Review of human decision-making during computer security incident analysis

We review practical advice on decision-making during computer security incident response. Scope includes standards from the IETF, ISO, FIRST, and the US intelligence community. To focus on human decision-making, the scope is the evidence collection, analysis, and reporting phases of response. The results indicate both strengths and gaps. A strength is available advice on how to accomplish many specific tasks. However, there is little guidance on how to prioritize tasks in limited time or how to interpret, generalize, and convincingly report results. Future work should focus on these gaps in explication and specification of decision-making during incident analysis

Maritime Cyber Security Incident Data Reporting for Autonomous Ships

The main research objective of this thesis was to find a suitable data model to be used for incident reporting purposes in the use case of autonomous shipping. To reach this objective, some research into the maritime industry, autonomous shipping, and incident management and reporting was needed. Research into these topics was conducted via a literature review. After these topics were investigated, some current incident data modeling and sharing methods were researched. Out of these IODEF seemed like the most suitable one for our use case, so it was chosen for further inspection. The IODEF specification was looked into more closely and a conclusion was ultimately made that the IODEF data model is suitable for reporting incident data from autonomous ships to the shore control center. However, the model was still missing some key information needed for this use case, so an extension for the data model was designed. The data model and extension were then put to test via different use scenarios to test applicability for the needs of autonomous shipping. From these use scenarios it was inferred that the model is applicable for the many different incident data reporting needs of autonomous shipping. Further analysis and testing was then conducted, including a transport test over cellular and satellite connections. The test and analysis further validated the use of the data model. All in all, the research was a success and a good data model was found for reporting incidents from autonomous ships. The work with the data model will continue further outside this thesis

Key Requirements for the Detection and Sharing of Behavioral Indicators of Compromise

[EN] Cyber threat intelligence feeds the focus on atomic and computed indicators of compromise. These indicators are the main source of tactical cyber intelligence most organizations benefit from. They are expressed in machine-readable formats, and they are easily loaded into security devices in order to protect infrastructures. However, their usefulness is very limited, specially in terms of time of life. These indicators can be useful when dealing with non-advanced actors, but they are easily avoided by advanced ones. To detect advanced actor¿s activities, an analyst must deal with behavioral indicators of compromise, which represent tactics, techniques and procedures that are not as common as the atomic and computed ones. In this paper, we analyze why these indicators are not widely used, and we identify key requirements for successful behavioral IOC detection, specification and sharing. We follow the intelligence cycle as the arranged sequence of steps for a defensive team to work, thereby providing a common reference for these teams to identify gaps in their capabilities.Villalón-Huerta, A.; Ripoll-Ripoll, I.; Marco-Gisbert, H. (2022). Key Requirements for the Detection and Sharing of Behavioral Indicators of Compromise. Electronics. 11(3):1-20. https://doi.org/10.3390/electronics1103041612011

Human decision-making in computer security incident response

Background: Cybersecurity has risen to international importance. Almost every organization will fall victim to a successful cyberattack. Yet, guidance for computer security incident response analysts is inadequate. Research Questions: What heuristics should an incident analyst use to construct general knowledge and analyse attacks? Can we construct formal tools to enable automated decision support for the analyst with such heuristics and knowledge? Method: We take an interdisciplinary approach. To answer the first question, we use the research tradition of philosophy of science, specifically the study of mechanisms. To answer the question on formal tools, we use the research tradition of program verification and logic, specifically Separation Logic. Results: We identify several heuristics from biological sciences that cybersecurity researchers have re-invented to varying degrees. We consolidate the new mechanisms literature to yield heuristics related to the fact that knowledge is of clusters of multi-field mechanism schema on four dimensions. General knowledge structures such as the intrusion kill chain provide context and provide hypotheses for filling in details. The philosophical analysis answers this research question, and also provides constraints on building the logic. Finally, we succeed in defining an incident analysis logic resembling Separation Logic and translating the kill chain into it as a proof of concept. Conclusion: These results benefits incident analysis, enabling it to expand from a tradecraft or art to also integrate science. Future research might realize our logic into automated decision-support. Additionally, we have opened the field of cybersecuity to collaboration with philosophers of science and logicians

