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

Owl ontology quality assessment and optimization in the cybersecurity domain

The purpose of this dissertation is to assess the quality of ontologies in patterns perceived by cybersecurity context. A content analysis between ontologies indicated that there were more pronounced differences in OWL ontologies in the cybersecurity field. Results showed an increase of relevance from expressivity to variability. Additionally, no differences were found in strategies used in most of the incidents. The ontology background needs to be emphasized to understand the quality of the phenomena. In addition, ontologies are a means of representing an area of knowledge through their semantic structure. The search of information and integration of data from different origins provides a common base that guarantees the coherence of the data. This can be categorized and described in a normative way. The unification of information with the world that surrounds us allows to create synergies between entities and relationships. However, the area of cybersecurity is one of the real-world domains where knowledge is uncertain. It is therefore necessary to analyze the challenges of choosing the appropriate representation of un-structured information. Vulnerabilities are identified, but incident response is not an automatic mechanism for understanding and processing unstructured text found on the web.O objetivo desta dissertação foi avaliar a qualidade das ontologias, em padrões percebidos pelo contexto de cibersegurança. Uma análise de conteúdo entre ontologias indicou que havia diferenças mais pronunciadas por ontologias OWL no campo da cibersegurança. Os resultados mostram um aumento da relevância de expressividade para a variabilidade. Além disso, não foram encontradas diferenças em estratégias utilizadas na maioria dos incidentes. O conhecimento das ontologias precisa de ser enfatizado para se entender os fenómenos de qualidade. Além disso, as ontologias são um meio de representar uma área de conhecimento através da sua estrutura semântica e facilita a pesquisa de informações e a integração de dados de diferentes origens, pois fornecem uma base comum que garante a coerência dos dados, categorizados e descritos, de forma normativa. A unificação da informação com o mundo que nos rodeia permite criar sinergias entre entidades e relacionamentos. No entanto, a área de cibersegurança é um dos domínios do mundo real em que o conhecimento é incerto e é fundamental analisar os desafios de escolher a representação apropriada de informações não estruturadas. As vulnerabilidades são identificadas, mas a resposta a incidentes não é um mecanismo automático para se entender e processar textos não estruturados encontrados na web

The future of Cybersecurity in Italy: Strategic focus area

This volume has been created as a continuation of the previous one, with the aim of outlining a set of focus areas and actions that the Italian Nation research community considers essential. The book touches many aspects of cyber security, ranging from the definition of the infrastructure and controls needed to organize cyberdefence to the actions and technologies to be developed to be better protected, from the identification of the main technologies to be defended to the proposal of a set of horizontal actions for training, awareness raising, and risk management

Clustering and Topic Modelling: A New Approach for Analysis of National Cyber security Strategies

The consequences of cybersecurity attacks can be severe for nation states and their people. Recently many nations have revisited their national cybersecurity strategies (NCSs) to ensure that their cybersecurity capabilities is sufficient to protect their citizens and cyberspace. This study is an initial attempt to compare NCSs by using clustering and topic modelling methods to investigate the similarity and differences between them. We also aimed to identify underlying topics that are appeared in NCSs. We have collected and examined 60 NCSs that have been developed during 2003-2016. By relying on institutional theories, we found that memberships in the international intuitions could be a determinant factor for harmonization and integration between NCSs. By applying hierarchical clustering method, we noticed a stronger similarities between NCSs that are developed by the EU or NATO members. We also found that public-private partnerships, protection of critical infrastructure, and defending citizen and public IT systems are among those topics that have been received considerable attention in the majority of NCSs. We also argue that topic modeling method, LDA, can be used as an automated technique for analysis and understanding of textual documents by policy makers and governments during the development and reviewing of national strategies and policies

AiCEF: An AI-assisted Cyber Exercise Content Generation Framework Using Named Entity Recognition

Content generation that is both relevant and up to date with the current threats of the target audience is a critical element in the success of any Cyber Security Exercise (CSE). Through this work, we explore the results of applying machine learning techniques to unstructured information sources to generate structured CSE content. The corpus of our work is a large dataset of publicly available cyber security articles that have been used to predict future threats and to form the skeleton for new exercise scenarios. Machine learning techniques, like named entity recognition (NER) and topic extraction, have been utilised to structure the information based on a novel ontology we developed, named Cyber Exercise Scenario Ontology (CESO). Moreover, we used clustering with outliers to classify the generated extracted data into objects of our ontology. Graph comparison methodologies were used to match generated scenario fragments to known threat actors' tactics and help enrich the proposed scenario accordingly with the help of synthetic text generators. CESO has also been chosen as the prominent way to express both fragments and the final proposed scenario content by our AI-assisted Cyber Exercise Framework (AiCEF). Our methodology was put to test by providing a set of generated scenarios for evaluation to a group of experts to be used as part of a real-world awareness tabletop exercise

