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

Using Capec Attack Patterns For Developing Abuse Cases

To engineer secure software, it is imperative to understand attackers’ perspectives and approaches. This information has been captured by attack patterns. The Common Attack Patterns Enumeration Classification (CAPEC) repository hosts over 450 attack patterns that contain information about how attacks have been launched against software. Researches have indicated that attack patterns can be utilized for developing secure software; however, there exists no systematic methodology to address this concern. This research proposes a methodology for utilizing CAPEC attack patterns for developing abuse cases at the requirements stage of the secure software development lifecycle (SDLC). In previous research, a tool for retrieving attack patterns (TrAP) was developed to retrieve CAPEC attack patterns according to Microsoft STRIDE threat categories. This tool also features a search function using keywords. The proposed methodology starts with a set of initial abuse cases developed through brainstorming. Microsoft SDL threat modelling tool is then used to identify and rank possible security threats in the system. The SDL tool generates a series of questions for each threat and these questions are used to extract keywords that serve as input to the TrAP tool to retrieve attack patterns relevant to the abuse cases. Keywords can also be system prerequisites or any technology being implemented in the system. From the list of retrieved attack patterns, the most relevant attack patterns are selected and used to extend the initial abuse cases. New abuse cases can also be discovered through this process

Research on Improving the Security Protection Capability of Critical Information Infrastructure based on Knowledge Graph

Critical information infrastructure is an important information system to ensure national social stability and normal economic operation, and it is also the main target of cyber-attacks. This paper proposes to build a threat-management knowledge graph, integrate security management requirement data and cyber threat intelligence data, and associate two types of data by link prediction algorithm, to realize the knowledge reasoning ability of threat management knowledge graph. The research shows that the threat-management knowledge graph can be used to deduce the security events of critical information infrastructure, find the corresponding security management requirements, and eff ectively improve the security ability of critical information infrastructure

Assessing The Security Posture Of Openemr Using Capec Attack Patterns

Attack patterns describe the common methods of exploiting software. Good software engineering practices and principles alone are not enough to produce secure software. It is also important to know how software it attacked and to guard against it. Knowledge of attack patterns provides a good perspective of an attacker, thus enabling developers and testers to build secure software. CAPEC list is a taxonomy of attack patterns which we believe can enhance security testing. This research seeks to assess the security posture of OpenEMR 4.1.1, an open source Electronic Medical Record (EMR) system, based on CAPEC attack patterns. Five categories of CAPEC attack patterns were analyzed to find their relevance and applicability to OpenEMR. Whereas inapplicable attack patterns were not further considered, applicable attack patterns were further tested to assess OpenEMR vulnerability to them. Various security testing tools were used to carry out the tests. Attack patterns helped to focus black-box and white-box testing procedures on what and where to test. OpenEMR was found to be vulnerable to a number of vulnerabilities such as cross site scripting, authentication bypass, session sidejacking, among others. A number of exploitations were carried out based on the vulnerabilities discovered

AIDIS: Detecting and Classifying Anomalous Behavior in UbiquitousKernel Processes

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Targeted attacks on IT systems are a rising threat against the confidentiality, integrity, and availability of critical information and infrastructures. With the rising prominence of advanced persistent threats (APTs), identifying and under-standing such attacks has become increasingly important. Current signature-based systems are heavily reliant on fixed patterns that struggle with unknown or evasive applications, while behavior-based solutions usually leave most of the interpretative work to a human analyst.In this article we propose AIDIS, an Advanced Intrusion Detection and Interpretation System capable to explain anomalous behavior within a network-enabled user session by considering kernel event anomalies identified through their deviation from a set of baseline process graphs. For this purpose we adapt star-structures, a bipartite representation used to approximate the edit distance be-tween two graphs. Baseline templates are generated automatically and adapt to the nature of the respective operating system process.We prototypically implemented smart anomaly classification through a set of competency questions applied to graph template deviations and evaluated the approach using both Random Forest and linear kernel support vector machines.The determined attack classes are ultimately mapped to a dedicated APT at-tacker/defender meta model that considers actions, actors, as well as assets and mitigating controls, thereby enabling decision support and contextual interpretation of ongoing attack