being identified as one of the leading causes of security breaches. In this paper, we revisited one of the strategies in solving software insecurity, which is the use of software quality metrics. We utilized a multilayer deep feedforward network in examining whether there is a combination of metrics that can predict the appearance of security-related bugs. We also applied the traditional machine learning algorithms such as decision tree, random forest, naïve bayes, and support vector machines and compared the results with that of the Deep Learning technique. The results have successfully demonstrated that it was possible to develop an effective predictive model to forecast software insecurity based on the software metrics and using Deep Learning. All the models generated have shown an accuracy of more than sixty percent with Deep Learning leading the list. This finding proved that utilizing Deep Learning methods and a combination of software metrics can be tapped to create a better forecasting model thereby aiding software developers in predicting security bugs.

Medeiros, N., Ivaki, N., Costa, P., Vieira, M..  2018.  An Approach for Trustworthiness Benchmarking Using Software Metrics. 2018 IEEE 23rd Pacific Rim International Symposium on Dependable Computing (PRDC). :84–93.

Trustworthiness is a paramount concern for users and customers in the selection of a software solution, specially in the context of complex and dynamic environments, such as Cloud and IoT. However, assessing and benchmarking trustworthiness (worthiness of software for being trusted) is a challenging task, mainly due to the variety of application scenarios (e.g., businesscritical, safety-critical), the large number of determinative quality attributes (e.g., security, performance), and last, but foremost, due to the subjective notion of trust and trustworthiness. In this paper, we present trustworthiness as a measurable notion in relative terms based on security attributes and propose an approach for the assessment and benchmarking of software. The main goal is to build a trustworthiness assessment model based on software metrics (e.g., Cyclomatic Complexity, CountLine, CBO) that can be used as indicators of software security. To demonstrate the proposed approach, we assessed and ranked several files and functions of the Mozilla Firefox project based on their trustworthiness score and conducted a survey among several software security experts in order to validate the obtained rank. Results show that our approach is able to provide a sound ranking of the benchmarked software.

Zieger, A., Freiling, F., Kossakowski, K..  2018.  The β-Time-to-Compromise Metric for Practical Cyber Security Risk Estimation. 2018 11th International Conference on IT Security Incident Management IT Forensics (IMF). :115–133.
To manage cybersecurity risks in practice, a simple yet effective method to assess suchs risks for individual systems is needed. With time-to-compromise (TTC), McQueen et al. (2005) introduced such a metric that measures the expected time that a system remains uncompromised given a specific threat landscape. Unlike other approaches that require complex system modeling to proceed, TTC combines simplicity with expressiveness and therefore has evolved into one of the most successful cybersecurity metrics in practice. We revisit TTC and identify several mathematical and methodological shortcomings which we address by embedding all aspects of the metric into the continuous domain and the possibility to incorporate information about vulnerability characteristics and other cyber threat intelligence into the model. We propose β-TTC, a formal extension of TTC which includes information from CVSS vectors as well as a continuous attacker skill based on a β-distribution. We show that our new metric (1) remains simple enough for practical use and (2) gives more realistic predictions than the original TTC by using data from a modern and productively used vulnerability database of a national CERT.
Arabsorkhi, A., Ghaffari, F..  2018.  Security Metrics: Principles and Security Assessment Methods. 2018 9th International Symposium on Telecommunications (IST). :305–310.
Nowadays, Information Technology is one of the important parts of human life and also of organizations. Organizations face problems such as IT problems. To solve these problems, they have to improve their security sections. Thus there is a need for security assessments within organizations to ensure security conditions. The use of security standards and general metric can be useful for measuring the safety of an organization; however, it should be noted that the general metric which are applied to businesses in general cannot be effective in this particular situation. Thus it's important to select metric standards for different businesses to improve both cost and organizational security. The selection of suitable security measures lies in the use of an efficient way to identify them. Due to the numerous complexities of these metric and the extent to which they are defined, in this paper that is based on comparative study and the benchmarking method, taxonomy for security measures is considered to be helpful for a business to choose metric tailored to their needs and conditions.
Urias, V. E., Stout, M. S. William, Leeuwen, B. V..  2018.  On the Feasibility of Generating Deception Environments for Industrial Control Systems. 2018 IEEE International Symposium on Technologies for Homeland Security (HST). :1–6.