Designing a framework for data populating alarms based on mitre techniques

In this paper we aim to develop a proof of concept framework as a step-by-step process for identifying what type of information and log types a SOC analyst needs to analyze and handle an alarm based on the alarms MITRE technique. To solve this, it was decided that using both theoretical and experimental research methodologies could be advantageous. Hence we first used a Systematic Literature Review to search, screen, and select relevant literature. Followed by the usage of Design Science Research method for conducting the research based upon a theoretical basis, and an experimental process. To develop a framework consisting of an easy to understand and independent step-by-step process. The proof of concept framework introduced in this paper, is an eight step process describing how one may proceed when gathering data needed for automating information gathering based on alarms MITRE techniques. In these eight steps it revolves around three main concepts, which are gathering a theoretical foundation by research and discussion, improving the theoretical foundation by testing and adjusting, and ends with a continuous process of maintaining the constructed automations when used in a production setting. This framework produced accurate results when tested during research, and we believe it should be further explored and tested in a larger scale. Also it should be considered a stepping stone into further automating the whole alarm handling process, from gathering data to response

Designing a framework for data populating alarms based on MITRE techniques

In this paper we aim to develop a proof of concept framework as a step-by-step process for identifying what type of information and log types a SOC analyst needs to analyze and handle an alarm based on the alarms MITRE technique. To solve this, it was decided that using both theoretical and experimental research methodologies could be advantageous. Hence we first used a Systematic Literature Review to search, screen, and select relevant literature. Followed by the usage of Design Science Research method for conducting the research based upon a theoretical basis, and an experimental process. To develop a framework consisting of an easy to understand and independent step-by-step process. The proof of concept framework introduced in this paper, is an eight step process describing how one may proceed when gathering data needed for automating information gathering based on alarms MITRE techniques. In these eight steps it revolves around three main concepts, which are gathering a theoretical foundation by research and discussion, improving the theoretical foundation by testing and adjusting, and ends with a continuous process of maintaining the constructed automations when used in a production setting. This framework produced accurate results when tested during research, and we believe it should be further explored and tested in a larger scale. Also it should be considered a stepping stone into further automating the whole alarm handling process, from gathering data to response

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

Building Confidence in the Cybersphere: A Path to Multilateral Progress

As use of the Internet has become critical to global economic development and international security, there is near-unanimous agreement on the need for more international cooperation to increase stability and security in cyberspace. Several multilateral initiatives over the last five years have begun to spell out cooperative measures, norms of behavior, and transparency and confidence-building measures (TCBMs) that could help improve mutual cybersecurity. These efforts have been painstakingly slow, and some have stalled due to competing interests. Nonetheless, a United Nations (UN) Group of Governmental Experts (GGE) and the Organization for Cooperation and Security in Europe (OSCE) have achieved some high-level agreement on principles, norms, and “rules of the road” for national Internet activities and transnational cyber interactions. Their agreements include commitments to share more information, improve national protective capacities, cooperate on incident response, and restrain certain destabilizing state practices. Voluntary international agreements are worth little, unless states implement their commitments. So far, implementation has been crippled by vague language, national security considerations, complex relations between public and private actors in cyberspace, and privacy concerns. This is particularly true regarding the upfront sharing of information on threats and the willingness of participants to cooperate on incident investigations, including identifying perpetrators. With multilateral forums struggling to find a way forward with norm-setting and implementation, alternate pathways are needed to protect and build on what has been accomplished so far. Different strategies can help advance implementation of measures in the UN and OSCE agreements. Some commitments, such as establishing and sharing information about national points of contact, are best handled unilaterally or through bilateral or regional inter-governmental cooperation. Other objectives, such as protecting the core architecture and functions of the Internet that support trans-border critical infrastructure and underpin the global financial system, require a multi-stakeholder approach that includes not only governments but also private sector service providers, academic experts, and nongovernmental organizations. This paper compares what the GGE and OSCE norm-building processes have achieved so far and what disagreements have impeded these efforts. It identifies several priorities for cooperation identified by participants in both forums. It also proposes three practical projects related to these priorities that members of regional or global organizations might be able to work on together despite political tensions and philosophical disputes. The first would help state and non-state actors share information and communicate about various types of cybersecurity threats using a flexible and intuitive effects-based taxonomy to categorize cyber activity. The second would develop a more sophisticated way for state and non-state actors to assess the risks of different types of cyber incidents and the potential benefits of cooperation. The third would identify aspects of the Internet that might be considered the core of a public utility, worthy of special protection in their own right and for their support of trans-border critical infrastructure