Cyber Threat Intelligence Research Articles

Cyber threat intelligence: existing benefits and challenges for law firms and businesses

Cyber threat intelligence: existing benefits and challenges for law firms and businesses

The Evolving Threat Landscape: How Cyber Threat Intelligence Empowers Proactive Defenses against WannaCry Ransomware

Cyber threat intelligence (CTI) is a rapidly growing field that plays an essential role in ensuring the security of online systems. CTI refers to the intelligence that is gathered, analyzed, and disseminated to help organizations understand and respond to cyber threats. This information can be used to identify vulnerabilities, detect potential attacks, and develop strategies to mitigate risks. The field of CTI is constantly evolving, as cyber threats become more sophisticated and complex. Legacy security measures like firewalls and anti-virus software are no longer enough to protect organizations from the many threats they face. CTI provides a proactive approach to cybersecurity, by enabling organizations to anticipate and prepare for threats before they occur. CTI relies on the collection and analysis of data from multiple sources, such as open-source intelligence (OSINT), dark web forums, social media, and other threat intelligence streams. The data is analyzed using a wide range of tools and techniques, including machine learning and artificial intelligence, to identify patterns and trends that may indicate a potential threat. One of the key benefits of CTI is its ability to help organizations understand the tactics, techniques, and procedures of attackers. By analyzing the behaviors, strategies, tactics, and actions of threat actors, organizations can develop a more comprehensive understanding of the threats they face and can better prepare for potential attacks.

Implications of GDPR and NIS2 for Cyber Threat Intelligence Exchange in Hospitals

The DYNAMO Horizon Europe Project aims to support critical sector (healthcare, energy production, marine transport) stakeholders in enhancing resilience and minimizing the effects of cyber-attacks. DYNAMO's objective is to use artificial intelligence to integrate cyber threat intelligence (CTI) and business continuity management (BCM) to support decision-making. The goal is joint preparation for EU cyber threats, necessitating timely global situational awareness and effective communication to address threats before they escalate. This paper focuses on the intelligence sharing and trust needs of the DYNAMO use cases while also meeting regulatory requirements. Analyzing DYNAMO’s internal materials and aligning them with authorities' requirements, particularly NIS2 and GDPR, reveals that healthcare organizations need to prepare for more effective data protection, incident response, and cyber-attack mitigation. While NIS2 doesn't specify technical requirements for healthcare, it offers a broader framework for organizations to make informed decisions about equipment suppliers and security applications. After the general review, this study examines a specific healthcare use case: a hospital infected by phishing, emphasizing that CTI exchanges may contain sensitive data falling under GDPR and NIS2 regulations. This includes technical details, health-related information, patient data, insurance details, and employee information. Concerning the AI-based approaches used, DYNAMO must handle this CTI exchange in compliance with the law. The case study compares the DYNAMO project's CTI exchange use case with GDPR and NIS2 requirements, highlighting challenges such as the difficulty in separating sensitive data under GDPR and differences in language and terms between the two regulations. Despite these challenges, the study discusses the impact of GDPR and NIS2 on CTI exchange in the healthcare sector, providing key implementation points and guidelines.

KnowCTI: Knowledge-based cyber threat intelligence entity and relation extraction

Structured cyber threat intelligence enables security researchers to know the occurrence of cyber threats in time, thereby improving the efficiency of security defense and analysis. Previous works usually use general deep learning and NLP techniques to extract intelligence. Such methods suffer from insufficient semantic understanding in the field of security. To address these issues, we propose a novel method called Knowledge-based Cyber Threat Intelligence Entity and Relation Extraction (KnowCTI), which incorporates cybersecurity knowledge into the model to enhance the understanding of the realm of cybersecurity and has a full picture of threats with the threat intelligence graph generation. Specifically, we first build a cybersecurity knowledge base and train cybersecurity-aware knowledge embeddings based on the base. Secondly, we refine the most related knowledge triples by attention mechanism and gate mechanism, and then construct a sentence tree through these triples. Next, we employ graph attention networks to incorporate knowledge information into the sentence by considering the sentence tree as a graph. Finally, we consider entity extraction as a sequence labeling problem and relation extraction as a classification problem to decode the entities and relation triples according to the threat intelligence ontology we designed. Experimental results demonstrate the superior performance with the F1 score exceeding 90.16 and 81.83 on entity and relation extraction separately.