The cyber threat landscape is a constantly morphing surface; the need for cyber defenders to develop and create proactive threat intelligence is on the rise, especially on critical infrastructure environments. It is commonly voiced that Supervisory Control and Data Acquisition (SCADA) systems and Industrial Control Systems (ICS) are vulnerable to the same classes of threats as other networked computer systems. However, cyber defense in operational ICS is difficult, often introducing unacceptable risks of disruption to critical physical processes. This is exacerbated by the notion that hardware used in ICS is often expensive, making full-scale mock-up systems for testing and/or cyber defense impractical. New paradigms in cyber security have focused heavily on using deception to not only protect assets, but also gather insight into adversary motives and tools. Much of the work that we see in today's literature is focused on creating deception environments for traditional IT enterprise networks; however, leveraging our prior work in the domain, we explore the opportunities, challenges and feasibility of doing deception in ICS networks.

Perez, R. Lopez, Adamsky, F., Soua, R., Engel, T..  2018.  Machine Learning for Reliable Network Attack Detection in SCADA Systems. 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). :633–638.

Critical Infrastructures (CIs) use Supervisory Control And Data Acquisition (SCADA) systems for remote control and monitoring. Sophisticated security measures are needed to address malicious intrusions, which are steadily increasing in number and variety due to the massive spread of connectivity and standardisation of open SCADA protocols. Traditional Intrusion Detection Systems (IDSs) cannot detect attacks that are not already present in their databases. Therefore, in this paper, we assess Machine Learning (ML) for intrusion detection in SCADA systems using a real data set collected from a gas pipeline system and provided by the Mississippi State University (MSU). The contribution of this paper is two-fold: 1) The evaluation of four techniques for missing data estimation and two techniques for data normalization, 2) The performances of Support Vector Machine (SVM), and Random Forest (RF) are assessed in terms of accuracy, precision, recall and F1score for intrusion detection. Two cases are differentiated: binary and categorical classifications. Our experiments reveal that RF detect intrusions effectively, with an F1score of respectively \textbackslashtextgreater 99%.

Zabetian-Hosseini, A., Mehrizi-Sani, A., Liu, C..  2018.  Cyberattack to Cyber-Physical Model of Wind Farm SCADA. IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society. :4929–4934.

In recent years, there has been a significant increase in wind power penetration into the power system. As a result, the behavior of the power system has become more dependent on wind power behavior. Supervisory control and data acquisition (SCADA) systems responsible for monitoring and controlling wind farms often have vulnerabilities that make them susceptible to cyberattacks. These vulnerabilities allow attackers to exploit and intrude in the wind farm SCADA system. In this paper, a cyber-physical system (CPS) model for the information and communication technology (ICT) model of the wind farm SCADA system integrated with SCADA of the power system is proposed. Cybersecurity of this wind farm SCADA system is discussed. Proposed cyberattack scenarios on the system are modeled and the impact of these cyberattacks on the behavior of the power systems on the IEEE 9-bus modified system is investigated. Finally, an anomaly attack detection algorithm is proposed to stop the attack of tripping of all wind farms. Case studies validate the performance of the proposed CPS model of the test system and the attack detection algorithm.

Kolosok, I., Korkina, E., Mahnitko, A., Gavrilovs, A..  2018.  Supporting Cyber-Physical Security of Electric Power System by the State Estimation Technique. 2018 IEEE 59th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON). :1–6.

Security is one of the most important properties of electric power system (EPS). We consider the state estimation (SE) tool as a barrier to the corruption of data on current operating conditions of the EPS. An algorithm for a two-level SE on the basis of SCADA and WAMS measurements is effective in terms of detection of malicious attacks on energy system. The article suggests a methodology to identify cyberattacks on SCADA and WAMS.

Kumar, S., Gaur, N., Kumar, A..  2018.  Developing a Secure Cyber Ecosystem for SCADA Architecture. 2018 Second International Conference on Computing Methodologies and Communication (ICCMC). :559–562.

Advent of Cyber has converted the entire World into a Global village. But, due to vurneabilites in SCADA architecture [1] national assests are more prone to cyber attacks.. Cyber invasions have a catastrophic effect in the minds of the civilian population, in terms of states security system. A robust cyber security is need of the hour to protect the critical information infastructrue & critical infrastructure of a country. Here, in this paper we scrutinize cyber terrorism, vurneabilites in SCADA network systems [1], [2] and concept of cyber resilience to combat cyber attacks.