A framework for financial botnet analysis

Financial botnets, those specifically aimed at carrying out financial fraud, represent a well-known threat for banking institutions all around the globe. Unfortunately, these malicious networks are responsible for huge economic losses or for conducting money laundering operations. Contrary to DDoS and spam malware, the stealthy nature of financial botnets requires new techniques and novel research in order to detect, analyze and even to take them down. This paper presents a work-in-progress research aimed at creating a system able to mitigate the financial botnet problem. The proposed system is based on a novel architecture that has been validated by one of the biggest savings banks in Spain. Based on previous experiences with two of the proposed architecture building blocks -the Dorothy framework and a blacklist-based IP reputation system-, we show that it is feasible to map financial botnet networks and to provide a non-deterministic score to its associated zombies. The proposed architecture also promotes intelligence information sharing with involved parties such as law enforcement authorities, ISPs and financial institutions. Our belief is that these functionalities will prove very useful to fight financial cybercrime

Cyber Threat Intelligence Exchange

The processing and exchange of Cyber Threat Intelligence (CTI) has become an increas- ingly important topic in recent years. This trend can be attributed to various factors. On the one hand, the exchange of information offers great potential to strengthen the knowledge base of companies and thus improve their protection against cyber threats. On the other hand, legislators in various countries have recognized this potential and translated it into legal reporting requirements. However, CTI is still a very young research area with only a small body of literature. Hence, there are hardly any guidelines, uniform standards, or specifications that define or support such an exchange. This dissertation addresses the problem by reviewing the methodological foundations for the exchange of threat intelligence in three focal areas. First, the underlying data formats and data structures are analyzed, and the basic methods and models are developed. In the further course of the work, possibilities for integrating humans into the analysis process of security incidents and into the generation of CTI are investigated. The final part of the work examines possible obstacles in the exchange of CTI. Both the legal environment and mechanisms to create incentives for an exchange are studied. This work thus creates a solid basis and a structured framework for the cooperative use of CTI

Mustererkennungsbasierte Verteidgung gegen gezielte Angriffe

The speed at which everything and everyone is being connected considerably outstrips the rate at which effective security mechanisms are introduced to protect them. This has created an opportunity for resourceful threat actors which have specialized in conducting low-volume persistent attacks through sophisticated techniques that are tailored to specific valuable targets. Consequently, traditional approaches are rendered ineffective against targeted attacks, creating an acute need for innovative defense mechanisms. This thesis aims at supporting the security practitioner in bridging this gap by introducing a holistic strategy against targeted attacks that addresses key challenges encountered during the phases of detection, analysis and response. The structure of this thesis is therefore aligned to these three phases, with each one of its central chapters taking on a particular problem and proposing a solution built on a strong foundation on pattern recognition and machine learning. In particular, we propose a detection approach that, in the absence of additional authentication mechanisms, allows to identify spear-phishing emails without relying on their content. Next, we introduce an analysis approach for malware triage based on the structural characterization of malicious code. Finally, we introduce MANTIS, an open-source platform for authoring, sharing and collecting threat intelligence, whose data model is based on an innovative unified representation for threat intelligence standards based on attributed graphs. As a whole, these ideas open new avenues for research on defense mechanisms and represent an attempt to counteract the imbalance between resourceful actors and society at large.In unserer heutigen Welt sind alle und alles miteinander vernetzt. Dies bietet mächtigen Angreifern die Möglichkeit, komplexe Verfahren zu entwickeln, die auf spezifische Ziele angepasst sind. Traditionelle Ansätze zur Bekämpfung solcher Angriffe werden damit ineffektiv, was die Entwicklung innovativer Methoden unabdingbar macht. Die vorliegende Dissertation verfolgt das Ziel, den Sicherheitsanalysten durch eine umfassende Strategie gegen gezielte Angriffe zu unterstützen. Diese Strategie beschäftigt sich mit den hauptsächlichen Herausforderungen in den drei Phasen der Erkennung und Analyse von sowie der Reaktion auf gezielte Angriffe. Der Aufbau dieser Arbeit orientiert sich daher an den genannten drei Phasen. In jedem Kapitel wird ein Problem aufgegriffen und eine entsprechende Lösung vorgeschlagen, die stark auf maschinellem Lernen und Mustererkennung basiert. Insbesondere schlagen wir einen Ansatz vor, der eine Identifizierung von Spear-Phishing-Emails ermöglicht, ohne ihren Inhalt zu betrachten. Anschliessend stellen wir einen Analyseansatz für Malware Triage vor, der auf der strukturierten Darstellung von Code basiert. Zum Schluss stellen wir MANTIS vor, eine Open-Source-Plattform für Authoring, Verteilung und Sammlung von Threat Intelligence, deren Datenmodell auf einer innovativen konsolidierten Graphen-Darstellung für Threat Intelligence Stardards basiert. Wir evaluieren unsere Ansätze in verschiedenen Experimenten, die ihren potentiellen Nutzen in echten Szenarien beweisen. Insgesamt bereiten diese Ideen neue Wege für die Forschung zu Abwehrmechanismen und erstreben, das Ungleichgewicht zwischen mächtigen Angreifern und der Gesellschaft zu minimieren