The Role of Threat Intelligence in Enhancing Cybersecurity Posture

Organizations confront more complex and persistent cyberattacks in the quickly changing cyber threat landscape of today. Through the provision of actionable insights on emerging threats, adversary tactics, and vulnerabilities, cyber threat intelligence (CTI) becomes an increasingly important component in strengthening the cybersecurity posture of organizational networks. The importance of CTI and its useful uses in enhancing cybersecurity defences are examined in this research study. It explores the several kinds of threat intelligence, methods for compiling and evaluating threat information, and proactive steps that businesses may take to make the most of CTI. This study emphasizes the significance of incorporating CTI into cybersecurity plans to more effectively identify, prevent, and respond to cyber attacks through a thorough review of industry practices and case studies. Organizations may improve their defences against cyberattacks and enhance vital assets and data by comprehending the role that CTI plays in cybersecurity.

Implementation of OSINT for Improving an International Finance Sector Organization’s Cybersecurity

This work-in-progress paper addresses the need to improve intelligence processes and enhance an organization's response to cyber threats while managing associated risks and improving Business Continuity Management (BCM). The paper focuses on the role of Open-Source Intelligence (OSINT) in Cyber Threat Intelligence (CTI) gathering and presents an operational process for its implementation. The process includes defining goals, selecting open sources, data collection, filtering, analysis, and reporting. Testing in an international financial sector organization yielded positive results, demonstrating the process's value in threat intelligence. Future research should clarify the role of artificial intelligence in OSINT

Utilization and Sharing of Cyber Threat Intelligence Produced by Open-Source Intelligence

Open-source intelligence (OSINT) is crucial for enhancing organizational cybersecurity by proactively identifying and mitigating potential threats using publicly available information. This study, part of the DYNAMO project, explores the production of cyber threat information (CTI) through OSINT, its application in safeguarding against cyber threats, and the necessary elements for secure information exchange between organizations. The authors employed an integrative literature review of various sources, including industry literature, articles, blog posts, studies, and organizational websites, which were then systematically analyzed using content analysis. The research focuses on OSINT tools and techniques emphasizing the need for expertise in discerning relevant data and respecting privacy rights. Human judgment is highlighted as crucial in ethical decision-making despite the significant role of technology in data collection. Platforms like the Malware Information Sharing Platform (MISP) facilitate the sharing of threat information, promoting prevention and identification of cyber-attacks. Ethical considerations, adherence to data protection legislation, and compliance with directives like the revision of the Network and Information Security Directive (NIS2) and artificial intelligence regulations are paramount. In conclusion, OSINT is a valuable tool for cybersecurity, requiring expertise, transparent processes, and a balanced integration of technology and human skills. The ethical dimensions of OSINT and the role of artificial intelligence merit separate in-depth studies.

Enhancing Cyber Supply Chain Security through Prophetic Analytics for Cyber Threats

The Cyber Provide series system a multifaceted system with a multiple subsystems carrying out distinct functions. The potential weaknesses and risks originating out of a segment and arrangement that will manipulated at some stage in the provide series. make supply chain security difficult As a result, it is critical for organizations to comprehend and assess the risks in order to implement the appropriate supply chain security management measures. Using machine learning (ML) techniques in conjunction with cyber threat intelligence (CTI), we have been able To examine and predict potential risks using the characteristics of CTI. This makes it possible to pinpoint the innate CSC vulnerabilities and implement the proper countermeasures for an overall increase in cyber security. In order to show the practicality of our method, we collect CTI data and use the Microsoft viruses forecast dataset to produce prophetic investigation using several machine learning (ML) algorithms, The test considers the pounce and TTP as capture variable, while the measures of compromise and vulnerabilities are considered as output parameters, incorporating Decision Tree, Random Forest, Support Vector Machine. KEY WORDS Cyber Supply Chain; Cyber warning surveillance; Predictive Analytics, Cyber Safety, Tactical Methods Process

Automated discovery and mapping ATT&CK tactics and techniques for unstructured cyber threat intelligence

As cyber attacks are growing, Cyber Threat Intelligence (CTI) enhances the ability of security systems to resist novel cyber threats. However, since most CTI is unstructured data written in natural language, it needs to be understood and summarized by security experts to be effectively utilized. To address the problem, we adopt the ATT&CK matrix as the taxonomy to propose a method for automated mapping of unstructured threat intelligence to tactics and techniques. The proposed method contains a pre-processor for text denoising, a label extractor for classifying which tactics and techniques category the text belongs to, and a post-processor for correcting the classification results. The label extractor consists of two multi-label classifiers based on DistilBERT for tactics and techniques classification respectively. The post-processor corrects the classification results based on the relations between tactics, techniques, and sub-techniques in the matrix, eliminating errors caused by the independence between categories. In the evaluation, we collect the text data from the ATT&CK knowledge base and real cyber threat reports to build an experiment dataset, which contains 26,602 sentence samples. We apply the proposed method to the dataset to verify its effectiveness. The results show that the proposed method can accurately retrieve tactics and techniques with F0.5 score of 85.50% and 75.17% respectively, which outperforms the baseline method by about 10%.