Amjad, N., Afzal, H., Amjad, M. F., Khan, F. A..  2018.  A Multi-Classifier Framework for Open Source Malware Forensics. 2018 IEEE 27th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE). :106-111.

Traditional anti-virus technologies have failed to keep pace with proliferation of malware due to slow process of their signatures and heuristics updates. Similarly, there are limitations of time and resources in order to perform manual analysis on each malware. There is a need to learn from this vast quantity of data, containing cyber attack pattern, in an automated manner to proactively adapt to ever-evolving threats. Machine learning offers unique advantages to learn from past cyber attacks to handle future cyber threats. The purpose of this research is to propose a framework for multi-classification of malware into well-known categories by applying different machine learning models over corpus of malware analysis reports. These reports are generated through an open source malware sandbox in an automated manner. We applied extensive pre-modeling techniques for data cleaning, features exploration and features engineering to prepare training and test datasets. Best possible hyper-parameters are selected to build machine learning models. These prepared datasets are then used to train the machine learning classifiers and to compare their prediction accuracy. Finally, these results are validated through a comprehensive 10-fold cross-validation methodology. The best results are achieved through Gaussian Naive Bayes classifier with random accuracy of 96% and 10-Fold Cross Validation accuracy of 91.2%. The said framework can be deployed in an operational environment to learn from malware attacks for proactively adapting matching counter measures.

Rosa, F. De Franco, Jino, M., Bueno, P. Marcos Siqueira, Bonacin, R..  2018.  Coverage-Based Heuristics for Selecting Assessment Items from Security Standards: A Core Set Proposal. 2018 Workshop on Metrology for Industry 4.0 and IoT. :192-197.

In the realm of Internet of Things (IoT), information security is a critical issue. Security standards, including their assessment items, are essential instruments in the evaluation of systems security. However, a key question remains open: ``Which test cases are most effective for security assessment?'' To create security assessment designs with suitable assessment items, we need to know the security properties and assessment dimensions covered by a standard. We propose an approach for selecting and analyzing security assessment items; its foundations come from a set of assessment heuristics and it aims to increase the coverage of assessment dimensions and security characteristics in assessment designs. The main contribution of this paper is the definition of a core set of security assessment heuristics. We systematize the security assessment process by means of a conceptual formalization of the security assessment area. Our approach can be applied to security standards to select or to prioritize assessment items with respect to 11 security properties and 6 assessment dimensions. The approach is flexible allowing the inclusion of dimensions and properties. Our proposal was applied to a well know security standard (ISO/IEC 27001) and its assessment items were analyzed. The proposal is meant to support: (i) the generation of high-coverage assessment designs, which include security assessment items with assured coverage of the main security characteristics, and (ii) evaluation of security standards with respect to the coverage of security aspects.

Li, D., Zhang, Z., Liao, W., Xu, Z..  2018.  KLRA: A Kernel Level Resource Auditing Tool For IoT Operating System Security. 2018 IEEE/ACM Symposium on Edge Computing (SEC). :427-432.

Nowadays, the rapid development of the Internet of Things facilitates human life and work, while it also brings great security risks to the society due to the frequent occurrence of various security issues. IoT device has the characteristics of large-scale deployment and single responsibility application, which makes it easy to cause a chain reaction and results in widespread privacy leakage and system security problems when the software vulnerability is identified. It is difficult to guarantee that there is no security hole in the IoT operating system which is usually designed for MCU and has no kernel mode. An alternative solution is to identify the security issues in the first time when the system is hijacked and suspend the suspicious task before it causes irreparable damage. This paper proposes KLRA (A Kernel Level Resource Auditing Tool) for IoT Operating System Security This tool collects the resource-sensitive events in the kernel and audit the the resource consumption pattern of the system at the same time. KLRA can take fine-grained events measure with low cost and report the relevant security warning in the first time when the behavior of the system is abnormal compared with daily operations for the real responsibility of this device. KLRA enables the IoT operating system for MCU to generate the security early warning and thereby provides a self-adaptive heuristic security mechanism for the entire IoT system.

Modi, F. M., Desai, M. R., Soni, D. R..  2018.  A Third Party Audit Mechanism for Cloud Based Storage Using File Versioning and Change Tracking Mechanism. 2018 International Conference on Inventive Research in Computing Applications (ICIRCA). :521-523.

