Vulnerability anti-patterns:a timeless way to capture poor software practices (Vulnerabilities)

There is a distinct communication gap between the software engineering and cybersecurity communities when it comes to addressing reoccurring security problems, known as vulnerabilities. Many vulnerabilities are caused by software errors that are created by software developers. Insecure software development practices are common due to a variety of factors, which include inefficiencies within existing knowledge transfer mechanisms based on vulnerability databases (VDBs), software developers perceiving security as an afterthought, and lack of consideration of security as part of the software development lifecycle (SDLC). The resulting communication gap also prevents developers and security experts from successfully sharing essential security knowledge. The cybersecurity community makes their expert knowledge available in forms including vulnerability databases such as CAPEC and CWE, and pattern catalogues such as Security Patterns, Attack Patterns, and Software Fault Patterns. However, these sources are not effective at providing software developers with an understanding of how malicious hackers can exploit vulnerabilities in the software systems they create. As developers are familiar with pattern-based approaches, this paper proposes the use of Vulnerability Anti-Patterns (VAP) to transfer usable vulnerability knowledge to developers, bridging the communication gap between security experts and software developers. The primary contribution of this paper is twofold: (1) it proposes a new pattern template – Vulnerability Anti-Pattern – that uses anti-patterns rather than patterns to capture and communicate knowledge of existing vulnerabilities, and (2) it proposes a catalogue of Vulnerability Anti-Patterns (VAP) based on the most commonly occurring vulnerabilities that software developers can use to learn how malicious hackers can exploit errors in software

Idea-caution before exploitation:the use of cybersecurity domain knowledge to educate software engineers against software vulnerabilities

The transfer of cybersecurity domain knowledge from security experts (‘Ethical Hackers’) to software engineers is discussed in terms of desirability and feasibility. Possible mechanisms for the transfer are critically examined. Software engineering methodologies do not make use of security domain knowledge in its form of vulnerability databases (e.g. CWE, CVE, Exploit DB), which are therefore not appropriate for this purpose. An approach based upon the improved use of pattern languages that encompasses security domain knowledge is proposed

Design of risk assessment methodology for IT/OT systems : Employment of online security catalogues in the risk assessment process

The revolution brought about with the transition from Industry 1.0 to 4.0 has expanded the cyber threats from Information Technology (IT) to Operational Technology (OT) systems. However, unlike IT systems, identifying the relevant threats in OT is more complex as penetration testing applications highly restrict OT availability. The complexity is enhanced by the significant amount of information available in online security catalogues, like Common Weakness Enumeration, Common Vulnerabilities and Exposures and Common Attack Pattern Enumeration and Classification, and the incomplete organisation of their relationships. These issues hinder the identification of relevant threats during risk assessment of OT systems. In this thesis, a methodology is proposed to reduce the aforementioned complexities and improve relationships among online security catalogues to identify the cybersecurity risk of IT/OT systems. The weaknesses, vulnerabilities and attack patterns stored in the online catalogues are extracted and categorised by mapping their potential mitigations to their security requirements, which are introduced on security standards that the system should comply with, like the ISA/IEC 62443. The system's assets are connected to the potential threats through the security requirements, which, combined with the relationships established among the catalogues, offer the basis for graphical representation of the results by employing tree-shaped graphical models. The methodology is tested on the components of an Information and Communication Technology system, whose results verify the simplification of the threat identification process but highlight the need for an in-depth understanding of the system. Hence, the methodology offers a significant basis on which further work can be applied to standardise the risk assessment process of IT/OT systems

Facilitating the Integrative Use of Security Knowledge Bases within a Modelling Environment

Security threat and risk assessment of systems requires the integrated use of information from multiple knowledge bases. Such use is typically carried out ad-hoc by security experts in an unstructured manner. Also, this ad-hoc use of information often lacks foundations that allow for rigorous, disciplined applications of policy enforcement and the establishment of a well-integrated body of knowledge. This hinders organisational learning as well as the maturation of the threat modelling discipline. In this article, we uncover a newly developed extension of a state-of-the-art modelling tool that allows users to integrate and curate security-related information from multiple knowledge bases. Specifically, we provide catalogues of threats and security controls based on information from CAPEC, ATT&CK, and NIST SP800-53. We demonstrate the ability to curate security information using the designed solution. We highlight the contribution to improving the communication of security information, including the systematic mapping between user-defined security guidance and information derived from knowledge bases. The solution is open source and relies on model-to-model transformations and extendable threat and security control catalogues. Accordingly, the solution allows prospective users to adapt the modelling environment to their needs as well as keep it current with respect to evolving knowledge bases

Matching Possible Mitigations to Cyber Threats: A Document-Driven Decision Support Systems Approach