Mitigating ransomware attacks through cyber threat intelligence and machine learning

<div>In the face of escalating cyber threats, particularly the rampant and sophisticated nature of ransomware attacks, organizations are compelled to adopt a proactive and multi-faceted strategy for mitigation. The fusion of machine learning (ML) algorithms enables the system to dynamically adapt and evolve in response to evolving attack vectors and tactics employed by cybercriminals. This paper presents a comprehensive approach that synergistically integrates ML and cyber threat intelligence (CTI) to fortify defenses against ransomware assaults. The proposed methodology incorporates three distinct machine learning techniques, namely random forest (RF), extreme gradient boosting (XGBoost), and adaptive boosting (AdaBoost). Empirical evidence derived from the study affirms the efficacy of this approach in effectively discriminating between malicious and ransom software, achieving a notable identification rate of 98.55%. The incorporation of CTI enhances the strategic posture by providing actionable insights into the threat landscape. The proposed focuses on identifying and neutralizing ransomware, aligning with contemporary cybersecurity imperatives, offering a proactive defense against ransomware attacks, ultimately safeguarding critical assets, and preserving the integrity of digital ecosystems.</div>

Creating Proactive Cyber Threat Intelligence with Hacker Exploit Labels: A Deep Transfer Learning Approach

The rapid proliferation of complex information systems has been met by an ever-increasing quantity of exploits that can cause irreparable cyber breaches. To mitigate these cyber threats, academia and industry have placed a significant focus on proactively identifying and labeling exploits developed by the international hacker community. However, prevailing approaches for labeling exploits in hacker forums do not leverage metadata from exploit darknet markets or public exploit repositories to enhance labeling performance. In this study, we adopted the computational design science paradigm to develop a novel information technology artifact, the deep transfer learning exploit labeler (DTL-EL). DTL-EL incorporates a pre-initialization design, multi-layer deep transfer learning (DTL), and a self-attention mechanism to automatically label exploits in hacker forums. We rigorously evaluated the proposed DTL-EL against state-of-the-art non-DTL benchmark methods based in classical machine learning and deep learning. Results suggest that the proposed DTL-EL significantly outperforms benchmark methods based on accuracy, precision, recall, and F1-score. Our proposed DTL-EL framework provides important practical implications for key stakeholders such as cybersecurity managers, analysts, and educators.

A Framework for the Development of Sharing and Collaboration of Cyber Threat Intelligence for Colleges in Camarines Norte

A Framework for the Development of Sharing and Collaboration of Cyber Threat Intelligence for Colleges in Camarines Norte

Cyber Threat Intelligence on Blockchain: A Systematic Literature Review

Cyber Threat Intelligence (CTI) has become increasingly important in safeguarding organizations against cyber threats. However, managing, storing, analyzing, and sharing vast and sensitive threat intelligence data is a challenge. Blockchain technology, with its robust and tamper-resistant properties, offers a promising solution to address these challenges. This systematic literature review explores the recent advancements and emerging trends at the intersection of CTI and blockchain technology. We reviewed research papers published during the last 5 years to investigate the various proposals, methodologies, models, and implementations related to the distributed ledger technology and how this technology can be used to collect, store, analyze, and share CTI in a secured and controlled manner, as well as how this combination can further support additional dimensions such as quality assurance, reputation, and trust. Our findings highlight the focus of the CTI and blockchain convergence on the dissemination phase in the CTI lifecycle, reflecting a substantial emphasis on optimizing the efficacy of communication and sharing mechanisms, based on an equitable emphasis on both permissioned, private blockchains and permissionless, public blockchains, addressing the diverse requirements and preferences within the CTI community. The analysis reveals a focus towards the tactical and technical dimensions of CTI, compared to the operational and strategic CTI levels, indicating an emphasis on more technical-oriented utilization within the domain of blockchain technology. The technological landscape supporting CTI and blockchain integration emerges as multifaceted, featuring pivotal roles played by smart contracts, machine learning, federated learning, consensus algorithms, IPFS, deep learning, and encryption. This integration of diverse technologies contributes to the robustness and adaptability of the proposed frameworks. Moreover, our exploration unveils the overarching significance of trust and privacy as predominant themes, underscoring their pivotal roles in shaping the landscape within our research realm. Additionally, our study addresses the maturity assessment of these integrated systems. The approach taken in evaluating maturity levels, distributed across the Technology Readiness Level (TRL) scale, reveals an average balance, indicating that research efforts span from early to mid-stages of maturity in implementation. This study signifies the ongoing evolution and maturation of research endeavors within the dynamic intersection of CTI and blockchain technology, identifies trends, and also highlights research gaps that can potentially be addressed by future research on the field.