Cloud storage is an exclusive resource in cloud computing, which helps to store and share the data on cloud storage server. Clients upload the data and its hash information n server together on cloud storage. The file owner always concern about data security like privacy and unauthorized access to third party. The owner also wants to ensure the integrity data during communication process. To ensure integrity, we propose a framework based on third party auditor which checks the integrity and correctness of data during audit process. Our aim is to design custom hash for the file which is not only justifies the integrity but also version information about file.

Ha\c silo\u glu, A., Bali, A..  2018.  Central Audit Logging Mechanism in Personal Data Web Services. 2018 6th International Symposium on Digital Forensic and Security (ISDFS). :1-3.

Personal data have been compiled and harnessed by a great number of establishments to execute their legal activities. Establishments are legally bound to maintain the confidentiality and security of personal data. Hence it is a requirement to provide access logs for the personal information. Depending on the needs and capacity, personal data can be opened to the users via platforms such as file system, database and web service. Web service platform is a popular alternative since it is autonomous and can isolate the data source from the user. In this paper, the way to log personal data accessed via web service method has been discussed. As an alternative to classical method in which logs were recorded and saved by client applications, a different mechanism of forming a central audit log with API manager has been investigated. By forging a model policy to exemplify central logging method, its advantages and disadvantages have been explored. It has been concluded in the end that this model could be employed in centrally recording audit logs.

2019-06-28
Kulik, T., Tran-Jørgensen, P. W. V., Boudjadar, J., Schultz, C..  2018.  A Framework for Threat-Driven Cyber Security Verification of IoT Systems. 2018 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW). :89-97.

Industrial control systems are changing from monolithic to distributed and interconnected architectures, entering the era of industrial IoT. One fundamental issue is that security properties of such distributed control systems are typically only verified empirically, during development and after system deployment. We propose a novel modelling framework for the security verification of distributed industrial control systems, with the goal of moving towards early design stage formal verification. In our framework we model industrial IoT infrastructures, attack patterns, and mitigation strategies for countering attacks. We conduct model checking-based formal analysis of system security through scenario execution, where the analysed system is exposed to attacks and implement mitigation strategies. We study the applicability of our framework for large systems using a scalability analysis.

2019-06-24
Mavroeidis, V., Vishi, K., Jøsang, A..  2018.  A Framework for Data-Driven Physical Security and Insider Threat Detection. 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). :1108–1115.

This paper presents PSO, an ontological framework and a methodology for improving physical security and insider threat detection. PSO can facilitate forensic data analysis and proactively mitigate insider threats by leveraging rule-based anomaly detection. In all too many cases, rule-based anomaly detection can detect employee deviations from organizational security policies. In addition, PSO can be considered a security provenance solution because of its ability to fully reconstruct attack patterns. Provenance graphs can be further analyzed to identify deceptive actions and overcome analytical mistakes that can result in bad decision-making, such as false attribution. Moreover, the information can be used to enrich the available intelligence (about intrusion attempts) that can form use cases to detect and remediate limitations in the system, such as loosely-coupled provenance graphs that in many cases indicate weaknesses in the physical security architecture. Ultimately, validation of the framework through use cases demonstrates and proves that PS0 can improve an organization's security posture in terms of physical security and insider threat detection.

Izurieta, C., Kimball, K., Rice, D., Valentien, T..  2018.  A Position Study to Investigate Technical Debt Associated with Security Weaknesses. 2018 IEEE/ACM International Conference on Technical Debt (TechDebt). :138–142.

Context: Managing technical debt (TD) associated with potential security breaches found during design can lead to catching vulnerabilities (i.e., exploitable weaknesses) earlier in the software lifecycle; thus, anticipating TD principal and interest that can have decidedly negative impacts on businesses. Goal: To establish an approach to help assess TD associated with security weaknesses by leveraging the Common Weakness Enumeration (CWE) and its scoring mechanism, the Common Weakness Scoring System (CWSS). Method: We present a position study with a five-step approach employing the Quamoco quality model to operationalize the scoring of architectural CWEs. Results: We use static analysis to detect design level CWEs, calculate their CWSS scores, and provide a relative ranking of weaknesses that help practitioners identify the highest risks in an organization with a potential to impact TD. Conclusion: CWSS is a community agreed upon method that should be leveraged to help inform the ranking of security related TD items.