Cyber systems are ubiquitous in all aspects of society. At the same time, breaches to cyber systems continue to be front-page news (Calfas, 2018; Equifax, 2017) and, despite more than a decade of heightened focus on cybersecurity, the threat continues to evolve and grow, costing globally up to $575 billion annually (Center for Strategic and International Studies, 2014; Gosler & Von Thaer, 2013; Microsoft, 2016; Verizon, 2017). To address possible impacts due to cyber threats, information system (IS) stakeholders must assess the risks they face. Following a risk assessment, the next step is to determine mitigations to counter the threats that pose unacceptably high risks. The literature contains a robust collection of studies on optimizing mitigation selections, but they universally assume that the starting list of appropriate mitigations for specific threats exists from which to down-select. In current practice, producing this starting list is largely a manual process and it is challenging because it requires detailed cybersecurity knowledge from highly decentralized sources, is often deeply technical in nature, and is primarily described in textual form, leading to dependence on human experts to interpret the knowledge for each specific context. At the same time cybersecurity experts remain in short supply relative to the demand, while the delta between supply and demand continues to grow (Center for Cyber Safety and Education, 2017; Kauflin, 2017; Libicki, Senty, & Pollak, 2014). Thus, an approach is needed to help cybersecurity experts (CSE) cut through the volume of available mitigations to select those which are potentially viable to offset specific threats. This dissertation explores the application of machine learning and text retrieval techniques to automate matching of relevant mitigations to cyber threats, where both are expressed as unstructured or semi-structured English language text. Using the Design Science Research Methodology (Hevner & March, 2004; Peffers, Tuunanen, Rothenberger, & Chatterjee, 2007), we consider a number of possible designs for the matcher, ultimately selecting a supervised machine learning approach that combines two techniques: support vector machine classification and latent semantic analysis. The selected approach demonstrates high recall for mitigation documents in the relevant class, bolstering confidence that potentially viable mitigations will not be overlooked. It also has a strong ability to discern documents in the non-relevant class, allowing approximately 97% of non-relevant mitigations to be excluded automatically, greatly reducing the CSE’s workload over purely manual matching. A false v positive rate of up to 3% prevents totally automated mitigation selection and requires the CSE to reject a few false positives. This research contributes to theory a method for automatically mapping mitigations to threats when both are expressed as English language text documents. This artifact represents a novel machine learning approach to threat-mitigation mapping. The research also contributes an instantiation of the artifact for demonstration and evaluation. From a practical perspective the artifact benefits all threat-informed cyber risk assessment approaches, whether formal or ad hoc, by aiding decision-making for cybersecurity experts whose job it is to mitigate the identified cyber threats. In addition, an automated approach makes mitigation selection more repeatable, facilitates knowledge reuse, extends the reach of cybersecurity experts, and is extensible to accommodate the continued evolution of both cyber threats and mitigations. Moreover, the selection of mitigations applicable to each threat can serve as inputs into multifactor analyses of alternatives, both automated and manual, thereby bridging the gap between cyber risk assessment and final mitigation selection

A Novel Model for Vulnerability Analysis through Enhanced Directed Graphs and Quantitative Metrics

The rapid evolution of industrial components, the paradigm of Industry 4.0, and the new connectivity features introduced by 5G technology all increase the likelihood of cybersecurity incidents. Such incidents are caused by the vulnerabilities present in these components. Designing a secure system is critical, but it is also complex, costly, and an extra factor to manage during the lifespan of the component. This paper presents a model to analyze the known vulnerabilities of industrial components over time. The proposed Extended Dependency Graph (EDG) model is based on two main elements: a directed graph representation of the internal structure of the component, and a set of quantitative metrics based on the Common Vulnerability Scoring System (CVSS). The EDG model can be applied throughout the entire lifespan of a device to track vulnerabilities, identify new requirements, root causes, and test cases. It also helps prioritize patching activities. The model was validated by application to the OpenPLC project. The results reveal that most of the vulnerabilities associated with OpenPLC were related to memory buffer operations and were concentrated in the libssl library. The model was able to determine new requirements and generate test cases from the analysis

A Relevance Model for Threat-Centric Ranking of Cybersecurity Vulnerabilities

The relentless and often haphazard process of tracking and remediating vulnerabilities is a top concern for cybersecurity professionals. The key challenge they face is trying to identify a remediation scheme specific to in-house, organizational objectives. Without a strategy, the result is a patchwork of fixes applied to a tide of vulnerabilities, any one of which could be the single point of failure in an otherwise formidable defense. This means one of the biggest challenges in vulnerability management relates to prioritization. Given that so few vulnerabilities are a focus of real-world attacks, a practical remediation strategy is to identify vulnerabilities likely to be exploited and focus efforts towards remediating those vulnerabilities first. The goal of this research is to demonstrate that aggregating and synthesizing readily accessible, public data sources to provide personalized, automated recommendations that an organization can use to prioritize its vulnerability management strategy will offer significant improvements over what is currently realized using the Common Vulnerability Scoring System (CVSS). We provide a framework for vulnerability management specifically focused on mitigating threats using adversary criteria derived from MITRE ATT&CK. We identify the data mining steps needed to acquire, standardize, and integrate publicly available cyber intelligence data sets into a robust knowledge graph from which stakeholders can infer business logic related to known threats. We tested our approach by identifying vulnerabilities in academic and common software associated with six universities and four government facilities. Ranking policy performance was measured using the Normalized Discounted Cumulative Gain (nDCG). Our results show an average 71.5% to 91.3% improvement towards the identification of vulnerabilities likely to be targeted and exploited by cyber threat actors. The ROI of patching using our policies resulted in a savings in the range of 23.3% to 25.5% in annualized unit costs. Our results demonstrate the efficiency of creating knowledge graphs to link large data sets to facilitate semantic queries and create data-driven, flexible ranking policies. Additionally, our framework uses only open standards, making implementation and improvement feasible for cyber practitioners and academia

Attack Modeling and Mitigation Strategies for Risk-Based Analysis of Networked Medical Devices

The escalating integration of network-enabled medical devices raises concerns for both practitioners and academics in terms of introducing new vulnerabilities and attack vectors. This prompts the idea that combining medical device data, security vulnerability enumerations, and attack-modeling data into a single database could enable security analysts to proactively identify potential security weaknesses in medical devices and formulate appropriate mitigation and remediation plans. This study introduces a novel extension to a relational database risk assessment framework by using the open-source tool OVAL to capture device states and compare them to security advisories that warn of threats and vulnerabilities, and where threats and vulnerabilities exist provide mitigation recommendations. The contribution of this research is a proof of concept evaluation that demonstrates the integration of OVAL and CAPEC attack patterns for analysis using a database-driven risk assessment framework