Instrumenting OpenCTI with a Capability for Attack Attribution Support

In addition to identifying and prosecuting cyber attackers, attack attribution activities can provide valuable information for guiding defenders’ security procedures and supporting incident response and remediation. However, the technical analysis involved in cyberattack attribution requires skills, experience, access to up-to-date Cyber Threat Intelligence, and significant investigator effort. Attribution results are not always reliable, and skillful attackers often work hard to hide or remove the traces of their operations and to mislead or confuse investigators. In this article, we translate the technical attack attribution problem to the supervised machine learning domain and present a tool designed to support technical attack attribution, implemented as a machine learning model extending the OpenCTI platform. We also discuss the tool’s performance in the investigation of recent cyberattacks, which shows its potential in increasing the effectiveness and efficiency of attribution operations.

Machine Learning Applications in Cyber Threat Intelligence: A Comprehensive Review

Contemporary institutions are consistently confronted with fraudulent activities that exploit weaknesses in interconnected systems. Securing critical data against unauthorized access by hackers and other cybercriminals requires the application of robust cybersecurity protocols. As the number and complexity of cyber threats continue to grow, innovative prevention strategies are required. The objective of this study is to investigate the correlation between machine learning (ML) and cyber threat intelligence (CTI) to improve cybersecurity strategies. For the detection of anomalies, the analysis of malware, and the prediction of threats, ML techniques are indispensable in industries including retail, finance, healthcare, and cybersecurity. By employing critical threat information (CTI), security teams can gain a comprehensive understanding of adversary strategies and bolster defensive measures; thus, they play a pivotal role in proactive defense. Integration of ML and CTI facilitates exhaustive analysis by automating the acquisition, processing, and categorization of data. However, obstacles arise when confronted with issues such as risk assessment, the requirement for precise data, and the initial stages of machine learning implementation in business intelligence. In this paper, we present an extensive examination of the current literature concerning the visualization of Cyber Threat Intelligence (CTI) and the utilization of Machine Learning (ML. Therefore, the report concludes with an analysis of emergent threats, potential future applications of AI and ML in the field of cyber threat intelligence, and the critical contribution of machine learning to the improvement of cybersecurity.

Trust Environment for Cyber-Physical Systems: The DYNAMO Approach

This paper explores the evolving landscape of cybersecurity within cyber-physical systems, emphasizing the critical need for resilience and trust-building mechanisms. Grounded in the foundational perspectives of Cybersecurity Science, the study addresses the challenges posed by the complex interdependencies among physical, information, cognitive, and social domains. It introduces the DYNAMO platform, a novel approach that integrates Cyber Threat Intelligence (CTI) with Business Continuity Management (BCM) to enhance situational awareness and recovery planning. The platform's comprehensive methodology spans existing tool incorporation, detailed assessments, identification of gaps, simulations, and knowledge generation. Furthermore, the paper delves into the intricacies of cybersecurity information sharing, advocating for a unified approach to combat hybrid threats. The establishment of a common operational picture and shared situational awareness emerges as pivotal, underlining the indispensable role of technology, social trust, and resilience in securing critical information-sharing mechanisms in cyber-physical systems.

Improving Threat Mitigation Through a Cybersecurity Risk Management Framework: A Computational Design Science Approach

ABSTRACT Cyberattacks have been increasing in volume and intensity, necessitating proactive measures. Cybersecurity risk management frameworks are deployed to provide actionable intelligence to mitigate potential threats by analyzing the available cybersecurity data. Existing frameworks, such as MITRE ATT&CK, provide timely mitigation strategies against attacker capabilities yet do not account for hacker data when developing cyber threat intelligence. Therefore, we developed a novel information technology artifact, ATT&CK-Link, which incorporates a novel transformer and multi-teacher knowledge distillation design, to link hacker threats to this broadly used framework. Here, we illustrated how hospital systems can use this framework to proactively protect their cyberinfrastructure against hacker threats. Our ATT&CK-Link framework has practical implications for cybersecurity professionals, who can implement our framework to generate strategic, operational, and tactical cyber threat intelligence. ATT&CK-Link also contributes to the information systems knowledge base by providing design principles to pursue targeted cybersecurity analytics, risk management, and broader text analytics research through simultaneous multi-modal (e.g., text and code) distillation and classification.

Clustering APT Groups Through Cyber Threat Intelligence by Weighted Similarity Measurement

Clustering APT Groups Through Cyber Threat Intelligence by Weighted Similarity Measurement

SmartSentry: Cyber Threat Intelligence in Industrial IoT

SmartSentry: Cyber Threat Intelligence in Industrial IoT

Cyber Defense Using Cyber Threat Intelligence to Anticipate and Avoid Future Cyber Attacks

Cyber Defense Using Cyber Threat Intelligence to Anticipate and Avoid Future Cyber Attacks