Cybersecurity Information Exchange with Privacy (CYBEX-P) and TAHOE – A Cyberthreat Language

Cybersecurity information sharing (CIS) is envisioned to protect organizations more effectively from advanced cyberattacks. However, a completely automated CIS platform is not widely adopted. The major challenges are: (1) the absence of advanced data analytics capabilities and (2) the absence of a robust cyberthreat language (CTL). This work introduces Cybersecurity Information Exchange with Privacy (CYBEX-P), as a CIS framework, to tackle these challenges. CYBEX-P allows organizations to share heterogeneous data from various sources. It correlates the data to automatically generate intuitive reports and defensive rules. To achieve such versatility, we have developed TAHOE - a graph-based CTL. TAHOE is a structure for storing, sharing, and analyzing threat data. It also intrinsically correlates the data. We have further developed a universal Threat Data Query Language (TDQL). In this work, we propose the system architecture for CYBEX-P. We then discuss its scalability along with a protocol to correlate attributes of threat data. We further introduce TAHOE & TDQL as better alternatives to existing CTLs and formulate ThreatRank - an algorithm to detect new malicious events.We have developed CYBEX-P as a complete CIS platform for not only data sharing but also for advanced threat data analysis. To that end, we have developed two frameworks that use CYBEX-P infrastructure as a service (IaaS). The first work is a phishing URL detector that uses machine learning to detect new phishing URLs. This real-time system adapts to the ever-changing landscape of phishing URLs and maintains an accuracy of 86%. The second work models attacker behavior in a botnet. It combines heterogeneous threat data and analyses them together to predict the behavior of an attacker in a host infected by a bot malware. We have achieved a prediction accuracy of 85-97% using our methodology. These two frameworks establish the feasibility of CYBEX-P for advanced threat data analysis for future researchers

NLP-Based Techniques for Cyber Threat Intelligence

In the digital era, threat actors employ sophisticated techniques for which, often, digital traces in the form of textual data are available. Cyber Threat Intelligence~(CTI) is related to all the solutions inherent to data collection, processing, and analysis useful to understand a threat actor's targets and attack behavior. Currently, CTI is assuming an always more crucial role in identifying and mitigating threats and enabling proactive defense strategies. In this context, NLP, an artificial intelligence branch, has emerged as a powerful tool for enhancing threat intelligence capabilities. This survey paper provides a comprehensive overview of NLP-based techniques applied in the context of threat intelligence. It begins by describing the foundational definitions and principles of CTI as a major tool for safeguarding digital assets. It then undertakes a thorough examination of NLP-based techniques for CTI data crawling from Web sources, CTI data analysis, Relation Extraction from cybersecurity data, CTI sharing and collaboration, and security threats of CTI. Finally, the challenges and limitations of NLP in threat intelligence are exhaustively examined, including data quality issues and ethical considerations. This survey draws a complete framework and serves as a valuable resource for security professionals and researchers seeking to understand the state-of-the-art NLP-based threat intelligence techniques and their potential impact on cybersecurity